From f4b99551d9bfddcd8a6ff5a8890cfdf5fa7a3457 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Wed, 29 Nov 2023 18:13:43 +0100 Subject: [PATCH 01/48] Simplified starter --- .gitignore | 2 + requirements.txt | 4 +- ...f open_source_license %}LICENSE{% endif %} | 2 +- template/.dockerignore | 2 + template/README.md | 53 ++ template/configs/feature_engineering.yaml | 0 template/configs/inference_config.yaml | 0 template/configs/training_config.yaml | 0 template/license | 110 +++ template/license_header | 2 + template/pipelines/__init__.py | 5 + template/pipelines/feature_engineering.py | 53 ++ template/pipelines/inference.py | 53 ++ template/pipelines/training.py | 53 ++ template/requirements.txt | 3 + template/run.ipynb | 773 ++++++++++++++++++ template/run.py | 299 +++++++ template/steps/__init__.py | 17 + template/steps/data_loader.py | 55 ++ template/steps/data_preprocessor.py | 67 ++ template/steps/data_splitter.py | 47 ++ template/steps/model_evaluator.py | 89 ++ template/steps/model_trainer.py | 74 ++ template/utils/__init__.py | 1 + template/utils/preprocess.py | 41 + ...f open_source_license %}LICENSE{% endif %} | 1 + template/{{ _copier_conf.answers_file }} | 2 + tests/test_starter_template.py | 229 +++--- 28 files changed, 1938 insertions(+), 99 deletions(-) create mode 100644 template/.dockerignore create mode 100644 template/README.md create mode 100644 template/configs/feature_engineering.yaml create mode 100644 template/configs/inference_config.yaml create mode 100644 template/configs/training_config.yaml create mode 100644 template/license create mode 100644 template/license_header create mode 100644 template/pipelines/__init__.py create mode 100644 template/pipelines/feature_engineering.py create mode 100644 template/pipelines/inference.py create mode 100644 template/pipelines/training.py create mode 100644 template/requirements.txt create mode 100644 template/run.ipynb create mode 100644 template/run.py create mode 100644 template/steps/__init__.py create mode 100644 template/steps/data_loader.py create mode 100644 template/steps/data_preprocessor.py create mode 100644 template/steps/data_splitter.py create mode 100644 template/steps/model_evaluator.py create mode 100644 template/steps/model_trainer.py create mode 100644 template/utils/__init__.py create mode 100644 template/utils/preprocess.py create mode 100644 template/{% if open_source_license %}LICENSE{% endif %} create mode 100644 template/{{ _copier_conf.answers_file }} diff --git a/.gitignore b/.gitignore index b6e4761..882db32 100644 --- a/.gitignore +++ b/.gitignore @@ -127,3 +127,5 @@ dmypy.json # Pyre type checker .pyre/ + +*.zen \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index eede3b1..a64987f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,5 @@ -scikit-learn +scikit-learn<1.3 copier jinja2-time +zenml[server]>=0.50.0 +notebook \ No newline at end of file diff --git a/starter/template/{% if open_source_license %}LICENSE{% endif %} b/starter/template/{% if open_source_license %}LICENSE{% endif %} index 4179465..3de332b 100644 --- a/starter/template/{% if open_source_license %}LICENSE{% endif %} +++ b/starter/template/{% if open_source_license %}LICENSE{% endif %} @@ -1 +1 @@ -{% include 'templates/license' %} \ No newline at end of file +{% include 'template/license' %} \ No newline at end of file diff --git a/template/.dockerignore b/template/.dockerignore new file mode 100644 index 0000000..455f4d7 --- /dev/null +++ b/template/.dockerignore @@ -0,0 +1,2 @@ +.venv* +.requirements* \ No newline at end of file diff --git a/template/README.md b/template/README.md new file mode 100644 index 0000000..aa76203 --- /dev/null +++ b/template/README.md @@ -0,0 +1,53 @@ +# 📜 ZenML Starter Project Template + +What would you need to get a quick understanding of the ZenML framework and +start building your own ML pipelines? The answer is a simple project template +to cover the basics of ZenML: a collection of steps and pipelines, a stack +configuration and, to top it all off, a simple but useful CLI. This is exactly +what the ZenML starter template is all about. + +This project template is a good starting point for anyone starting out with +ZenML. It showcases the following fundamental ZenML concepts in a relatable +ML context: + +* designing [ZenML pipeline steps](https://docs.zenml.io/user-guide/starter-guide#pipelines-and-steps) +in general, but also particularly useful for the following applications: + * data ingestion, data transformation and data train/test splitting + * model training and evaluation +* using [step parameterization and caching](https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions) +to design flexible and reusable steps +* using [custom data types for your artifacts and writing materializers for them](https://docs.zenml.io/user-guide/advanced-guide/artifact-management/handle-custom-data-types) +* constructing and running a [ZenML pipeline](https://docs.zenml.io/user-guide/starter-guide#pipelines-and-steps) +* accessing ZenML pipeline run artifacts in [the post-execution phase](https://docs.zenml.io/user-guide/starter-guide/fetch-runs-after-execution), +after a pipeline run has concluded +* best practices for implementing and running reproducible and reliable ML +pipelines with ZenML + +In addition to that, the entire project is implemented with the [scikit-learn](https://scikit-learn.org) +library and showcases how to use ZenML with a popular ML framework. It makes +heavy use of the tabular datasets and classification models that scikit-learn +provides, but the concepts and patterns it showcases are applicable to any +other ML framework. + +## 📃 Template Parameters + +| Parameter | Description | Default | +|-----------|-------------|---------| +| Name | The name of the person/entity holding the copyright | ZenML GmbH | +| Email | The email of the person/entity holding the copyright | info@zenml.io | +| Project Name | Short name for your project | ZenML Starter | +| Project Slug | A slugified version of the project name (automatically generated from the project name) | zenml_starter | +| Project Version | The version of your project | 0.1.0 | +| Project License | The license under which your project will be released (one of `Apache Software License 2.0`, `MIT license`, `BSD license`, `ISC license`, `GNU General Public License v3` and `Not open source`) | Apache Software License 2.0 | +| Auto-Format | Whether to automatically format and cleanup the generated code with [black](https://black.readthedocs.io/), [ruff](https://beta.ruff.rs/docs/) and [autoflake](https://github.com/PyCQA/autoflake) (yes/no). You also need to have these Python packages installed for this option to take effect. | no | +| Use ZenML Step Params | Whether to showcase using parameters for the ZenML steps in the project (yes/no). If selected, all generated ZenML pipeline steps will be parameterized. | yes | +| Use ZenML Materializers | Whether to showcase using custom data types for the ZenML artifacts in the project (yes/no). If selected, the generated code will demonstrate the use of custom artifact data types and materializers in the generated steps and pipelines. | yes | +| UCI Dataset | The name of the UCI provided scikit-learn dataset to use in the project (one of `Iris`, `Breast Cancer` and `Wine`) | Wine | +| Scikit-learn Model | The name of the scikit-learn classifier model to use in the project (one of `Logistic Regression`, `SVC`, `Linear SVC`, `Random Forest`, `KNN`, `Gaussian NB`, `Perceptron`, `SGD Classifier` and `Decision Tree`) | Logistic Regression | +| Runtime Configurable Dataset | Whether to make the dataset a configurable parameter of the data loader step and CLI (yes/no). Only has effect if the step parameters were also selected. | yes | +| Runtime Configurable Model | Whether to make the model a configurable parameter of the model trainer step and CLI (yes/no). Only has effect if the step parameters were also selected. | yes | + +## 🚀 Generate a ZenML Project + +Please see [the main README page](../README.md) for instructions on how to +generate a ZenML project from this template. diff --git a/template/configs/feature_engineering.yaml b/template/configs/feature_engineering.yaml new file mode 100644 index 0000000..e69de29 diff --git a/template/configs/inference_config.yaml b/template/configs/inference_config.yaml new file mode 100644 index 0000000..e69de29 diff --git a/template/configs/training_config.yaml b/template/configs/training_config.yaml new file mode 100644 index 0000000..e69de29 diff --git a/template/license b/template/license new file mode 100644 index 0000000..3697641 --- /dev/null +++ b/template/license @@ -0,0 +1,110 @@ +{% if open_source_license == 'mit' -%} +MIT License + +Copyright (c) {{ full_name }} {% now 'local', '%Y' %} + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +{% elif open_source_license == 'bsd' %} + +BSD License + +Copyright (c) {{ full_name }} {% now 'local', '%Y' %}. All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. +{% elif open_source_license == 'isc' -%} +ISC License + +Copyright (c) {{ full_name }} {% now 'local', '%Y' %} + +Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies. + +THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. +{% elif open_source_license == 'apache' -%} +Apache Software License 2.0 + +Copyright (c) {{ full_name }} {% now 'local', '%Y' %}. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +{% elif open_source_license == 'gpl3' -%} +GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + {{ project_short_description }} + Copyright (C) {{ full_name }} {% now 'local', '%Y' %} + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. +{% endif %} \ No newline at end of file diff --git a/template/license_header b/template/license_header new file mode 100644 index 0000000..cc653b6 --- /dev/null +++ b/template/license_header @@ -0,0 +1,2 @@ +{%- macro license() %}{% include 'template/license' %}{% endmacro -%} +{{ license() | replace('\n', '\n# ') }} \ No newline at end of file diff --git a/template/pipelines/__init__.py b/template/pipelines/__init__.py new file mode 100644 index 0000000..b91d3ee --- /dev/null +++ b/template/pipelines/__init__.py @@ -0,0 +1,5 @@ +# {% include 'template/license_header' %} + +from feature_engineering import _feature_engineering +from training import _model_training +from inference import _inference \ No newline at end of file diff --git a/template/pipelines/feature_engineering.py b/template/pipelines/feature_engineering.py new file mode 100644 index 0000000..5043aa8 --- /dev/null +++ b/template/pipelines/feature_engineering.py @@ -0,0 +1,53 @@ +# {% include 'template/license_header' %} + +import random +from typing import Optional, List + +from zenml import pipeline +from zenml.logger import get_logger + +from steps import ( + data_loader, + data_splitter, + data_preprocessor, +) + + +logger = get_logger(__name__) + + +@pipeline +def _feature_engineering( + test_size: float = 0.2, + drop_na: Optional[bool] = None, + normalize: Optional[bool] = None, + drop_columns: Optional[List[str]] = None, +): + """ + Model training pipeline. + + This is a pipeline that loads the data, processes it and splits + it into train and test sets, then search for best hyperparameters, + trains and evaluates a model. + + Args: + test_size: Size of holdout set for training 0.0..1.0 + drop_na: If `True` NA values will be removed from dataset + normalize: If `True` dataset will be normalized with MinMaxScaler + drop_columns: List of columns to drop from dataset + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # Link all the steps together by calling them and passing the output + # of one step as the input of the next step. + raw_data, target, _ = data_loader(random_state=random.randint(0,100)) + dataset_trn, dataset_tst = data_splitter( + dataset=raw_data, + test_size=test_size, + ) + dataset_trn, dataset_tst, _ = data_preprocessor( + dataset_trn=dataset_trn, + dataset_tst=dataset_tst, + drop_na=drop_na, + normalize=normalize, + drop_columns=drop_columns, + ) diff --git a/template/pipelines/inference.py b/template/pipelines/inference.py new file mode 100644 index 0000000..428b878 --- /dev/null +++ b/template/pipelines/inference.py @@ -0,0 +1,53 @@ +# {% include 'template/license_header' %} + +import random +from typing import Optional, List + +from zenml import pipeline +from zenml.logger import get_logger + +from steps import ( + data_loader, + data_splitter, + data_preprocessor, +) + + +logger = get_logger(__name__) + + +@pipeline +def _inference( + test_size: float = 0.2, + drop_na: Optional[bool] = None, + normalize: Optional[bool] = None, + drop_columns: Optional[List[str]] = None, +): + """ + Model training pipeline. + + This is a pipeline that loads the data, processes it and splits + it into train and test sets, then search for best hyperparameters, + trains and evaluates a model. + + Args: + test_size: Size of holdout set for training 0.0..1.0 + drop_na: If `True` NA values will be removed from dataset + normalize: If `True` dataset will be normalized with MinMaxScaler + drop_columns: List of columns to drop from dataset + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # Link all the steps together by calling them and passing the output + # of one step as the input of the next step. + raw_data, target, _ = data_loader(random_state=random.randint(0,100)) + dataset_trn, dataset_tst = data_splitter( + dataset=raw_data, + test_size=test_size, + ) + dataset_trn, dataset_tst, _ = data_preprocessor( + dataset_trn=dataset_trn, + dataset_tst=dataset_tst, + drop_na=drop_na, + normalize=normalize, + drop_columns=drop_columns, + ) diff --git a/template/pipelines/training.py b/template/pipelines/training.py new file mode 100644 index 0000000..4e94d66 --- /dev/null +++ b/template/pipelines/training.py @@ -0,0 +1,53 @@ +# {% include 'template/license_header' %} + +import random +from typing import Optional, List + +from zenml import pipeline +from zenml.logger import get_logger + +from steps import ( + data_loader, + data_splitter, + data_preprocessor, +) + + +logger = get_logger(__name__) + + +@pipeline +def _training( + test_size: float = 0.2, + drop_na: Optional[bool] = None, + normalize: Optional[bool] = None, + drop_columns: Optional[List[str]] = None, +): + """ + Model training pipeline. + + This is a pipeline that loads the data, processes it and splits + it into train and test sets, then search for best hyperparameters, + trains and evaluates a model. + + Args: + test_size: Size of holdout set for training 0.0..1.0 + drop_na: If `True` NA values will be removed from dataset + normalize: If `True` dataset will be normalized with MinMaxScaler + drop_columns: List of columns to drop from dataset + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # Link all the steps together by calling them and passing the output + # of one step as the input of the next step. + raw_data, target, _ = data_loader(random_state=random.randint(0,100)) + dataset_trn, dataset_tst = data_splitter( + dataset=raw_data, + test_size=test_size, + ) + dataset_trn, dataset_tst, _ = data_preprocessor( + dataset_trn=dataset_trn, + dataset_tst=dataset_tst, + drop_na=drop_na, + normalize=normalize, + drop_columns=drop_columns, + ) diff --git a/template/requirements.txt b/template/requirements.txt new file mode 100644 index 0000000..f3a893a --- /dev/null +++ b/template/requirements.txt @@ -0,0 +1,3 @@ +zenml[server]>=0.50.0 +notebook +scikit-learn<1.3 \ No newline at end of file diff --git a/template/run.ipynb b/template/run.ipynb new file mode 100644 index 0000000..87bd223 --- /dev/null +++ b/template/run.ipynb @@ -0,0 +1,773 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: zenml>=0.50.0 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from zenml[server]>=0.50.0->-r requirements.txt (line 1)) (0.50.0)\n", + "Requirement already satisfied: notebook in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from -r requirements.txt (line 2)) (7.0.6)\n", + "Requirement already satisfied: scikit-learn<1.3 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from -r requirements.txt (line 3)) (1.2.2)\n", + "Requirement already satisfied: alembic<1.9.0,>=1.8.1 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from zenml>=0.50.0->zenml[server]>=0.50.0->-r requirements.txt (line 1)) (1.8.1)\n", + "Requirement 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+ "Requirement already satisfied: scipy>=1.3.2 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from scikit-learn<1.3->-r requirements.txt (line 3)) (1.10.1)\n", + "Requirement already satisfied: joblib>=1.1.1 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from scikit-learn<1.3->-r requirements.txt (line 3)) (1.3.2)\n", + "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from scikit-learn<1.3->-r requirements.txt (line 3)) (3.2.0)\n", + "Requirement already satisfied: Jinja2 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from zenml[server]>=0.50.0->-r requirements.txt (line 1)) (3.1.2)\n", + "Requirement already satisfied: fastapi<0.100,>=0.75 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from zenml[server]>=0.50.0->-r requirements.txt (line 1)) (0.99.1)\n", + "Requirement already satisfied: 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"Requirement already satisfied: pytz>=2020.1 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from pandas>=1.1.5->zenml>=0.50.0->zenml[server]>=0.50.0->-r requirements.txt (line 1)) (2023.3.post1)\n", + "Requirement already satisfied: tzdata>=2022.1 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from pandas>=1.1.5->zenml>=0.50.0->zenml[server]>=0.50.0->-r requirements.txt (line 1)) (2023.3)\n", + "Requirement already satisfied: six>=1.5 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from python-dateutil<3.0.0,>=2.8.1->zenml>=0.50.0->zenml[server]>=0.50.0->-r requirements.txt (line 1)) (1.16.0)\n", + "Requirement already satisfied: markdown-it-py>=2.2.0 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from rich>=12.0.0->rich[jupyter]>=12.0.0->zenml>=0.50.0->zenml[server]>=0.50.0->-r requirements.txt (line 1)) (3.0.0)\n", + "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in 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the repo active stack to default.\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mZenML repository initialized at \u001b[0m\n", + "\u001b[2;35m/home/htahir1/workspace/zenml_io/template-starter/\u001b[0m\u001b[2;95mtemplate.\u001b[0m\n", + "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", + "\u001b[2;36m/home/htahir1/workspace/zenml_io/template-starter/template.\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", + "/home/htahir1/workspace/zenml_io/template-starter/template.\n", + "\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mThe local active stack was initialized to \u001b[0m\u001b[2;32m'default'\u001b[0m\u001b[2;36m. This local configuration \u001b[0m\n", + "\u001b[2;36mwill only take effect when you're running ZenML from the initialized repository \u001b[0m\n", + "\u001b[2;36mroot, or from a subdirectory. For more information on repositories and \u001b[0m\n", + "\u001b[2;36mconfigurations, please visit \u001b[0m\n", + "\u001b[2;4;94mhttps://docs.zenml.io/user-guide/starter-guide/understand-stacks.\u001b[0m\n" + ] + } + ], + "source": [ + "!zenml init" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "51690802-31a7-4e6d-9f88-e6457c6c4a96", + "metadata": {}, + "source": [ + "# Huggingface Model to Sagemaker Endpoint: Automating MLOps with ZenML\n", + "Deploying Huggingface models to AWS Sagemaker endpoints typically only requires a few lines of code. However, there's a growing demand to not just deploy, but to seamlessly automate the entire flow from training to production with comprehensive lineage tracking. ZenML adeptly fills this niche, providing an end-to-end MLOps solution for Huggingface users wishing to deploy to Sagemaker. Below, we’ll walk through the architecture that ZenML employs to bring a Huggingface model into production with AWS Sagemaker. Of course all of this can be adapted to not just Sagemaker, but any other model deployment service like GCP Vertex or Azure ML Platform.\n", + "\n", + "This blog post showcases one way of using ZenML pipelines to achieve this:\n", + "\n", + "- Create and version a dataset in a feature_engineering_pipeline.\n", + "- Train/Finetune a BERT-based Sentiment Analysis NLP model and push to Huggingface Hub in a training_pipeline.\n", + "- Promote this model to Production by comparing to previous models in a promotion_pipeline.\n", + "- Deploy the model at the Production Stage to a AWS Sagemaker endpoint with a deployment_pipeline.\n", + "\n", + "\"Pipelines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "500e3c24-b105-4a69-b2fc-e0ce1f1c1d46", + "metadata": {}, + "outputs": [], + "source": [ + "# Do the imports at the top\n", + "\n", + "import numpy as np\n", + "from datasets import DatasetDict, load_dataset\n", + "from typing_extensions import Annotated\n", + "from zenml import step\n", + "from zenml.logger import get_logger\n", + "\n", + "import os\n", + "from typing import Optional\n", + "from datetime import datetime as dt\n", + "\n", + "from zenml import pipeline\n", + "from zenml.model import ModelConfig\n", + "\n", + "from steps import (\n", + " data_loader,\n", + " notify_on_failure,\n", + " tokenization_step,\n", + " tokenizer_loader,\n", + " generate_reference_and_comparison_datasets,\n", + ")\n", + "from zenml.integrations.evidently.metrics import EvidentlyMetricConfig\n", + "from zenml.integrations.evidently.steps import (\n", + " EvidentlyColumnMapping,\n", + " evidently_report_step,\n", + ")\n", + "\n", + "from pipelines import (\n", + " sentinment_analysis_deploy_pipeline,\n", + " sentinment_analysis_promote_pipeline,\n", + " sentinment_analysis_training_pipeline,\n", + ")\n", + "\n", + "logger = get_logger(__name__)" + ] + }, + { + "cell_type": "markdown", + "id": "fc77b660-e206-46b1-a924-407e797a8f47", + "metadata": {}, + "source": [ + "# 🍳Breaking it down\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "31edaf46-6981-42be-99b7-9bdd91c160d5", + "metadata": {}, + "source": [ + "## 👶 Step 1: Start with feature engineering\n", + "\n", + "Automated feature engineering forms the foundation of this MLOps workflow. Thats why the first pipeline is the feature engineering pipeline. This pipeline loads some data from Huggingface and uses a base tokenizer to create a tokenized dataset. The data loader step is a simple Python function that returns a Huggingface dataloader object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35de0e4c-b6f8-4b68-927a-f40e4130dc93", + "metadata": {}, + "outputs": [], + "source": [ + "@step\n", + "def data_loader() -> Annotated[DatasetDict, \"dataset\"]:\n", + " logger.info(f\"Loading dataset airline_reviews... \")\n", + " hf_dataset = load_dataset(\"Shayanvsf/US_Airline_Sentiment\")\n", + " hf_dataset = hf_dataset.rename_column(\"airline_sentiment\", \"label\")\n", + " hf_dataset = hf_dataset.remove_columns(\n", + " [\"airline_sentiment_confidence\", \"negativereason_confidence\"]\n", + " )\n", + " return hf_dataset" + ] + }, + { + "cell_type": "markdown", + "id": "49e4462c-1e64-48d3-bae7-76696a958646", + "metadata": {}, + "source": [ + "Notice that you can give each dataset a name with Python’s Annotated object. The DatasetDict is a native Huggingface dataset which ZenML knows how to persist through steps. This flow ensures reproducibility and version control for every dataset iteration.\n", + "\n", + "Also notice this is a simple Python function, that can be called with the `entrypoint` wrapper:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18144a6b-c266-453d-82c8-b5d6aa1be0aa", + "metadata": {}, + "outputs": [], + "source": [ + "hf_dataset = data_loader.entrypoint()\n", + "print(hf_dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "31330d3c-044f-4912-8d36-74146f48cecf", + "metadata": {}, + "source": [ + "Now we put this a full feature engineering pipeline. Each run of the feature engineering pipeline produces a new dataset to use for the training pipeline. ZenML versions this data as it flows through the pipeline.\n", + "\n", + "\"Pipelines" + ] + }, + { + "cell_type": "markdown", + "id": "9511bd84-1e97-42db-9b75-06285cc6904c", + "metadata": {}, + "source": [ + "### Set your stack" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76f3a7e7-0d85-43b3-9e9f-4c7f20ea65e6", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack describe hf-sagemaker-local" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04b0bf69-70c6-4408-b18c-95df9e030c0c", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack set hf-sagemaker-local" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de5398a4-a9ec-42d6-bbd6-390244c52d13", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack get" + ] + }, + { + "cell_type": "markdown", + "id": "152f718d-70c2-4a29-a73e-37db85675cb8", + "metadata": {}, + "source": [ + "### Run the pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ca6c41e-e4b3-46d2-8264-9a453ac9aa3c", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "@pipeline(on_failure=notify_on_failure)\n", + "def sentinment_analysis_feature_engineering_pipeline(\n", + " lower_case: Optional[bool] = True,\n", + " padding: Optional[str] = \"max_length\",\n", + " max_seq_length: Optional[int] = 128,\n", + " text_column: Optional[str] = \"text\",\n", + " label_column: Optional[str] = \"label\",\n", + "):\n", + " # Link all the steps together by calling them and passing the output\n", + " # of one step as the input of the next step.\n", + "\n", + " ########## Load Dataset stage ##########\n", + " dataset = data_loader()\n", + "\n", + " ########## Data Quality stage ##########\n", + " reference_dataset, comparison_dataset = generate_reference_and_comparison_datasets(\n", + " dataset\n", + " )\n", + " text_data_report = evidently_report_step.with_options(\n", + " parameters=dict(\n", + " column_mapping=EvidentlyColumnMapping(\n", + " target=\"label\",\n", + " text_features=[\"text\"],\n", + " ),\n", + " metrics=[\n", + " EvidentlyMetricConfig.metric(\"DataQualityPreset\"),\n", + " EvidentlyMetricConfig.metric(\n", + " \"TextOverviewPreset\", column_name=\"text\"\n", + " ),\n", + " ],\n", + " # We need to download the NLTK data for the TextOverviewPreset\n", + " download_nltk_data=True,\n", + " ),\n", + " )\n", + " text_data_report(reference_dataset, comparison_dataset)\n", + "\n", + " ########## Tokenization stage ##########\n", + " tokenizer = tokenizer_loader(lower_case=lower_case)\n", + " tokenized_data = tokenization_step(\n", + " dataset=dataset,\n", + " tokenizer=tokenizer,\n", + " padding=padding,\n", + " max_seq_length=max_seq_length,\n", + " text_column=text_column,\n", + " label_column=label_column,\n", + " )\n", + " return tokenizer, tokenized_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c8a5be7-ebaa-41c4-ac23-4afc6e7e06aa", + "metadata": {}, + "outputs": [], + "source": [ + "# Run a pipeline with the required parameters. \n", + "no_cache: bool = True\n", + "zenml_model_name: str = \"distil_bert_sentiment_analysis\"\n", + "max_seq_length = 512\n", + "\n", + "# This executes all steps in the pipeline in the correct order using the orchestrator\n", + "# stack component that is configured in your active ZenML stack.\n", + "model_config = ModelConfig(\n", + " name=zenml_model_name,\n", + " license=\"Apache 2.0\",\n", + " description=\"Show case Model Control Plane.\",\n", + " create_new_model_version=True,\n", + " delete_new_version_on_failure=True,\n", + " tags=[\"sentiment_analysis\", \"huggingface\"],\n", + ")\n", + "\n", + "pipeline_args = {}\n", + "\n", + "if no_cache:\n", + " pipeline_args[\"enable_cache\"] = False\n", + "\n", + "# Execute Feature Engineering Pipeline\n", + "pipeline_args[\"model_config\"] = model_config\n", + "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"feature_engineering_config.yaml\")\n", + "run_args_feature = {\n", + " \"max_seq_length\": max_seq_length,\n", + "}\n", + "pipeline_args[\n", + " \"run_name\"\n", + "] = f\"sentinment_analysis_feature_engineering_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"\n", + "p = sentinment_analysis_feature_engineering_pipeline.with_options(**pipeline_args)\n", + "p(**run_args_feature)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e7c1ea2-64fe-478a-9963-17c7b7f62110", + "metadata": {}, + "outputs": [], + "source": [ + "from zenml.client import Client\n", + "from IPython.display import display, HTML\n", + "\n", + "client = Client()\n", + "# CHANGE THIS TO THE LATEST RUN ID\n", + "latest_run = client.get_pipeline_run(\"sentinment_analysis_feature_engineering_pipeline_run_2023_11_21_10_55_56\")\n", + "html = latest_run.steps[\"evidently_report_step\"].outputs['report_html'].load()\n", + "display(HTML(html))" + ] + }, + { + "cell_type": "markdown", + "id": "78ab8771-4421-4975-a3d5-12892a56b805", + "metadata": {}, + "source": [ + "## 💪 Step 2: Train the model with Huggingface Hub as the model registry\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "2843efa8-32b6-4b13-ac85-33c99cc94e3e", + "metadata": {}, + "source": [ + "Once the feature engineering pipeline has run a few times, we have many datasets to choose from. We can feed our desired one into a function that trains the model on the data. Thanks to the ZenML Huggingface integration, this data is loaded directly from the ZenML artifact store.\n", + "\n", + "\"Pipelines\n", + "\n", + "On the left side, we see our local MLOps stack, which defines our infrastructure and tooling we are using for this particular pipeline. ZenML makes it easy to run on a local stack on your development machine, or switch out the stack to run on a AWS Kubeflow-based stack (if you want to scale up).\n", + "\n", + "On the right side is the new kid on the block - the ZenML Model Control Plane. The Model Control Plane is a new feature in ZenML that allows users to have a complete overview of their machine learning models. It allows teams to consolidate all artifacts related to their ML models into one place, and manage its lifecycle easily as you can see from this view from the ZenML Cloud:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c99b20f-8e3b-4119-86e9-33dd1395470a", + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"trainer_config.yaml\")\n", + "\n", + "pipeline_args[\"enable_cache\"] = True\n", + "\n", + "run_args_train = {\n", + " \"num_epochs\": 1,\n", + " \"train_batch_size\": 64,\n", + " \"eval_batch_size\": 64,\n", + " \"learning_rate\": 2e-4,\n", + " \"weight_decay\": 0.01,\n", + " \"max_seq_length\": 512,\n", + "}\n", + "\n", + "# Use versioned artifacts from the last step\n", + "# run_args_train[\"dataset_artifact_id\"] = latest_run.steps['tokenization_step'].output.id\n", + "# run_args_train[\"tokenizer_artifact_id\"] = latest_run.steps['tokenizer_loader'].output.id\n", + "\n", + "# Configure the model\n", + "pipeline_args[\"model_config\"] = model_config\n", + "\n", + "pipeline_args[\n", + " \"run_name\"\n", + "] = f\"sentinment_analysis_training_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96592299-0090-4d2a-962e-6ca232c1fb75", + "metadata": {}, + "outputs": [], + "source": [ + "sentinment_analysis_training_pipeline.with_options(**pipeline_args)(\n", + " **run_args_train\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e24e29de-6d1b-41da-9ab2-ca2b32f1f540", + "metadata": {}, + "outputs": [], + "source": [ + "### Check out a new stack\n", + "!zenml stack describe hf-sagemaker-airflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c9a5bee-8465-4d41-888a-093f1f6a2ef1", + "metadata": {}, + "outputs": [], + "source": [ + "### Change the stack\n", + "!zenml stack set hf-sagemaker-airflow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3772c50-1c90-4ffc-8394-c9cfca16cc53", + "metadata": {}, + "outputs": [], + "source": [ + "sentinment_analysis_training_pipeline.with_options(**pipeline_args)(\n", + " **run_args_train\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "be79f454-a45d-4f5f-aa93-330d52069124", + "metadata": {}, + "source": [ + "## 🫅 Step 3: Promote the model to production\n" + ] + }, + { + "cell_type": "markdown", + "id": "5a09b432-7a66-473e-bdb6-ffdca730498b", + "metadata": {}, + "source": [ + "Following training, the automated promotion pipeline evaluates models against predefined metrics, identifying and marking the most performant one as 'Production ready'. This is another common use case for the Model Control Plane; we store the relevant metrics there to access them easily later.\n", + "\n", + "\"Pipelines" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5bac7ae5-70d0-449c-929c-e175c3062f2d", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack set hf-sagemaker-local" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "170c9ef6-4e6f-4e50-ac37-e05bef8570ea", + "metadata": {}, + "outputs": [], + "source": [ + "run_args_promoting = {}\n", + "model_config = ModelConfig(name=zenml_model_name)\n", + "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"promoting_config.yaml\")\n", + "\n", + "pipeline_args[\"model_config\"] = model_config\n", + "\n", + "pipeline_args[\n", + " \"run_name\"\n", + "] = f\"sentinment_analysis_promoting_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6df11e2-4591-4186-a8f8-243f9c4d1e3d", + "metadata": {}, + "outputs": [], + "source": [ + "sentinment_analysis_promote_pipeline.with_options(**pipeline_args)(\n", + " **run_args_promoting\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6efc4968-35fd-42e3-ba62-d8e1557aa0d6", + "metadata": {}, + "source": [ + "## 💯 Step 4: Deploy the model to AWS Sagemaker Endpoints\n" + ] + }, + { + "cell_type": "markdown", + "id": "577aff86-bde9-48d4-9b52-209cfed9fd4e", + "metadata": {}, + "source": [ + "This is the final step to automate the deployment of the slated production model to a Sagemaker endpoint. The deployment pipelines handles the complexities of AWS interactions and ensures that the model, along with its full history and context, is transitioned into a live environment ready for use. Here again we use the Model Control Plane interface to query the Huggingface revision and use that information to push to Huggingface Hub.\n", + "\n", + "\"Pipelines\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1513ab5f-de05-4344-9d2c-fedbfbd21ef0", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml stack set hf-sagemaker-local" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "606fdb3c-4eca-4d32-bccb-280743d15528", + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"deploying_config.yaml\")\n", + "\n", + "# Deploying pipeline has new ZenML model config\n", + "model_config = ModelConfig(\n", + " name=zenml_model_name,\n", + " version=ModelStages.PRODUCTION,\n", + ")\n", + "pipeline_args[\"model_config\"] = model_config\n", + "pipeline_args[\"enable_cache\"] = False\n", + "run_args_deploying = {}\n", + "pipeline_args[\n", + " \"run_name\"\n", + "] = f\"sentinment_analysis_deploy_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87f1f982-ab96-4207-8e7e-e318473587e9", + "metadata": {}, + "outputs": [], + "source": [ + "sentinment_analysis_deploy_pipeline.with_options(**pipeline_args)(\n", + " **run_args_deploying\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", + "metadata": {}, + "source": [ + "ZenML builds upon the straightforward deployment capability of Huggingface models to AWS Sagemaker, and transforms it into a sophisticated, repeatable, and transparent MLOps workflow. It takes charge of the intricate steps necessary for modern ML systems, ensuring that software engineering leads can focus on iteration and innovation rather than operational intricacies.\n", + "\n", + "To delve deeper into each stage, refer to the comprehensive guide on GitHub[: zenml-io/zenml-huggingface-sagemak](https://github.com/zenml-io/zenml-huggingface-sagemaker)er. Additionally[, this YouTube playli](https://www.youtube.com/watch?v=Q1EH2H8Akgo&list=PLhNrLW_IWplw6dBbmGcL828-atJMu3CwF)st provides a detailed visual walkthrough of the entire pipeline: Huggingface to Sagemaker ZenML tutorial.\n", + "\n", + "Interested in standardizing your MLOps workflows? ZenML Cloud is now available to all - get a managed ZenML server with important features such as RBAC and pipeline trigge[rs. Book a ](https://zenml.io/book-a-demo)demo with us now to learn how you can create your own MLOps pipelines today." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/template/run.py b/template/run.py new file mode 100644 index 0000000..fdd603d --- /dev/null +++ b/template/run.py @@ -0,0 +1,299 @@ +# {% include 'templates/license_header' %} + +import os +import click +from typing import Any, Dict, Optional +from steps import ( + data_loader, + data_preprocessor, + data_splitter, + model_trainer, + model_evaluator, +) +from pipelines import ( + _model_training, + _feature_engineering, + _inference, +) +from zenml.logger import get_logger +from zenml import ModelVersion, ModelStages, Client + +logger = get_logger(__name__) + +@click.command( + help=""" +ZenML NLP project CLI v0.0.1. + +Run the ZenML NLP project model training pipeline with various +options. + +Examples: + + + \b + # Run the pipeline with default options + python run.py + + \b + # Run the pipeline without cache + python run.py --no-cache + + \b + # Run the pipeline without NA drop and normalization, + # but dropping columns [A,B,C] and keeping 10% of dataset + # as test set. + python run.py --num-epochs 3 --train-batch-size 8 --eval-batch-size 8 + + \b + # Run the pipeline with Quality Gate for accuracy set at 90% for train set + # and 85% for test set. If any of accuracies will be lower - pipeline will fail. + python run.py --min-train-accuracy 0.9 --min-test-accuracy 0.85 --fail-on-accuracy-quality-gates + + +""" +) +@click.option( + "--no-cache", + is_flag=True, + default=False, + help="Disable caching for the pipeline run.", +) +@click.option( + "--num-epochs", + default=1, + type=click.INT, + help="Number of epochs to train the model for.", +) +@click.option( + "--train-batch-size", + default=8, + type=click.INT, + help="Batch size for training the model.", +) +@click.option( + "--eval-batch-size", + default=8, + type=click.INT, + help="Batch size for evaluating the model.", +) +@click.option( + "--learning-rate", + default=2e-5, + type=click.FLOAT, + help="Learning rate for training the model.", +) +@click.option( + "--weight-decay", + default=0.01, + type=click.FLOAT, + help="Weight decay for training the model.", +) +@click.option( + "--max-seq-length", + default=512, + type=click.INT, + help="The maximum total input sequence length after tokenization.", +) +@click.option( + "--dataset-name", + default="tokenized_dataset", + type=click.STRING, + help="The name of the dataset produced by feature engineering.", +) +@click.option( + "--dataset-version-name", + default=None, + type=click.STRING, + help="Version of the dataset produced by feature engineering. " + "If not specified, the a new version will be used.", +) +@click.option( + "--feature-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that creates the dataset.", +) +@click.option( + "--training-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that trains the model.", +) +@click.option( + "--dataset-artifact-id", + default=None, + type=click.STRING, + help="Dataset artifact id to use for training. If not specified, " + "the latest version will be used.", +) +@click.option( + "--tokenizer-artifact-id", + default=None, + type=click.STRING, + help="Tokenizer artifact id to use for training. If not specified, " + "the latest version will be used.", +) +@click.option( + "--promoting-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that promotes the model to staging.", +) +@click.option( + "--deploying-pipeline", + is_flag=True, + default=False, + help="Whether to run the pipeline that deploys the model to selected deployment platform.", +) +@click.option( + "--zenml-model-name", + default="distil_bert_sentiment_analysis", + type=click.STRING, + help="Name of the ZenML Model.", +) +def main( + no_cache: bool = True, + num_epochs: int = 3, + train_batch_size: int = 8, + eval_batch_size: int = 8, + learning_rate: float = 2e-5, + weight_decay: float = 0.01, + max_seq_length: int = 512, + dataset_artifact_id: Optional[str] = None, + tokenizer_artifact_id: Optional[str] = None, + dataset_name: str = "tokenized_dataset", + dataset_version_name: Optional[str] = None, + feature_pipeline: bool = False, + training_pipeline: bool = False, + promoting_pipeline: bool = False, + deploying_pipeline: bool = False, + zenml_model_name: str = "distil_bert_sentiment_analysis", +): + """Main entry point for the pipeline execution. + + This entrypoint is where everything comes together: + + * configuring pipeline with the required parameters + (some of which may come from command line arguments) + * launching the pipeline + """ + + # Run a pipeline with the required parameters. This executes + # all steps in the pipeline in the correct order using the orchestrator + # stack component that is configured in your active ZenML stack. + config_folder = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + "configs", + ) + model_version = ModelVersion( + name=zenml_model_name, + license="Apache 2.0", + description="Show case Model Control Plane.", + create_new_model_version=True, + delete_new_version_on_failure=True, + tags=["sentiment_analysis", "huggingface"], + ) + + pipeline_args = {} + + if no_cache: + pipeline_args["enable_cache"] = False + + # Execute Feature Engineering Pipeline + if feature_pipeline: + pipeline_args["model_version"] = model_version + pipeline_args["config_path"] = os.path.join(config_folder, "feature_engineering_config.yaml") + run_args_feature = { + "max_seq_length": max_seq_length, + } + pipeline_args[ + "run_name" + ] = f"sentinment_analysis_feature_engineering_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" + sentinment_analysis_feature_engineering_pipeline.with_options(**pipeline_args)( + **run_args_feature + ) + logger.info("Feature Engineering pipeline finished successfully!") + + # Execute Training Pipeline + if training_pipeline: + pipeline_args["config_path"] = os.path.join(config_folder, "trainer_config.yaml") + + run_args_train = { + "num_epochs": num_epochs, + "train_batch_size": train_batch_size, + "eval_batch_size": eval_batch_size, + "learning_rate": learning_rate, + "weight_decay": weight_decay, + "max_seq_length": max_seq_length, + "dataset_artifact_id": dataset_artifact_id, + "tokenizer_artifact_id": tokenizer_artifact_id, + } + + # If dataset_version_name is specified, use versioned artifacts + if dataset_version_name: + client = Client() + tokenized_dataset_artifact = client.get_artifact( + dataset_name, dataset_version_name + ) + # base tokenizer is always the same version + # as the dataset version + tokenized_tokenizer_artifact = client.get_artifact( + "base_tokenizer", dataset_version_name + ) + # Use versioned artifacts + run_args_train["dataset_artifact_id"] = tokenized_dataset_artifact.id + run_args_train["tokenizer_artifact_id"] = tokenized_tokenizer_artifact.id + + pipeline_args["model_version"] = model_version + + pipeline_args[ + "run_name" + ] = f"sentinment_analysis_training_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" + + sentinment_analysis_training_pipeline.with_options(**pipeline_args)( + **run_args_train + ) + logger.info("Training pipeline finished successfully!") + + # Execute Promoting Pipeline + if promoting_pipeline: + run_args_promoting = {} + # Promoting pipeline always check latest version + model_version = ModelVersion( + name=zenml_model_name, + version=ModelStages.LATEST, + ) + pipeline_args["config_path"] = os.path.join(config_folder, "promoting_config.yaml") + + pipeline_args["model_version"] = model_version + + pipeline_args[ + "run_name" + ] = f"sentinment_analysis_promoting_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" + sentinment_analysis_promote_pipeline.with_options(**pipeline_args)( + **run_args_promoting + ) + logger.info("Promoting pipeline finished successfully!") + + if deploying_pipeline: + pipeline_args["config_path"] = os.path.join(config_folder, "deploying_config.yaml") + + # Deploying pipeline has new ZenML model config + model_version = ModelVersion( + name=zenml_model_name, + version=ModelStages.PRODUCTION, + ) + pipeline_args["model_version"] = model_version + pipeline_args["enable_cache"] = False + run_args_deploying = {} + pipeline_args[ + "run_name" + ] = f"sentinment_analysis_deploy_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" + sentinment_analysis_deploy_pipeline.with_options(**pipeline_args)( + **run_args_deploying + ) + logger.info("Deploying pipeline finished successfully!") + + +if __name__ == "__main__": + main() diff --git a/template/steps/__init__.py b/template/steps/__init__.py new file mode 100644 index 0000000..a49bff9 --- /dev/null +++ b/template/steps/__init__.py @@ -0,0 +1,17 @@ +# {% include 'template/license_header' %} + +from data_loader import ( + data_loader, +) +from data_preprocessor import ( + data_preprocessor, +) +from data_splitter import ( + data_splitter, +) +from model_evaluator import ( + model_evaluator, +) +from model_trainer import ( + model_trainer, +) diff --git a/template/steps/data_loader.py b/template/steps/data_loader.py new file mode 100644 index 0000000..8b8de4d --- /dev/null +++ b/template/steps/data_loader.py @@ -0,0 +1,55 @@ +# {% include 'template/license_header' %} + +from typing import Tuple + +import pandas as pd +from sklearn.datasets import load_breast_cancer +from typing_extensions import Annotated +from zenml import step +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@step +def data_loader( + random_state: int, is_inference: bool = False +) -> Tuple[ + Annotated[pd.DataFrame, "dataset"], + Annotated[str, "target"], + Annotated[int, "random_state"], +]: + """Dataset reader step. + + This is an example of a dataset reader step that load Breast Cancer dataset. + + This step is parameterized, which allows you to configure the step + independently of the step code, before running it in a pipeline. + In this example, the step can be configured with number of rows and logic + to drop target column or not. See the documentation for more information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + is_inference: If `True` subset will be returned and target column + will be removed from dataset. + random_state: Random state for sampling + + Returns: + The dataset artifact as Pandas DataFrame and name of target column. + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + dataset = load_breast_cancer(as_frame=True) + inference_size = int(len(dataset.target) * 0.05) + target = "target" + dataset: pd.DataFrame = dataset.frame + inference_subset = dataset.sample(inference_size, random_state=random_state) + if is_inference: + dataset = inference_subset + dataset.drop(columns=target, inplace=True) + else: + dataset.drop(inference_subset.index, inplace=True) + dataset.reset_index(drop=True, inplace=True) + logger.info(f"Dataset with {len(dataset)} records loaded!") + ### YOUR CODE ENDS HERE ### + return dataset, target, random_state diff --git a/template/steps/data_preprocessor.py b/template/steps/data_preprocessor.py new file mode 100644 index 0000000..2572c6f --- /dev/null +++ b/template/steps/data_preprocessor.py @@ -0,0 +1,67 @@ +# {% include 'template/license_header' %} + +from typing import List, Optional, Tuple +from typing_extensions import Annotated + +import pandas as pd +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import MinMaxScaler +from utils.preprocess import ColumnsDropper, DataFrameCaster, NADropper +from zenml import step + + +@step +def data_preprocessor( + dataset_trn: pd.DataFrame, + dataset_tst: pd.DataFrame, + drop_na: Optional[bool] = None, + normalize: Optional[bool] = None, + drop_columns: Optional[List[str]] = None, +) -> Tuple[ + Annotated[pd.DataFrame, "dataset_trn"], + Annotated[pd.DataFrame, "dataset_tst"], + Annotated[Pipeline, "preprocess_pipeline"], +]: + """Data preprocessor step. + + This is an example of a data processor step that prepares the data so that + it is suitable for model training. It takes in a dataset as an input step + artifact and performs any necessary preprocessing steps like cleaning, + feature engineering, feature selection, etc. It then returns the processed + dataset as an step output artifact. + + This step is parameterized, which allows you to configure the step + independently of the step code, before running it in a pipeline. + In this example, the step can be configured to drop NA values, drop some + columns and normalize numerical columns. See the documentation for more + information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + dataset_trn: The train dataset. + dataset_tst: The test dataset. + drop_na: If `True` all NA rows will be dropped. + normalize: If `True` all numeric fields will be normalized. + drop_columns: List of column names to drop. + + Returns: + The processed datasets (dataset_trn, dataset_tst) and fitted `Pipeline` object. + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # We use the sklearn pipeline to chain together multiple preprocessing steps + preprocess_pipeline = Pipeline([("passthrough", "passthrough")]) + if drop_na: + preprocess_pipeline.steps.append(("drop_na", NADropper())) + if drop_columns: + # Drop columns + preprocess_pipeline.steps.append(("drop_columns", ColumnsDropper(drop_columns))) + if normalize: + # Normalize the data + preprocess_pipeline.steps.append(("normalize", MinMaxScaler())) + preprocess_pipeline.steps.append(("cast", DataFrameCaster(dataset_trn.columns))) + dataset_trn = preprocess_pipeline.fit_transform(dataset_trn) + dataset_tst = preprocess_pipeline.transform(dataset_tst) + ### YOUR CODE ENDS HERE ### + + return dataset_trn, dataset_tst, preprocess_pipeline diff --git a/template/steps/data_splitter.py b/template/steps/data_splitter.py new file mode 100644 index 0000000..121cfd2 --- /dev/null +++ b/template/steps/data_splitter.py @@ -0,0 +1,47 @@ +# {% include 'template/license_header' %} + +from typing import Tuple +from typing_extensions import Annotated + +import pandas as pd +from sklearn.model_selection import train_test_split +from zenml import step + + +@step +def data_splitter( + dataset: pd.DataFrame, test_size: float = 0.2 +) -> Tuple[ + Annotated[pd.DataFrame, "raw_dataset_trn"], + Annotated[pd.DataFrame, "raw_dataset_tst"], +]: + """Dataset splitter step. + + This is an example of a dataset splitter step that splits the data + into train and test set before passing it to ML model. + + This step is parameterized, which allows you to configure the step + independently of the step code, before running it in a pipeline. + In this example, the step can be configured to use different test + set sizes. See the documentation for more information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + dataset: Dataset read from source. + test_size: 0.0..1.0 defining portion of test set. + + Returns: + The split dataset: dataset_trn, dataset_tst. + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + dataset_trn, dataset_tst = train_test_split( + dataset, + test_size=test_size, + random_state=42, + shuffle=True, + ) + dataset_trn = pd.DataFrame(dataset_trn, columns=dataset.columns) + dataset_tst = pd.DataFrame(dataset_tst, columns=dataset.columns) + ### YOUR CODE ENDS HERE ### + return dataset_trn, dataset_tst diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py new file mode 100644 index 0000000..c7640c2 --- /dev/null +++ b/template/steps/model_evaluator.py @@ -0,0 +1,89 @@ +# {% include 'template/license_header' %} + +import pandas as pd +from sklearn.base import ClassifierMixin +from zenml import step +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@step +def model_evaluator( + model: ClassifierMixin, + dataset_trn: pd.DataFrame, + dataset_tst: pd.DataFrame, + target: str, + min_train_accuracy: float = 0.0, + min_test_accuracy: float = 0.0, + fail_on_accuracy_quality_gates: bool = False, +) -> None: + """Evaluate a trained model. + + This is an example of a model evaluation step that takes in a model artifact + previously trained by another step in your pipeline, and a training + and validation data set pair which it uses to evaluate the model's + performance. The model metrics are then returned as step output artifacts + (in this case, the model accuracy on the train and test set). + + The suggested step implementation also outputs some warnings if the model + performance does not meet some minimum criteria. This is just an example of + how you can use steps to monitor your model performance and alert you if + something goes wrong. As an alternative, you can raise an exception in the + step to force the pipeline run to fail early and all subsequent steps to + be skipped. + + This step is parameterized to configure the step independently of the step code, + before running it in a pipeline. In this example, the step can be configured + to use different values for the acceptable model performance thresholds and + to control whether the pipeline run should fail if the model performance + does not meet the minimum criteria. See the documentation for more + information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + model: The pre-trained model artifact. + dataset_trn: The train dataset. + dataset_tst: The test dataset. + target: Name of target columns in dataset. + min_train_accuracy: Minimal acceptable training accuracy value. + min_test_accuracy: Minimal acceptable testing accuracy value. + fail_on_accuracy_quality_gates: If `True` a `RuntimeException` is raised + upon not meeting one of the minimal accuracy thresholds. + + Raises: + RuntimeError: if any of accuracies is lower than respective threshold + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # Calculate the model accuracy on the train and test set + trn_acc = model.score( + dataset_trn.drop(columns=[target]), + dataset_trn[target], + ) + logger.info(f"Train accuracy={trn_acc*100:.2f}%") + tst_acc = model.score( + dataset_tst.drop(columns=[target]), + dataset_tst[target], + ) + logger.info(f"Test accuracy={tst_acc*100:.2f}%") + + messages = [] + if trn_acc < min_train_accuracy: + messages.append( + f"Train accuracy {trn_acc*100:.2f}% is below {min_train_accuracy*100:.2f}% !" + ) + if tst_acc < min_test_accuracy: + messages.append( + f"Test accuracy {tst_acc*100:.2f}% is below {min_test_accuracy*100:.2f}% !" + ) + if fail_on_accuracy_quality_gates and messages: + raise RuntimeError( + "Model performance did not meet the minimum criteria:\n" + + "\n".join(messages) + ) + else: + for message in messages: + logger.warning(message) + + ### YOUR CODE ENDS HERE ### diff --git a/template/steps/model_trainer.py b/template/steps/model_trainer.py new file mode 100644 index 0000000..1718640 --- /dev/null +++ b/template/steps/model_trainer.py @@ -0,0 +1,74 @@ +# {% include 'template/license_header' %} + +from typing_extensions import Annotated + +import pandas as pd +from sklearn.base import ClassifierMixin +from zenml import ArtifactConfig, log_artifact_metadata, step +from zenml.client import Client +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@step +def model_trainer( + dataset_trn: pd.DataFrame, + model: ClassifierMixin, + target: str, + name: str, +) -> Annotated[ + ClassifierMixin, ArtifactConfig(name="model", is_model_artifact=True) +]: + """Configure and train a model on the training dataset. + + This is an example of a model training step that takes in a dataset artifact + previously loaded and pre-processed by other steps in your pipeline, then + configures and trains a model on it. The model is then returned as a step + output artifact. + + Model training steps should have caching disabled if they are not + deterministic (i.e. if the model training involve some random processes + like initializing weights or shuffling data that are not controlled by + setting a fixed random seed). This example step ensures the outcome is + deterministic by initializing the model with a fixed random seed. + + This step is parameterized to configure the step independently of the step code, + before running it in a pipeline. In this example, the step can be configured + to use a different model, change the random seed, or pass different + hyperparameters to the model constructor. See the documentation for more + information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + dataset_trn: The preprocessed train dataset. + model: The model instance to train. + target: Name of target columns in dataset. + name: The name of the model. + + Returns: + The trained model artifact. + """ + + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # Initialize the model with the hyperparameters indicated in the step + # parameters and train it on the training set. + logger.info(f"Training model {model}...") + model.fit( + dataset_trn.drop(columns=[target]), + dataset_trn[target], + ) + + # keep track of mlflow version for future use + model_registry = Client().active_stack.model_registry + if model_registry: + versions = model_registry.list_model_versions(name=name) + if versions: + log_artifact_metadata( + metadata={"model_registry_version": versions[-1].version}, + artifact_name="model", + ) + ### YOUR CODE ENDS HERE ### + + return model diff --git a/template/utils/__init__.py b/template/utils/__init__.py new file mode 100644 index 0000000..4bc11e5 --- /dev/null +++ b/template/utils/__init__.py @@ -0,0 +1 @@ +# {% include 'template/license_header' %} diff --git a/template/utils/preprocess.py b/template/utils/preprocess.py new file mode 100644 index 0000000..2dd4a85 --- /dev/null +++ b/template/utils/preprocess.py @@ -0,0 +1,41 @@ +# {% include 'template/license_header' %} + +from typing import Union + +import pandas as pd + + +class NADropper: + """Support class to drop NA values in sklearn Pipeline.""" + + def fit(self, *args, **kwargs): + return self + + def transform(self, X: Union[pd.DataFrame, pd.Series]): + return X.dropna() + + +class ColumnsDropper: + """Support class to drop specific columns in sklearn Pipeline.""" + + def __init__(self, columns): + self.columns = columns + + def fit(self, *args, **kwargs): + return self + + def transform(self, X: Union[pd.DataFrame, pd.Series]): + return X.drop(columns=self.columns) + + +class DataFrameCaster: + """Support class to cast type back to pd.DataFrame in sklearn Pipeline.""" + + def __init__(self, columns): + self.columns = columns + + def fit(self, *args, **kwargs): + return self + + def transform(self, X): + return pd.DataFrame(X, columns=self.columns) diff --git a/template/{% if open_source_license %}LICENSE{% endif %} b/template/{% if open_source_license %}LICENSE{% endif %} new file mode 100644 index 0000000..3de332b --- /dev/null +++ b/template/{% if open_source_license %}LICENSE{% endif %} @@ -0,0 +1 @@ +{% include 'template/license' %} \ No newline at end of file diff --git a/template/{{ _copier_conf.answers_file }} b/template/{{ _copier_conf.answers_file }} new file mode 100644 index 0000000..ea97bd4 --- /dev/null +++ b/template/{{ _copier_conf.answers_file }} @@ -0,0 +1,2 @@ +# Changes here will be overwritten by Copier +{{ _copier_answers|to_nice_yaml -}} \ No newline at end of file diff --git a/tests/test_starter_template.py b/tests/test_starter_template.py index c7ac609..57c3a6f 100644 --- a/tests/test_starter_template.py +++ b/tests/test_starter_template.py @@ -11,93 +11,108 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express # or implied. See the License for the specific language governing # permissions and limitations under the License. + + +import os +import pathlib +import platform import shutil import subprocess import sys from typing import Optional -from copier import Worker -import os -import pathlib -import pytest -from zenml.enums import ExecutionStatus -from zenml.post_execution import get_pipeline +import pytest +from copier import Worker from zenml.client import Client +from zenml.enums import ExecutionStatus -TEMPLATE_DIRECTORY = str(pathlib.Path(__file__).parent.parent) +TEMPLATE_DIRECTORY = str(pathlib.Path.joinpath(pathlib.Path(__file__).parent.parent)) def generate_and_run_project( tmp_path_factory: pytest.TempPathFactory, open_source_license: Optional[str] = "apache", - auto_format: bool = True, - use_step_params: bool = True, - use_custom_artifacts: bool = True, - configurable_dataset: bool = True, - configurable_model: bool = True, - sklearn_dataset_name: str = "wine", - sklearn_model_name: str = "SVC", - pipeline_name: str = "model_training_pipeline", + product_name: str = "e2e_pipeline_pytest", + hyperparameters_tuning: bool = True, + metric_compare_promotion: bool = True, + data_quality_checks: bool = True, + target_environment: str = "staging", + notify_on_failures: bool = True, + notify_on_successes: bool = False, + zenml_server_url: str = "", ): """Generate and run the starter project with different options.""" answers = { - "template": "starter", - "project_name": "Pytest Starter", + "project_name": "Pytest Templated Project", "version": "0.0.1", "open_source_license": str(open_source_license).lower(), - "email": "pytest@zenml.io", - "full_name": "Pytest", - "auto_format": auto_format, - "use_custom_artifacts": use_custom_artifacts, - "use_step_params": use_step_params, - "configurable_dataset": configurable_dataset, - "configurable_model": configurable_model, - "sklearn_dataset_name": sklearn_dataset_name, - "sklearn_model_name": sklearn_model_name, + "product_name": product_name, + "hyperparameters_tuning": hyperparameters_tuning, + "metric_compare_promotion": metric_compare_promotion, + "data_quality_checks": data_quality_checks, + "target_environment": target_environment, + "notify_on_failures": notify_on_failures, + "notify_on_successes": notify_on_successes, + "zenml_server_url": zenml_server_url, } + if open_source_license: + answers["email"] = "pytest@zenml.io" + answers["full_name"] = "Pytest" # generate the template in a temp path current_dir = os.getcwd() dst_path = tmp_path_factory.mktemp("pytest-template") + print("TEMPLATE_DIR:", TEMPLATE_DIRECTORY) + print("dst_path:", dst_path) + print("current_dir:", current_dir) os.chdir(str(dst_path)) with Worker( src_path=TEMPLATE_DIRECTORY, dst_path=str(dst_path), data=answers, unsafe=True, + vcs_ref="HEAD", ) as worker: worker.run_copy() - # run the project - call = [sys.executable, "run.py"] - - try: - subprocess.check_call( - call, - cwd=str(dst_path), - env=os.environ.copy(), - ) - except Exception as e: - raise RuntimeError( - f"Failed to run project generated with parameters: {answers}" - ) from e - - # check the pipeline run is successful - pipeline = get_pipeline(pipeline_name) - assert pipeline - runs = pipeline.runs - assert len(runs) == 1 - assert runs[0].status == ExecutionStatus.COMPLETED - - # clean up - Client().delete_pipeline(pipeline_name) + # MLFlow Deployer not supported on Windows + # MLFlow `service daemon is not running` error on MacOS + if platform.system().lower() not in ["windows"]: + # run the project + call = [sys.executable, "run.py"] + + try: + subprocess.check_output( + call, + cwd=str(dst_path), + env=os.environ.copy(), + stderr=subprocess.STDOUT, + ) + except subprocess.CalledProcessError as e: + raise RuntimeError( + f"Failed to run project generated with parameters: {answers}\n" + f"{e.output.decode()}" + ) from e + + # check the pipeline run is successful + for pipeline_suffix in ["_training", "_batch_inference"]: + pipeline = Client().get_pipeline(product_name + pipeline_suffix) + assert pipeline + runs = pipeline.runs + assert len(runs) == 1 + assert runs[0].status == ExecutionStatus.COMPLETED + + # clean up + Client().delete_pipeline(product_name + pipeline_suffix) + Client().delete_model(product_name) + Client().active_stack.model_registry.delete_model(product_name) os.chdir(current_dir) shutil.rmtree(dst_path) -@pytest.mark.parametrize("open_source_license", ["mit", None]) +@pytest.mark.parametrize("open_source_license", ["mit", None], ids=["oss", "css"]) def test_generate_license( clean_zenml_client, tmp_path_factory: pytest.TempPathFactory, @@ -111,75 +126,95 @@ def test_generate_license( ) -def test_no_auto_format( +def test_custom_product_name( + clean_zenml_client, + tmp_path_factory: pytest.TempPathFactory, +): + """Test using custom pipeline name.""" + + generate_and_run_project( + tmp_path_factory=tmp_path_factory, + product_name="custom_product_name", + ) + + +def test_no_hp_tuning( + clean_zenml_client, + tmp_path_factory: pytest.TempPathFactory, +): + """Test turning off hyperparameter tuning.""" + + generate_and_run_project( + tmp_path_factory=tmp_path_factory, hyperparameters_tuning=False + ) + + +def test_latest_promotion( + clean_zenml_client, + tmp_path_factory: pytest.TempPathFactory, +): + """Test using latest promotion.""" + + generate_and_run_project( + tmp_path_factory=tmp_path_factory, metric_compare_promotion=False + ) + + +def test_no_data_quality_checks( clean_zenml_client, tmp_path_factory: pytest.TempPathFactory, ): - """Test turning off code auto-format.""" + """Test skipping Data Quality checks.""" generate_and_run_project( tmp_path_factory=tmp_path_factory, - auto_format=False, + data_quality_checks=False, ) -@pytest.mark.parametrize("use_custom_artifacts", [True, False]) -@pytest.mark.parametrize("sklearn_dataset_name", ["wine", "iris"]) -@pytest.mark.parametrize( - "sklearn_model_name", - [ - "SGDClassifier", - "DecisionTreeClassifier", - ], -) -def test_step_params_disabled( +def test_production_environment( clean_zenml_client, tmp_path_factory: pytest.TempPathFactory, - use_custom_artifacts: bool, - sklearn_dataset_name: str, - sklearn_model_name: str, ): - """Test generating the starter template with step parameters disabled .""" + """Test deploying to production stage.""" generate_and_run_project( tmp_path_factory=tmp_path_factory, - use_step_params=False, - use_custom_artifacts=use_custom_artifacts, - configurable_dataset=False, - configurable_model=False, - sklearn_dataset_name=sklearn_dataset_name, - sklearn_model_name=sklearn_model_name, + target_environment="production", ) -@pytest.mark.parametrize("use_custom_artifacts", [True, False]) -@pytest.mark.parametrize("configurable_dataset", [True, False]) -@pytest.mark.parametrize("configurable_model", [True, False]) -@pytest.mark.parametrize("sklearn_dataset_name", ["iris", "breast_cancer"]) -@pytest.mark.parametrize( - "sklearn_model_name", - [ - "RandomForestClassifier", - "KNeighborsClassifier", - ], -) -def test_step_params_enabled( +def test_no_notify_on_failure( clean_zenml_client, tmp_path_factory: pytest.TempPathFactory, - use_custom_artifacts: bool, - configurable_dataset: bool, - configurable_model: bool, - sklearn_dataset_name: str, - sklearn_model_name: str, ): - """Test generating the starter template with step parameters enabled .""" + """Test skipping notification on failure.""" generate_and_run_project( tmp_path_factory=tmp_path_factory, - use_step_params=False, - use_custom_artifacts=use_custom_artifacts, - configurable_dataset=configurable_dataset, - configurable_model=configurable_model, - sklearn_dataset_name=sklearn_dataset_name, - sklearn_model_name=sklearn_model_name, + notify_on_failures=False, ) + + +def test_notify_on_success( + clean_zenml_client, + tmp_path_factory: pytest.TempPathFactory, +): + """Test skipping notification on success.""" + + generate_and_run_project( + tmp_path_factory=tmp_path_factory, + notify_on_successes=True, + ) + + +def test_custom_zenml_server_url( + clean_zenml_client, + tmp_path_factory: pytest.TempPathFactory, +): + """Test deploying to production stage.""" + + generate_and_run_project( + tmp_path_factory=tmp_path_factory, + zenml_server_url="foo", + ) \ No newline at end of file From 2dd1ac4b0d705adcd6b90a3521ee2d79b889d8dc Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 1 Dec 2023 09:38:05 +0100 Subject: [PATCH 02/48] Template --- template/configs/feature_engineering.yaml | 12 + template/run.ipynb | 326 ++++++++++------------ template/steps/__init__.py | 16 +- template/steps/inference_predict.py | 61 ++++ template/steps/inference_preprocessor.py | 53 ++++ 5 files changed, 278 insertions(+), 190 deletions(-) create mode 100644 template/steps/inference_predict.py create mode 100644 template/steps/inference_preprocessor.py diff --git a/template/configs/feature_engineering.yaml b/template/configs/feature_engineering.yaml index e69de29..daa91a1 100644 --- a/template/configs/feature_engineering.yaml +++ b/template/configs/feature_engineering.yaml @@ -0,0 +1,12 @@ +# environment configuration +settings: + docker: + required_integrations: + - sklearn + +# configuration of the Model Control Plane +model_version: + name: breast_cancer_classifier + license: Apache 2.0 + description: Classification of Breast Cancer Dataset. + tags: ["classification", "sklearn"] diff --git a/template/run.ipynb b/template/run.ipynb index 87bd223..48d9165 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -2,211 +2,167 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "id": "a681b91b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: zenml>=0.50.0 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from zenml[server]>=0.50.0->-r requirements.txt (line 1)) (0.50.0)\n", - "Requirement already satisfied: notebook in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from -r requirements.txt (line 2)) (7.0.6)\n", - "Requirement already satisfied: scikit-learn<1.3 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from -r requirements.txt (line 3)) (1.2.2)\n", - 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"Requirement already satisfied: types-python-dateutil>=2.8.10 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook->-r requirements.txt (line 2)) (2.8.19.14)\n" - ] - } - ], + "outputs": [], "source": [ "!pip install -r requirements.txt" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "081d5616", + "metadata": {}, + "outputs": [], + "source": [ + "!zenml init" + ] + }, { "cell_type": "code", "execution_count": 2, + "id": "79f775f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Do the imports at the top\n", + "\n", + "import random\n", + "import numpy as np\n", + "from typing_extensions import Annotated\n", + "from zenml import step\n", + "from zenml.logger import get_logger\n", + "\n", + "import os\n", + "from typing import Optional, List\n", + "from datetime import datetime as dt\n", + "\n", + "from zenml import pipeline\n", + "from zenml import ModelVersion\n", + "\n", + "from steps import (\n", + " data_loader,\n", + " data_preprocessor,\n", + " data_splitter,\n", + " model_evaluator,\n", + " model_trainer,\n", + " inference_predict,\n", + " inference_preprocessor\n", + ")\n", + "\n", + "logger = get_logger(__name__)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b50a9537", + "metadata": {}, + "outputs": [], + "source": [ + "@pipeline(enable_cache=False)\n", + "def _feature_engineering(\n", + " test_size: float = 0.2,\n", + " drop_na: Optional[bool] = None,\n", + " normalize: Optional[bool] = None,\n", + " drop_columns: Optional[List[str]] = None,\n", + "):\n", + " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", + " # Link all the steps together by calling them and passing the output\n", + " # of one step as the input of the next step.\n", + " raw_data, target, _ = data_loader(random_state=random.randint(0,100))\n", + " dataset_trn, dataset_tst = data_splitter(\n", + " dataset=raw_data,\n", + " test_size=test_size,\n", + " )\n", + " dataset_trn, dataset_tst, _ = data_preprocessor(\n", + " dataset_trn=dataset_trn,\n", + " dataset_tst=dataset_tst,\n", + " drop_na=drop_na,\n", + " normalize=normalize,\n", + " drop_columns=drop_columns,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bc5feef4-7016-420e-9af9-2e87ff666f74", + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_args = {}\n", + "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"feature_engineering.yaml\")\n", + "fe_p_configured = _feature_engineering.with_options(**pipeline_args)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "75cf3740-b2d8-4c4b-b91b-dc1637000880", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[?25l\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/home/htahir1/workspace/zenml_io/template-starter/template.\n", - "\u001b[1;35mSetting the repo active workspace to 'default'.\u001b[0m\n", - "\u001b[33mSetting the repo active stack to default.\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mZenML repository initialized at \u001b[0m\n", - "\u001b[2;35m/home/htahir1/workspace/zenml_io/template-starter/\u001b[0m\u001b[2;95mtemplate.\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", - "\u001b[2;36m/home/htahir1/workspace/zenml_io/template-starter/template.\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/home/htahir1/workspace/zenml_io/template-starter/template.\n", - "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mThe local active stack was initialized to \u001b[0m\u001b[2;32m'default'\u001b[0m\u001b[2;36m. This local configuration \u001b[0m\n", - "\u001b[2;36mwill only take effect when you're running ZenML from the initialized repository \u001b[0m\n", - "\u001b[2;36mroot, or from a subdirectory. For more information on repositories and \u001b[0m\n", - "\u001b[2;36mconfigurations, please visit \u001b[0m\n", - "\u001b[2;4;94mhttps://docs.zenml.io/user-guide/starter-guide/understand-stacks.\u001b[0m\n" + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36m_feature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m9\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36m_feature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.257s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m9.532s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m12.232s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36m_feature_engineering-2023_11_30-10_09_12_105336\u001b[1;35m has finished in \u001b[0m\u001b[1;36m35.624s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/31df4e45-78ee-4712-874a-482a705ce545/runs/059cb308-1c17-4d65-9939-60af42d62179/dag\u001b[0m\n" ] } ], "source": [ - "!zenml init" + "latest_run = fe_p_configured()" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "latest_run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/template/steps/__init__.py b/template/steps/__init__.py index a49bff9..708f1d5 100644 --- a/template/steps/__init__.py +++ b/template/steps/__init__.py @@ -1,17 +1,23 @@ # {% include 'template/license_header' %} -from data_loader import ( +from .data_loader import ( data_loader, ) -from data_preprocessor import ( +from .data_preprocessor import ( data_preprocessor, ) -from data_splitter import ( +from .data_splitter import ( data_splitter, ) -from model_evaluator import ( +from .model_evaluator import ( model_evaluator, ) -from model_trainer import ( +from .model_trainer import ( model_trainer, ) +from .inference_preprocessor import ( + inference_preprocessor, +) +from .inference_predict import ( + inference_predict, +) diff --git a/template/steps/inference_predict.py b/template/steps/inference_predict.py new file mode 100644 index 0000000..9c69a5f --- /dev/null +++ b/template/steps/inference_predict.py @@ -0,0 +1,61 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2023. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import Optional + +import pandas as pd +from typing_extensions import Annotated + +from zenml import get_step_context, step +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@step +def inference_predict( + dataset_inf: pd.DataFrame, +) -> Annotated[pd.Series, "predictions"]: + """Predictions step. + + This is an example of a predictions step that takes the data in and returns + predicted values. + + This step is parameterized, which allows you to configure the step + independently of the step code, before running it in a pipeline. + In this example, the step can be configured to use different input data. + See the documentation for more information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + dataset_inf: The inference dataset. + + Returns: + The predictions as pandas series + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + model_version = get_step_context().model_version + + # run prediction from memory + predictor = model_version.load_artifact("model") + predictions = predictor.predict(dataset_inf) + + predictions = pd.Series(predictions, name="predicted") + ### YOUR CODE ENDS HERE ### + + return predictions diff --git a/template/steps/inference_preprocessor.py b/template/steps/inference_preprocessor.py new file mode 100644 index 0000000..4fb11d7 --- /dev/null +++ b/template/steps/inference_preprocessor.py @@ -0,0 +1,53 @@ +# Apache Software License 2.0 +# +# Copyright (c) ZenML GmbH 2023. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import pandas as pd +from sklearn.pipeline import Pipeline +from typing_extensions import Annotated + +from zenml import step + + +@step +def inference_preprocessor( + dataset_inf: pd.DataFrame, + preprocess_pipeline: Pipeline, + target: str, +) -> Annotated[pd.DataFrame, "inference_dataset"]: + """Data preprocessor step. + + This is an example of a data processor step that prepares the data so that + it is suitable for model inference. It takes in a dataset as an input step + artifact and performs any necessary preprocessing steps based on pretrained + preprocessing pipeline. + + Args: + dataset_inf: The inference dataset. + preprocess_pipeline: Pretrained `Pipeline` to process dataset. + target: Name of target columns in dataset. + + Returns: + The processed dataframe: dataset_inf. + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # artificially adding `target` column to avoid Pipeline issues + dataset_inf[target] = pd.Series([1] * dataset_inf.shape[0]) + dataset_inf = preprocess_pipeline.transform(dataset_inf) + dataset_inf.drop(columns=["target"], inplace=True) + ### YOUR CODE ENDS HERE ### + + return dataset_inf From ff02dbfa885f9794fc34e533c46cf7a2f5dedde3 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 1 Dec 2023 17:01:39 +0100 Subject: [PATCH 03/48] Latest --- .../{training_config.yaml => training.yaml} | 0 template/pipelines/__init__.py | 2 +- template/pipelines/feature_engineering.py | 11 +- template/pipelines/inference.py | 2 +- template/pipelines/training.py | 53 ++++--- template/run.ipynb | 145 +++++++++++------- template/run.py | 21 ++- template/steps/data_loader.py | 19 ++- template/steps/data_preprocessor.py | 15 +- template/steps/model_evaluator.py | 12 +- template/steps/model_trainer.py | 37 +---- tests/test_starter_template.py | 2 +- 12 files changed, 185 insertions(+), 134 deletions(-) rename template/configs/{training_config.yaml => training.yaml} (100%) diff --git a/template/configs/training_config.yaml b/template/configs/training.yaml similarity index 100% rename from template/configs/training_config.yaml rename to template/configs/training.yaml diff --git a/template/pipelines/__init__.py b/template/pipelines/__init__.py index b91d3ee..02779f9 100644 --- a/template/pipelines/__init__.py +++ b/template/pipelines/__init__.py @@ -2,4 +2,4 @@ from feature_engineering import _feature_engineering from training import _model_training -from inference import _inference \ No newline at end of file +from inference import _inference diff --git a/template/pipelines/feature_engineering.py b/template/pipelines/feature_engineering.py index 5043aa8..759d240 100644 --- a/template/pipelines/feature_engineering.py +++ b/template/pipelines/feature_engineering.py @@ -22,24 +22,25 @@ def _feature_engineering( drop_na: Optional[bool] = None, normalize: Optional[bool] = None, drop_columns: Optional[List[str]] = None, + target: Optional[str] = "target", ): """ - Model training pipeline. + Feature engineering pipeline. This is a pipeline that loads the data, processes it and splits - it into train and test sets, then search for best hyperparameters, - trains and evaluates a model. + it into train and test sets. Args: test_size: Size of holdout set for training 0.0..1.0 drop_na: If `True` NA values will be removed from dataset normalize: If `True` dataset will be normalized with MinMaxScaler drop_columns: List of columns to drop from dataset + target: Name of target column in dataset """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. - raw_data, target, _ = data_loader(random_state=random.randint(0,100)) + raw_data = data_loader(random_state=random.randint(0, 100), target=target) dataset_trn, dataset_tst = data_splitter( dataset=raw_data, test_size=test_size, @@ -50,4 +51,6 @@ def _feature_engineering( drop_na=drop_na, normalize=normalize, drop_columns=drop_columns, + target=target, ) + return dataset_trn, dataset_tst diff --git a/template/pipelines/inference.py b/template/pipelines/inference.py index 428b878..13dabe2 100644 --- a/template/pipelines/inference.py +++ b/template/pipelines/inference.py @@ -39,7 +39,7 @@ def _inference( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. - raw_data, target, _ = data_loader(random_state=random.randint(0,100)) + raw_data, target, _ = data_loader(random_state=random.randint(0, 100)) dataset_trn, dataset_tst = data_splitter( dataset=raw_data, test_size=test_size, diff --git a/template/pipelines/training.py b/template/pipelines/training.py index 4e94d66..e90b094 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -1,27 +1,31 @@ # {% include 'template/license_header' %} -import random -from typing import Optional, List +from typing import Optional from zenml import pipeline from zenml.logger import get_logger +from uuid import UUID -from steps import ( - data_loader, - data_splitter, - data_preprocessor, + +from steps import model_trainer, model_evaluator + +from pipelines import ( + _feature_engineering, ) +from zenml import ExternalArtifact +from zenml.client import Client + logger = get_logger(__name__) @pipeline def _training( - test_size: float = 0.2, - drop_na: Optional[bool] = None, - normalize: Optional[bool] = None, - drop_columns: Optional[List[str]] = None, + train_dataset_id: Optional[UUID] = None, + test_dataset_id: Optional[UUID] = None, + min_train_accuracy: float = 0.0, + min_test_accuracy: float = 0.0, ): """ Model training pipeline. @@ -39,15 +43,28 @@ def _training( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. - raw_data, target, _ = data_loader(random_state=random.randint(0,100)) - dataset_trn, dataset_tst = data_splitter( - dataset=raw_data, - test_size=test_size, + + # Execute Feature Engineering Pipeline + if train_dataset_id is None or test_dataset_id is None: + dataset_trn, dataset_tst = _feature_engineering() + else: + dataset_trn = ExternalArtifact(id=train_dataset_id) + dataset_tst = ExternalArtifact(id=test_dataset_id) + + # Use the metadata of the dataset to understand what the target is + client = Client() + target = client.get_artifact("dataset_trn").run_metadata["target"].value + + model = model_trainer( + dataset_trn=dataset_trn, + target=target, ) - dataset_trn, dataset_tst, _ = data_preprocessor( + + model_evaluator( + model=model, dataset_trn=dataset_trn, dataset_tst=dataset_tst, - drop_na=drop_na, - normalize=normalize, - drop_columns=drop_columns, + min_train_accuracy=min_train_accuracy, + min_test_accuracy=min_test_accuracy, + target=target, ) diff --git a/template/run.ipynb b/template/run.ipynb index 48d9165..04723ad 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -1,15 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "a681b91b", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -r requirements.txt" - ] - }, { "cell_type": "code", "execution_count": null, @@ -22,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -30,17 +20,15 @@ "# Do the imports at the top\n", "\n", "import random\n", - "import numpy as np\n", - "from typing_extensions import Annotated\n", - "from zenml import step\n", + "from zenml import ExternalArtifact, pipeline \n", + "from zenml.client import Client\n", "from zenml.logger import get_logger\n", + "from uuid import UUID\n", "\n", "import os\n", "from typing import Optional, List\n", - "from datetime import datetime as dt\n", "\n", "from zenml import pipeline\n", - "from zenml import ModelVersion\n", "\n", "from steps import (\n", " data_loader,\n", @@ -52,27 +40,43 @@ " inference_preprocessor\n", ")\n", "\n", - "logger = get_logger(__name__)" + "logger = get_logger(__name__)\n", + "\n", + "client = Client()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "b50a9537", "metadata": {}, "outputs": [], "source": [ - "@pipeline(enable_cache=False)\n", + "@pipeline\n", "def _feature_engineering(\n", " test_size: float = 0.2,\n", " drop_na: Optional[bool] = None,\n", " normalize: Optional[bool] = None,\n", " drop_columns: Optional[List[str]] = None,\n", + " target: Optional[str] = \"target\",\n", "):\n", + " \"\"\"\n", + " Feature engineering pipeline.\n", + "\n", + " This is a pipeline that loads the data, processes it and splits\n", + " it into train and test sets.\n", + "\n", + " Args:\n", + " test_size: Size of holdout set for training 0.0..1.0\n", + " drop_na: If `True` NA values will be removed from dataset\n", + " normalize: If `True` dataset will be normalized with MinMaxScaler\n", + " drop_columns: List of columns to drop from dataset\n", + " target: Name of target column in dataset\n", + " \"\"\"\n", " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", " # Link all the steps together by calling them and passing the output\n", " # of one step as the input of the next step.\n", - " raw_data, target, _ = data_loader(random_state=random.randint(0,100))\n", + " raw_data = data_loader(random_state=random.randint(0, 100), target=target)\n", " dataset_trn, dataset_tst = data_splitter(\n", " dataset=raw_data,\n", " test_size=test_size,\n", @@ -83,12 +87,15 @@ " drop_na=drop_na,\n", " normalize=normalize,\n", " drop_columns=drop_columns,\n", - " )" + " target=target,\n", + " )\n", + " \n", + " return dataset_trn, dataset_tst" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "bc5feef4-7016-420e-9af9-2e87ff666f74", "metadata": {}, "outputs": [], @@ -100,46 +107,67 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "75cf3740-b2d8-4c4b-b91b-dc1637000880", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36m_feature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m9\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36m_feature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.257s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m9.532s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m12.232s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36m_feature_engineering-2023_11_30-10_09_12_105336\u001b[1;35m has finished in \u001b[0m\u001b[1;36m35.624s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/31df4e45-78ee-4712-874a-482a705ce545/runs/059cb308-1c17-4d65-9939-60af42d62179/dag\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "latest_run = fe_p_configured()" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "latest_run" + "@pipeline\n", + "def _training(\n", + " train_dataset_id: Optional[UUID] = None,\n", + " test_dataset_id: Optional[UUID] = None,\n", + " min_train_accuracy: float = 0.0,\n", + " min_test_accuracy: float = 0.0,\n", + "):\n", + " \"\"\"\n", + " Model training pipeline.\n", + "\n", + " This is a pipeline that loads the data, processes it and splits\n", + " it into train and test sets, then search for best hyperparameters,\n", + " trains and evaluates a model.\n", + "\n", + " Args:\n", + " test_size: Size of holdout set for training 0.0..1.0\n", + " drop_na: If `True` NA values will be removed from dataset\n", + " normalize: If `True` dataset will be normalized with MinMaxScaler\n", + " drop_columns: List of columns to drop from dataset\n", + " \"\"\"\n", + " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", + " # Link all the steps together by calling them and passing the output\n", + " # of one step as the input of the next step.\n", + " \n", + " # Execute Feature Engineering Pipeline\n", + " if train_dataset_id is None or test_dataset_id is None:\n", + " dataset_trn, dataset_tst = _feature_engineering()\n", + " else:\n", + " dataset_trn = ExternalArtifact(id=train_dataset_id)\n", + " dataset_tst = ExternalArtifact(id=test_dataset_id)\n", + "\n", + " # Use the dataset to fetch the target\n", + " target = client.get_artifact(\"dataset_trn\").run_metadata['target'].value\n", + " \n", + " model = model_trainer(\n", + " dataset_trn=dataset_trn,\n", + " target=target,\n", + " )\n", + "\n", + " model_evaluator(\n", + " model=model,\n", + " dataset_trn=dataset_trn,\n", + " dataset_tst=dataset_tst,\n", + " min_train_accuracy=min_train_accuracy,\n", + " min_test_accuracy=min_test_accuracy,\n", + " target=target,\n", + " )\n" ] }, { @@ -147,7 +175,20 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "pipeline_args = {}\n", + "# pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"training.yaml\")\n", + "fe_t_configured = _training.with_options(**pipeline_args)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fe_t_configured()" + ] }, { "cell_type": "code", diff --git a/template/run.py b/template/run.py index fdd603d..513d5b9 100644 --- a/template/run.py +++ b/template/run.py @@ -20,6 +20,7 @@ logger = get_logger(__name__) + @click.command( help=""" ZenML NLP project CLI v0.0.1. @@ -182,8 +183,8 @@ def main( # all steps in the pipeline in the correct order using the orchestrator # stack component that is configured in your active ZenML stack. config_folder = os.path.join( - os.path.dirname(os.path.realpath(__file__)), - "configs", + os.path.dirname(os.path.realpath(__file__)), + "configs", ) model_version = ModelVersion( name=zenml_model_name, @@ -202,7 +203,9 @@ def main( # Execute Feature Engineering Pipeline if feature_pipeline: pipeline_args["model_version"] = model_version - pipeline_args["config_path"] = os.path.join(config_folder, "feature_engineering_config.yaml") + pipeline_args["config_path"] = os.path.join( + config_folder, "feature_engineering_config.yaml" + ) run_args_feature = { "max_seq_length": max_seq_length, } @@ -216,7 +219,9 @@ def main( # Execute Training Pipeline if training_pipeline: - pipeline_args["config_path"] = os.path.join(config_folder, "trainer_config.yaml") + pipeline_args["config_path"] = os.path.join( + config_folder, "trainer_config.yaml" + ) run_args_train = { "num_epochs": num_epochs, @@ -263,7 +268,9 @@ def main( name=zenml_model_name, version=ModelStages.LATEST, ) - pipeline_args["config_path"] = os.path.join(config_folder, "promoting_config.yaml") + pipeline_args["config_path"] = os.path.join( + config_folder, "promoting_config.yaml" + ) pipeline_args["model_version"] = model_version @@ -276,7 +283,9 @@ def main( logger.info("Promoting pipeline finished successfully!") if deploying_pipeline: - pipeline_args["config_path"] = os.path.join(config_folder, "deploying_config.yaml") + pipeline_args["config_path"] = os.path.join( + config_folder, "deploying_config.yaml" + ) # Deploying pipeline has new ZenML model config model_version = ModelVersion( diff --git a/template/steps/data_loader.py b/template/steps/data_loader.py index 8b8de4d..3d56e2e 100644 --- a/template/steps/data_loader.py +++ b/template/steps/data_loader.py @@ -1,24 +1,19 @@ # {% include 'template/license_header' %} -from typing import Tuple - import pandas as pd from sklearn.datasets import load_breast_cancer from typing_extensions import Annotated from zenml import step from zenml.logger import get_logger +from zenml import log_artifact_metadata logger = get_logger(__name__) @step def data_loader( - random_state: int, is_inference: bool = False -) -> Tuple[ - Annotated[pd.DataFrame, "dataset"], - Annotated[str, "target"], - Annotated[int, "random_state"], -]: + random_state: int, is_inference: bool = False, target: str = "target" +) -> Annotated[pd.DataFrame, "dataset"]: """Dataset reader step. This is an example of a dataset reader step that load Breast Cancer dataset. @@ -34,6 +29,7 @@ def data_loader( is_inference: If `True` subset will be returned and target column will be removed from dataset. random_state: Random state for sampling + target: Name of target columns in dataset. Returns: The dataset artifact as Pandas DataFrame and name of target column. @@ -41,7 +37,6 @@ def data_loader( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### dataset = load_breast_cancer(as_frame=True) inference_size = int(len(dataset.target) * 0.05) - target = "target" dataset: pd.DataFrame = dataset.frame inference_subset = dataset.sample(inference_size, random_state=random_state) if is_inference: @@ -51,5 +46,9 @@ def data_loader( dataset.drop(inference_subset.index, inplace=True) dataset.reset_index(drop=True, inplace=True) logger.info(f"Dataset with {len(dataset)} records loaded!") + + # Recording metadata for this dataset + log_artifact_metadata(metadata={"random_state": random_state, target: target}) + ### YOUR CODE ENDS HERE ### - return dataset, target, random_state + return dataset diff --git a/template/steps/data_preprocessor.py b/template/steps/data_preprocessor.py index 2572c6f..42af03a 100644 --- a/template/steps/data_preprocessor.py +++ b/template/steps/data_preprocessor.py @@ -7,7 +7,7 @@ from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler from utils.preprocess import ColumnsDropper, DataFrameCaster, NADropper -from zenml import step +from zenml import step, log_artifact_metadata @step @@ -17,6 +17,7 @@ def data_preprocessor( drop_na: Optional[bool] = None, normalize: Optional[bool] = None, drop_columns: Optional[List[str]] = None, + target: Optional[str] = "target", ) -> Tuple[ Annotated[pd.DataFrame, "dataset_trn"], Annotated[pd.DataFrame, "dataset_tst"], @@ -62,6 +63,16 @@ def data_preprocessor( preprocess_pipeline.steps.append(("cast", DataFrameCaster(dataset_trn.columns))) dataset_trn = preprocess_pipeline.fit_transform(dataset_trn) dataset_tst = preprocess_pipeline.transform(dataset_tst) - ### YOUR CODE ENDS HERE ### + # Log metadata of target to both datasets + log_artifact_metadata( + artifact_name="dataset_trn", + metadata={"target": target}, + ) + log_artifact_metadata( + artifact_name="dataset_tst", + metadata={"target": target}, + ) + + ### YOUR CODE ENDS HERE ### return dataset_trn, dataset_tst, preprocess_pipeline diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index c7640c2..c5f88cd 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -2,7 +2,7 @@ import pandas as pd from sklearn.base import ClassifierMixin -from zenml import step +from zenml import step, log_artifact_metadata from zenml.logger import get_logger logger = get_logger(__name__) @@ -16,7 +16,6 @@ def model_evaluator( target: str, min_train_accuracy: float = 0.0, min_test_accuracy: float = 0.0, - fail_on_accuracy_quality_gates: bool = False, ) -> None: """Evaluate a trained model. @@ -77,13 +76,12 @@ def model_evaluator( messages.append( f"Test accuracy {tst_acc*100:.2f}% is below {min_test_accuracy*100:.2f}% !" ) - if fail_on_accuracy_quality_gates and messages: - raise RuntimeError( - "Model performance did not meet the minimum criteria:\n" - + "\n".join(messages) - ) else: for message in messages: logger.warning(message) + log_artifact_metadata( + metadata={"train_accuracy": trn_acc, "test_accuracy": tst_acc}, + artifact_name="model", + ) ### YOUR CODE ENDS HERE ### diff --git a/template/steps/model_trainer.py b/template/steps/model_trainer.py index 1718640..62f5c5b 100644 --- a/template/steps/model_trainer.py +++ b/template/steps/model_trainer.py @@ -3,6 +3,7 @@ from typing_extensions import Annotated import pandas as pd +from sklearn.tree import DecisionTreeClassifier from sklearn.base import ClassifierMixin from zenml import ArtifactConfig, log_artifact_metadata, step from zenml.client import Client @@ -14,12 +15,8 @@ @step def model_trainer( dataset_trn: pd.DataFrame, - model: ClassifierMixin, target: str, - name: str, -) -> Annotated[ - ClassifierMixin, ArtifactConfig(name="model", is_model_artifact=True) -]: +) -> Annotated[ClassifierMixin, ArtifactConfig(name="model", is_model_artifact=True)]: """Configure and train a model on the training dataset. This is an example of a model training step that takes in a dataset artifact @@ -27,25 +24,9 @@ def model_trainer( configures and trains a model on it. The model is then returned as a step output artifact. - Model training steps should have caching disabled if they are not - deterministic (i.e. if the model training involve some random processes - like initializing weights or shuffling data that are not controlled by - setting a fixed random seed). This example step ensures the outcome is - deterministic by initializing the model with a fixed random seed. - - This step is parameterized to configure the step independently of the step code, - before running it in a pipeline. In this example, the step can be configured - to use a different model, change the random seed, or pass different - hyperparameters to the model constructor. See the documentation for more - information: - - https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines - Args: dataset_trn: The preprocessed train dataset. - model: The model instance to train. - target: Name of target columns in dataset. - name: The name of the model. + target: The name of the target column in the dataset. Returns: The trained model artifact. @@ -54,21 +35,13 @@ def model_trainer( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Initialize the model with the hyperparameters indicated in the step # parameters and train it on the training set. + model = DecisionTreeClassifier() logger.info(f"Training model {model}...") + model.fit( dataset_trn.drop(columns=[target]), dataset_trn[target], ) - - # keep track of mlflow version for future use - model_registry = Client().active_stack.model_registry - if model_registry: - versions = model_registry.list_model_versions(name=name) - if versions: - log_artifact_metadata( - metadata={"model_registry_version": versions[-1].version}, - artifact_name="model", - ) ### YOUR CODE ENDS HERE ### return model diff --git a/tests/test_starter_template.py b/tests/test_starter_template.py index 57c3a6f..fc6f836 100644 --- a/tests/test_starter_template.py +++ b/tests/test_starter_template.py @@ -217,4 +217,4 @@ def test_custom_zenml_server_url( generate_and_run_project( tmp_path_factory=tmp_path_factory, zenml_server_url="foo", - ) \ No newline at end of file + ) From 2d566dc7b09b885fe16a8b6da2430877a8f95a2b Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 1 Dec 2023 18:02:13 +0100 Subject: [PATCH 04/48] Steps --- template/configs/inference.yaml | 13 ++++ template/configs/inference_config.yaml | 0 template/configs/training.yaml | 13 ++++ template/run.ipynb | 95 ++++++++++++++++++++++++-- template/steps/inference_predict.py | 1 + template/steps/model_evaluator.py | 2 +- 6 files changed, 119 insertions(+), 5 deletions(-) create mode 100644 template/configs/inference.yaml delete mode 100644 template/configs/inference_config.yaml diff --git a/template/configs/inference.yaml b/template/configs/inference.yaml new file mode 100644 index 0000000..e014224 --- /dev/null +++ b/template/configs/inference.yaml @@ -0,0 +1,13 @@ +# environment configuration +settings: + docker: + required_integrations: + - sklearn + +# configuration of the Model Control Plane +model_version: + name: breast_cancer_classifier + version: latest + license: Apache 2.0 + description: Classification of Breast Cancer Dataset. + tags: ["classification", "sklearn"] diff --git a/template/configs/inference_config.yaml b/template/configs/inference_config.yaml deleted file mode 100644 index e69de29..0000000 diff --git a/template/configs/training.yaml b/template/configs/training.yaml index e69de29..e014224 100644 --- a/template/configs/training.yaml +++ b/template/configs/training.yaml @@ -0,0 +1,13 @@ +# environment configuration +settings: + docker: + required_integrations: + - sklearn + +# configuration of the Model Control Plane +model_version: + name: breast_cancer_classifier + version: latest + license: Apache 2.0 + description: Classification of Breast Cancer Dataset. + tags: ["classification", "sklearn"] diff --git a/template/run.ipynb b/template/run.ipynb index 04723ad..0922ccd 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -177,7 +177,7 @@ "outputs": [], "source": [ "pipeline_args = {}\n", - "# pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"training.yaml\")\n", + "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"training.yaml\")\n", "fe_t_configured = _training.with_options(**pipeline_args)" ] }, @@ -195,21 +195,108 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from typing import Optional\n", + "\n", + "import pandas as pd\n", + "from typing_extensions import Annotated\n", + "\n", + "from zenml import get_step_context, step\n", + "from zenml.logger import get_logger\n", + "\n", + "logger = get_logger(__name__)\n", + "\n", + "\n", + "@step\n", + "def inference_predict(\n", + " dataset_inf: pd.DataFrame,\n", + ") -> Annotated[pd.Series, \"predictions\"]:\n", + " \"\"\"Predictions step.\n", + "\n", + " This is an example of a predictions step that takes the data in and returns\n", + " predicted values.\n", + "\n", + " This step is parameterized, which allows you to configure the step\n", + " independently of the step code, before running it in a pipeline.\n", + " In this example, the step can be configured to use different input data.\n", + " See the documentation for more information:\n", + "\n", + " https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines\n", + "\n", + " Args:\n", + " dataset_inf: The inference dataset.\n", + "\n", + " Returns:\n", + " The predictions as pandas series\n", + " \"\"\"\n", + " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", + " model_version = get_step_context().model_version\n", + "\n", + " print(model_version)\n", + "\n", + " # run prediction from memory\n", + " predictor = model_version.load_artifact(\"model\")\n", + " predictions = predictor.predict(dataset_inf)\n", + "\n", + " print(predictions)\n", + " predictions = pd.Series(predictions, name=\"predicted\")\n", + " ### YOUR CODE ENDS HERE ###\n", + "\n", + " return predictions\n" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "@pipeline\n", + "def _batch_inference():\n", + " \"\"\"\n", + " Model batch inference pipeline.\n", + "\n", + " This is a pipeline that loads the inference data, processes\n", + " it, analyze for data drift and run inference.\n", + " \"\"\"\n", + " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", + " # Link all the steps together by calling them and passing the output\n", + " # of one step as the input of the next step.\n", + " ########## ETL stage ##########\n", + " random_state = client.get_artifact(\"dataset\").run_metadata[\"random_state\"].value\n", + " target = client.get_artifact(\"dataset_trn\").run_metadata['target'].value\n", + " df_inference = data_loader(\n", + " random_state=random_state, is_inference=True\n", + " )\n", + " df_inference = inference_preprocessor(\n", + " dataset_inf=df_inference,\n", + " preprocess_pipeline=ExternalArtifact(name=\"preprocess_pipeline\"),\n", + " target=target,\n", + " )\n", + " inference_predict(\n", + " dataset_inf=df_inference,\n", + " )\n" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "pipeline_args = {}\n", + "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"inference.yaml\")\n", + "fe_b_configured = _batch_inference.with_options(**pipeline_args)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fe_b_configured()" + ] }, { "cell_type": "code", diff --git a/template/steps/inference_predict.py b/template/steps/inference_predict.py index 9c69a5f..773df08 100644 --- a/template/steps/inference_predict.py +++ b/template/steps/inference_predict.py @@ -55,6 +55,7 @@ def inference_predict( predictor = model_version.load_artifact("model") predictions = predictor.predict(dataset_inf) + breakpoint() predictions = pd.Series(predictions, name="predicted") ### YOUR CODE ENDS HERE ### diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index c5f88cd..66af60a 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -81,7 +81,7 @@ def model_evaluator( logger.warning(message) log_artifact_metadata( - metadata={"train_accuracy": trn_acc, "test_accuracy": tst_acc}, + metadata={"train_accuracy": float(trn_acc), "test_accuracy": float(tst_acc)}, artifact_name="model", ) ### YOUR CODE ENDS HERE ### From 8ea0c853d351eb9cdf54af4afb1967302a525ea7 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Tue, 5 Dec 2023 15:45:36 +0100 Subject: [PATCH 05/48] Works until inference pipeline --- template/run.ipynb | 73 ++++++++++++++++++++++++------- template/steps/model_evaluator.py | 6 ++- template/steps/model_trainer.py | 9 +++- 3 files changed, 69 insertions(+), 19 deletions(-) diff --git a/template/run.ipynb b/template/run.ipynb index 0922ccd..8e31e2c 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "b50a9537", "metadata": {}, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "bc5feef4-7016-420e-9af9-2e87ff666f74", "metadata": {}, "outputs": [], @@ -107,17 +107,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "75cf3740-b2d8-4c4b-b91b-dc1637000880", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36m_feature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 7)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m22\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m6.238s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.293s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.469s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36m_feature_engineering-2023_12_05-14_38_56_253977\u001b[1;35m has finished in \u001b[0m\u001b[1;36m29.443s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/8cddb86e-98e2-4aba-a562-4af0f1840f36/runs/064bea1d-b5d9-4e5a-8df7-d1cc17ffb000/dag\u001b[0m\n" + ] + } + ], "source": [ "latest_run = fe_p_configured()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -151,13 +175,9 @@ " else:\n", " dataset_trn = ExternalArtifact(id=train_dataset_id)\n", " dataset_tst = ExternalArtifact(id=test_dataset_id)\n", - "\n", - " # Use the dataset to fetch the target\n", - " target = client.get_artifact(\"dataset_trn\").run_metadata['target'].value\n", " \n", " model = model_trainer(\n", " dataset_trn=dataset_trn,\n", - " target=target,\n", " )\n", "\n", " model_evaluator(\n", @@ -166,15 +186,22 @@ " dataset_tst=dataset_tst,\n", " min_train_accuracy=min_train_accuracy,\n", " min_test_accuracy=min_test_accuracy,\n", - " target=target,\n", " )\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mlatest\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" + ] + } + ], "source": [ "pipeline_args = {}\n", "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"training.yaml\")\n", @@ -183,9 +210,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'target' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 8\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m fe_t_configured()\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/new/pipelines/pipeline.py:1438\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[39mif\u001b[39;00m Pipeline\u001b[39m.\u001b[39mACTIVE_PIPELINE:\n\u001b[1;32m 1430\u001b[0m \u001b[39m# Calling a pipeline inside a pipeline, we return the potential\u001b[39;00m\n\u001b[1;32m 1431\u001b[0m \u001b[39m# outputs of the entrypoint function\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1434\u001b[0m \u001b[39m# and instead applies the configuration of the previously active\u001b[39;00m\n\u001b[1;32m 1435\u001b[0m \u001b[39m# pipeline. Is this what we want?\u001b[39;00m\n\u001b[1;32m 1436\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mentrypoint(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m-> 1438\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprepare(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1439\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run_args)\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/new/pipelines/pipeline.py:440\u001b[0m, in \u001b[0;36mPipeline.prepare\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_invocations \u001b[39m=\u001b[39m {}\n\u001b[1;32m 436\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[1;32m 437\u001b[0m \u001b[39m# Enter the context manager, so we become the active pipeline. This\u001b[39;00m\n\u001b[1;32m 438\u001b[0m \u001b[39m# means that all steps that get called while the entrypoint function\u001b[39;00m\n\u001b[1;32m 439\u001b[0m \u001b[39m# is executed will be added as invocation to this pipeline instance.\u001b[39;00m\n\u001b[0;32m--> 440\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_entrypoint(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/new/pipelines/pipeline.py:1466\u001b[0m, in \u001b[0;36mPipeline._call_entrypoint\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1460\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 1461\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mInvalid or missing inputs for pipeline entrypoint function. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1462\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mOnly JSON serializable inputs are allowed as pipeline inputs.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1463\u001b[0m ) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n\u001b[1;32m 1465\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameters \u001b[39m=\u001b[39m validated_args\n\u001b[0;32m-> 1466\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mentrypoint(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mvalidated_args)\n", + "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 8\u001b[0m line \u001b[0;36m4\n\u001b[1;32m 30\u001b[0m dataset_tst \u001b[39m=\u001b[39m ExternalArtifact(\u001b[39mid\u001b[39m\u001b[39m=\u001b[39mtest_dataset_id)\n\u001b[1;32m 32\u001b[0m model \u001b[39m=\u001b[39m model_trainer(\n\u001b[1;32m 33\u001b[0m dataset_trn\u001b[39m=\u001b[39mdataset_trn,\n\u001b[1;32m 34\u001b[0m )\n\u001b[1;32m 36\u001b[0m model_evaluator(\n\u001b[1;32m 37\u001b[0m model\u001b[39m=\u001b[39mmodel,\n\u001b[1;32m 38\u001b[0m dataset_trn\u001b[39m=\u001b[39mdataset_trn,\n\u001b[1;32m 39\u001b[0m dataset_tst\u001b[39m=\u001b[39mdataset_tst,\n\u001b[1;32m 40\u001b[0m min_train_accuracy\u001b[39m=\u001b[39mmin_train_accuracy,\n\u001b[1;32m 41\u001b[0m min_test_accuracy\u001b[39m=\u001b[39mmin_test_accuracy,\n\u001b[0;32m---> 42\u001b[0m target\u001b[39m=\u001b[39mtarget,\n\u001b[1;32m 43\u001b[0m )\n", + "\u001b[0;31mNameError\u001b[0m: name 'target' is not defined" + ] + } + ], "source": [ "fe_t_configured()" ] diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index 66af60a..383dd40 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -13,7 +13,6 @@ def model_evaluator( model: ClassifierMixin, dataset_trn: pd.DataFrame, dataset_tst: pd.DataFrame, - target: str, min_train_accuracy: float = 0.0, min_test_accuracy: float = 0.0, ) -> None: @@ -45,7 +44,6 @@ def model_evaluator( model: The pre-trained model artifact. dataset_trn: The train dataset. dataset_tst: The test dataset. - target: Name of target columns in dataset. min_train_accuracy: Minimal acceptable training accuracy value. min_test_accuracy: Minimal acceptable testing accuracy value. fail_on_accuracy_quality_gates: If `True` a `RuntimeException` is raised @@ -55,6 +53,10 @@ def model_evaluator( RuntimeError: if any of accuracies is lower than respective threshold """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # context = get_step_context() + # target = context.inputs["dataset_trn"].run_metadata['target'].value + target = "target" + # Calculate the model accuracy on the train and test set trn_acc = model.score( dataset_trn.drop(columns=[target]), diff --git a/template/steps/model_trainer.py b/template/steps/model_trainer.py index 62f5c5b..27c641f 100644 --- a/template/steps/model_trainer.py +++ b/template/steps/model_trainer.py @@ -5,7 +5,7 @@ import pandas as pd from sklearn.tree import DecisionTreeClassifier from sklearn.base import ClassifierMixin -from zenml import ArtifactConfig, log_artifact_metadata, step +from zenml import ArtifactConfig, get_step_context, step from zenml.client import Client from zenml.logger import get_logger @@ -15,7 +15,6 @@ @step def model_trainer( dataset_trn: pd.DataFrame, - target: str, ) -> Annotated[ClassifierMixin, ArtifactConfig(name="model", is_model_artifact=True)]: """Configure and train a model on the training dataset. @@ -33,6 +32,12 @@ def model_trainer( """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + + # Use the dataset to fetch the target + # context = get_step_context() + # target = context.inputs["dataset_trn"].run_metadata['target'].value + target = "target" + # Initialize the model with the hyperparameters indicated in the step # parameters and train it on the training set. model = DecisionTreeClassifier() From 16d0faa7e64b78ccb61d3a4118bfea695d350b1a Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Tue, 5 Dec 2023 15:45:42 +0100 Subject: [PATCH 06/48] Works until inference pipeline --- template/run.ipynb | 68 +++++++--------------------------------------- 1 file changed, 10 insertions(+), 58 deletions(-) diff --git a/template/run.ipynb b/template/run.ipynb index 8e31e2c..456778e 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "b50a9537", "metadata": {}, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "bc5feef4-7016-420e-9af9-2e87ff666f74", "metadata": {}, "outputs": [], @@ -107,41 +107,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "75cf3740-b2d8-4c4b-b91b-dc1637000880", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36m_feature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 7)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m22\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m6.238s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.293s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.469s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36m_feature_engineering-2023_12_05-14_38_56_253977\u001b[1;35m has finished in \u001b[0m\u001b[1;36m29.443s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/8cddb86e-98e2-4aba-a562-4af0f1840f36/runs/064bea1d-b5d9-4e5a-8df7-d1cc17ffb000/dag\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "latest_run = fe_p_configured()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -191,17 +167,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mlatest\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "pipeline_args = {}\n", "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"training.yaml\")\n", @@ -210,25 +178,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'target' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 8\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m fe_t_configured()\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/new/pipelines/pipeline.py:1438\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1429\u001b[0m \u001b[39mif\u001b[39;00m Pipeline\u001b[39m.\u001b[39mACTIVE_PIPELINE:\n\u001b[1;32m 1430\u001b[0m \u001b[39m# Calling a pipeline inside a pipeline, we return the potential\u001b[39;00m\n\u001b[1;32m 1431\u001b[0m \u001b[39m# outputs of the entrypoint function\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1434\u001b[0m \u001b[39m# and instead applies the configuration of the previously active\u001b[39;00m\n\u001b[1;32m 1435\u001b[0m \u001b[39m# pipeline. Is this what we want?\u001b[39;00m\n\u001b[1;32m 1436\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mentrypoint(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m-> 1438\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprepare(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1439\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run_args)\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/new/pipelines/pipeline.py:440\u001b[0m, in \u001b[0;36mPipeline.prepare\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 434\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_invocations \u001b[39m=\u001b[39m {}\n\u001b[1;32m 436\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[1;32m 437\u001b[0m \u001b[39m# Enter the context manager, so we become the active pipeline. This\u001b[39;00m\n\u001b[1;32m 438\u001b[0m \u001b[39m# means that all steps that get called while the entrypoint function\u001b[39;00m\n\u001b[1;32m 439\u001b[0m \u001b[39m# is executed will be added as invocation to this pipeline instance.\u001b[39;00m\n\u001b[0;32m--> 440\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_entrypoint(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/new/pipelines/pipeline.py:1466\u001b[0m, in \u001b[0;36mPipeline._call_entrypoint\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1460\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 1461\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mInvalid or missing inputs for pipeline entrypoint function. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1462\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mOnly JSON serializable inputs are allowed as pipeline inputs.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1463\u001b[0m ) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n\u001b[1;32m 1465\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameters \u001b[39m=\u001b[39m validated_args\n\u001b[0;32m-> 1466\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mentrypoint(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mvalidated_args)\n", - "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 8\u001b[0m line \u001b[0;36m4\n\u001b[1;32m 30\u001b[0m dataset_tst \u001b[39m=\u001b[39m ExternalArtifact(\u001b[39mid\u001b[39m\u001b[39m=\u001b[39mtest_dataset_id)\n\u001b[1;32m 32\u001b[0m model \u001b[39m=\u001b[39m model_trainer(\n\u001b[1;32m 33\u001b[0m dataset_trn\u001b[39m=\u001b[39mdataset_trn,\n\u001b[1;32m 34\u001b[0m )\n\u001b[1;32m 36\u001b[0m model_evaluator(\n\u001b[1;32m 37\u001b[0m model\u001b[39m=\u001b[39mmodel,\n\u001b[1;32m 38\u001b[0m dataset_trn\u001b[39m=\u001b[39mdataset_trn,\n\u001b[1;32m 39\u001b[0m dataset_tst\u001b[39m=\u001b[39mdataset_tst,\n\u001b[1;32m 40\u001b[0m min_train_accuracy\u001b[39m=\u001b[39mmin_train_accuracy,\n\u001b[1;32m 41\u001b[0m min_test_accuracy\u001b[39m=\u001b[39mmin_test_accuracy,\n\u001b[0;32m---> 42\u001b[0m target\u001b[39m=\u001b[39mtarget,\n\u001b[1;32m 43\u001b[0m )\n", - "\u001b[0;31mNameError\u001b[0m: name 'target' is not defined" - ] - } - ], + "outputs": [], "source": [ "fe_t_configured()" ] From 5f781bf26430ebafe081a81d949d7a528b16ae40 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Tue, 5 Dec 2023 16:47:47 +0100 Subject: [PATCH 07/48] Run py cleaned --- template/configs/inference.yaml | 2 +- template/pipelines/__init__.py | 6 +- template/pipelines/feature_engineering.py | 10 +- template/pipelines/inference.py | 55 +++-- template/pipelines/training.py | 19 +- template/run.py | 285 ++++++---------------- template/steps/__init__.py | 12 +- template/steps/data_loader.py | 3 +- template/steps/data_preprocessor.py | 4 +- template/steps/data_splitter.py | 2 +- template/steps/inference_predict.py | 2 - template/steps/inference_preprocessor.py | 1 - template/steps/model_evaluator.py | 2 +- template/steps/model_trainer.py | 12 +- 14 files changed, 133 insertions(+), 282 deletions(-) diff --git a/template/configs/inference.yaml b/template/configs/inference.yaml index e014224..661b328 100644 --- a/template/configs/inference.yaml +++ b/template/configs/inference.yaml @@ -7,7 +7,7 @@ settings: # configuration of the Model Control Plane model_version: name: breast_cancer_classifier - version: latest + version: production license: Apache 2.0 description: Classification of Breast Cancer Dataset. tags: ["classification", "sklearn"] diff --git a/template/pipelines/__init__.py b/template/pipelines/__init__.py index 02779f9..9c07547 100644 --- a/template/pipelines/__init__.py +++ b/template/pipelines/__init__.py @@ -1,5 +1,5 @@ # {% include 'template/license_header' %} -from feature_engineering import _feature_engineering -from training import _model_training -from inference import _inference +from .feature_engineering import _feature_engineering +from .inference import _inference +from .training import _training diff --git a/template/pipelines/feature_engineering.py b/template/pipelines/feature_engineering.py index 759d240..5465382 100644 --- a/template/pipelines/feature_engineering.py +++ b/template/pipelines/feature_engineering.py @@ -1,17 +1,15 @@ # {% include 'template/license_header' %} import random -from typing import Optional, List - -from zenml import pipeline -from zenml.logger import get_logger +from typing import List, Optional from steps import ( data_loader, - data_splitter, data_preprocessor, + data_splitter, ) - +from zenml import pipeline +from zenml.logger import get_logger logger = get_logger(__name__) diff --git a/template/pipelines/inference.py b/template/pipelines/inference.py index 13dabe2..62d5823 100644 --- a/template/pipelines/inference.py +++ b/template/pipelines/inference.py @@ -1,17 +1,12 @@ # {% include 'template/license_header' %} -import random -from typing import Optional, List - -from zenml import pipeline -from zenml.logger import get_logger +from typing import List, Optional from steps import ( data_loader, - data_splitter, - data_preprocessor, ) - +from zenml import pipeline +from zenml.logger import get_logger logger = get_logger(__name__) @@ -39,15 +34,39 @@ def _inference( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. - raw_data, target, _ = data_loader(random_state=random.randint(0, 100)) - dataset_trn, dataset_tst = data_splitter( - dataset=raw_data, - test_size=test_size, + random_state = client.get_artifact("dataset").run_metadata["random_state"].value + target = client.get_artifact("dataset_trn").run_metadata["target"].value + df_inference = data_loader(random_state=random_state, is_inference=True) + df_inference = inference_preprocessor( + dataset_inf=df_inference, + preprocess_pipeline=ExternalArtifact(name="preprocess_pipeline"), + target=target, + ) + inference_predict( + dataset_inf=df_inference, + ) + + +@pipeline +def _batch_inference(): + """ + Model batch inference pipeline. + + This is a pipeline that loads the inference data, processes + it, analyze for data drift and run inference. + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + # Link all the steps together by calling them and passing the output + # of one step as the input of the next step. + ########## ETL stage ########## + random_state = client.get_artifact("dataset").run_metadata["random_state"].value + target = client.get_artifact("dataset_trn").run_metadata["target"].value + df_inference = data_loader(random_state=random_state, is_inference=True) + df_inference = inference_preprocessor( + dataset_inf=df_inference, + preprocess_pipeline=ExternalArtifact(name="preprocess_pipeline"), + target=target, ) - dataset_trn, dataset_tst, _ = data_preprocessor( - dataset_trn=dataset_trn, - dataset_tst=dataset_tst, - drop_na=drop_na, - normalize=normalize, - drop_columns=drop_columns, + inference_predict( + dataset_inf=df_inference, ) diff --git a/template/pipelines/training.py b/template/pipelines/training.py index e90b094..8343c3c 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -1,22 +1,16 @@ # {% include 'template/license_header' %} from typing import Optional - -from zenml import pipeline -from zenml.logger import get_logger from uuid import UUID - -from steps import model_trainer, model_evaluator +from steps import model_evaluator, model_trainer +from zenml import ExternalArtifact, pipeline +from zenml.logger import get_logger from pipelines import ( _feature_engineering, ) -from zenml import ExternalArtifact -from zenml.client import Client - - logger = get_logger(__name__) @@ -51,13 +45,8 @@ def _training( dataset_trn = ExternalArtifact(id=train_dataset_id) dataset_tst = ExternalArtifact(id=test_dataset_id) - # Use the metadata of the dataset to understand what the target is - client = Client() - target = client.get_artifact("dataset_trn").run_metadata["target"].value - model = model_trainer( dataset_trn=dataset_trn, - target=target, ) model_evaluator( @@ -66,5 +55,5 @@ def _training( dataset_tst=dataset_tst, min_train_accuracy=min_train_accuracy, min_test_accuracy=min_test_accuracy, - target=target, ) + ### END CODE HERE ### diff --git a/template/run.py b/template/run.py index 513d5b9..44f0798 100644 --- a/template/run.py +++ b/template/run.py @@ -1,112 +1,71 @@ # {% include 'templates/license_header' %} import os +from typing import Optional + import click -from typing import Any, Dict, Optional -from steps import ( - data_loader, - data_preprocessor, - data_splitter, - model_trainer, - model_evaluator, -) from pipelines import ( - _model_training, _feature_engineering, _inference, + _training, ) +from zenml.client import Client from zenml.logger import get_logger -from zenml import ModelVersion, ModelStages, Client logger = get_logger(__name__) @click.command( help=""" -ZenML NLP project CLI v0.0.1. +ZenML Starter project CLI v0.0.1. -Run the ZenML NLP project model training pipeline with various -options. +Run the ZenML starter project with basic options. Examples: - \b - # Run the pipeline with default options - python run.py - + # Run the feature engineering pipeline + python run.py --feature-pipeline + \b - # Run the pipeline without cache - python run.py --no-cache + # Run the training pipeline + python run.py --training-pipeline - \b - # Run the pipeline without NA drop and normalization, - # but dropping columns [A,B,C] and keeping 10% of dataset - # as test set. - python run.py --num-epochs 3 --train-batch-size 8 --eval-batch-size 8 + \b + # Run the training pipeline with versioned artifacts + python run.py --training-pipeline --train-dataset-version-name=1 --test-dataset-version-name=1 \b - # Run the pipeline with Quality Gate for accuracy set at 90% for train set - # and 85% for test set. If any of accuracies will be lower - pipeline will fail. - python run.py --min-train-accuracy 0.9 --min-test-accuracy 0.85 --fail-on-accuracy-quality-gates - + # Run the inference pipeline + python run.py --inference-pipeline """ ) @click.option( - "--no-cache", - is_flag=True, - default=False, - help="Disable caching for the pipeline run.", -) -@click.option( - "--num-epochs", - default=1, - type=click.INT, - help="Number of epochs to train the model for.", -) -@click.option( - "--train-batch-size", - default=8, - type=click.INT, - help="Batch size for training the model.", -) -@click.option( - "--eval-batch-size", - default=8, - type=click.INT, - help="Batch size for evaluating the model.", -) -@click.option( - "--learning-rate", - default=2e-5, - type=click.FLOAT, - help="Learning rate for training the model.", -) -@click.option( - "--weight-decay", - default=0.01, - type=click.FLOAT, - help="Weight decay for training the model.", + "--train-dataset-name", + default="dataset_trn", + type=click.STRING, + help="The name of the train dataset produced by feature engineering.", ) @click.option( - "--max-seq-length", - default=512, - type=click.INT, - help="The maximum total input sequence length after tokenization.", + "--train-dataset-version-name", + default=None, + type=click.STRING, + help="Version of the train dataset produced by feature engineering. " + "If not specified, a new version will be created.", ) @click.option( - "--dataset-name", - default="tokenized_dataset", + "--test-dataset-name", + default="dataset_tst", type=click.STRING, - help="The name of the dataset produced by feature engineering.", + help="The name of the test dataset produced by feature engineering.", ) @click.option( - "--dataset-version-name", + "--test-dataset-version-name", default=None, type=click.STRING, - help="Version of the dataset produced by feature engineering. " - "If not specified, the a new version will be used.", + help="Version of the test dataset produced by feature engineering. " + "If not specified, a new version will be created.", ) @click.option( "--feature-pipeline", @@ -121,187 +80,79 @@ help="Whether to run the pipeline that trains the model.", ) @click.option( - "--dataset-artifact-id", - default=None, - type=click.STRING, - help="Dataset artifact id to use for training. If not specified, " - "the latest version will be used.", -) -@click.option( - "--tokenizer-artifact-id", - default=None, - type=click.STRING, - help="Tokenizer artifact id to use for training. If not specified, " - "the latest version will be used.", -) -@click.option( - "--promoting-pipeline", - is_flag=True, - default=False, - help="Whether to run the pipeline that promotes the model to staging.", -) -@click.option( - "--deploying-pipeline", + "--inference-pipeline", is_flag=True, default=False, - help="Whether to run the pipeline that deploys the model to selected deployment platform.", -) -@click.option( - "--zenml-model-name", - default="distil_bert_sentiment_analysis", - type=click.STRING, - help="Name of the ZenML Model.", + help="Whether to run the pipeline that performs inference.", ) def main( - no_cache: bool = True, - num_epochs: int = 3, - train_batch_size: int = 8, - eval_batch_size: int = 8, - learning_rate: float = 2e-5, - weight_decay: float = 0.01, - max_seq_length: int = 512, - dataset_artifact_id: Optional[str] = None, - tokenizer_artifact_id: Optional[str] = None, - dataset_name: str = "tokenized_dataset", - dataset_version_name: Optional[str] = None, + train_dataset_name: str = "dataset_trn", + train_dataset_version_name: Optional[str] = None, + test_dataset_name: str = "dataset_tst", + test_dataset_version_name: Optional[str] = None, feature_pipeline: bool = False, training_pipeline: bool = False, - promoting_pipeline: bool = False, - deploying_pipeline: bool = False, - zenml_model_name: str = "distil_bert_sentiment_analysis", + inference_pipeline: bool = False, ): """Main entry point for the pipeline execution. This entrypoint is where everything comes together: * configuring pipeline with the required parameters - (some of which may come from command line arguments) + (some of which may come from command line arguments, but most + of which comes from the YAML config files) * launching the pipeline """ - - # Run a pipeline with the required parameters. This executes - # all steps in the pipeline in the correct order using the orchestrator - # stack component that is configured in your active ZenML stack. config_folder = os.path.join( os.path.dirname(os.path.realpath(__file__)), "configs", ) - model_version = ModelVersion( - name=zenml_model_name, - license="Apache 2.0", - description="Show case Model Control Plane.", - create_new_model_version=True, - delete_new_version_on_failure=True, - tags=["sentiment_analysis", "huggingface"], - ) - - pipeline_args = {} - - if no_cache: - pipeline_args["enable_cache"] = False # Execute Feature Engineering Pipeline if feature_pipeline: - pipeline_args["model_version"] = model_version + pipeline_args = {} pipeline_args["config_path"] = os.path.join( - config_folder, "feature_engineering_config.yaml" - ) - run_args_feature = { - "max_seq_length": max_seq_length, - } - pipeline_args[ - "run_name" - ] = f"sentinment_analysis_feature_engineering_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" - sentinment_analysis_feature_engineering_pipeline.with_options(**pipeline_args)( - **run_args_feature + config_folder, "feature_engineering.yaml" ) + run_args_feature = {} + _feature_engineering.with_options(**pipeline_args)(**run_args_feature) logger.info("Feature Engineering pipeline finished successfully!") # Execute Training Pipeline if training_pipeline: - pipeline_args["config_path"] = os.path.join( - config_folder, "trainer_config.yaml" - ) + pipeline_args = {} + pipeline_args["config_path"] = os.path.join(config_folder, "training.yaml") - run_args_train = { - "num_epochs": num_epochs, - "train_batch_size": train_batch_size, - "eval_batch_size": eval_batch_size, - "learning_rate": learning_rate, - "weight_decay": weight_decay, - "max_seq_length": max_seq_length, - "dataset_artifact_id": dataset_artifact_id, - "tokenizer_artifact_id": tokenizer_artifact_id, - } + run_args_train = {} - # If dataset_version_name is specified, use versioned artifacts - if dataset_version_name: + # If train_dataset_version_name is specified, use versioned artifacts + if train_dataset_version_name or test_dataset_version_name: + # However, both train and test dataset versions must be specified + assert ( + train_dataset_version_name is not None + and test_dataset_version_name is not None + ) client = Client() - tokenized_dataset_artifact = client.get_artifact( - dataset_name, dataset_version_name + train_dataset_artifact = client.get_artifact( + train_dataset_name, train_dataset_version_name ) - # base tokenizer is always the same version - # as the dataset version - tokenized_tokenizer_artifact = client.get_artifact( - "base_tokenizer", dataset_version_name + # If train dataset is specified, test dataset must be specified + test_dataset_artifact = client.get_artifact( + test_dataset_name, test_dataset_version_name ) # Use versioned artifacts - run_args_train["dataset_artifact_id"] = tokenized_dataset_artifact.id - run_args_train["tokenizer_artifact_id"] = tokenized_tokenizer_artifact.id - - pipeline_args["model_version"] = model_version + run_args_train["train_dataset_id"] = train_dataset_artifact.id + run_args_train["test_dataset_id"] = test_dataset_artifact.id - pipeline_args[ - "run_name" - ] = f"sentinment_analysis_training_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" - - sentinment_analysis_training_pipeline.with_options(**pipeline_args)( - **run_args_train - ) + _training.with_options(**pipeline_args)(**run_args_train) logger.info("Training pipeline finished successfully!") - # Execute Promoting Pipeline - if promoting_pipeline: - run_args_promoting = {} - # Promoting pipeline always check latest version - model_version = ModelVersion( - name=zenml_model_name, - version=ModelStages.LATEST, - ) - pipeline_args["config_path"] = os.path.join( - config_folder, "promoting_config.yaml" - ) - - pipeline_args["model_version"] = model_version - - pipeline_args[ - "run_name" - ] = f"sentinment_analysis_promoting_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" - sentinment_analysis_promote_pipeline.with_options(**pipeline_args)( - **run_args_promoting - ) - logger.info("Promoting pipeline finished successfully!") - - if deploying_pipeline: - pipeline_args["config_path"] = os.path.join( - config_folder, "deploying_config.yaml" - ) - - # Deploying pipeline has new ZenML model config - model_version = ModelVersion( - name=zenml_model_name, - version=ModelStages.PRODUCTION, - ) - pipeline_args["model_version"] = model_version - pipeline_args["enable_cache"] = False - run_args_deploying = {} - pipeline_args[ - "run_name" - ] = f"sentinment_analysis_deploy_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}" - sentinment_analysis_deploy_pipeline.with_options(**pipeline_args)( - **run_args_deploying - ) - logger.info("Deploying pipeline finished successfully!") + if inference_pipeline: + pipeline_args = {} + pipeline_args["config_path"] = os.path.join(config_folder, "inference.yaml") + run_args_inference = {} + _inference.with_options(**pipeline_args)(**run_args_inference) + logger.info("Inference pipeline finished successfully!") if __name__ == "__main__": diff --git a/template/steps/__init__.py b/template/steps/__init__.py index 708f1d5..ec171fe 100644 --- a/template/steps/__init__.py +++ b/template/steps/__init__.py @@ -9,15 +9,15 @@ from .data_splitter import ( data_splitter, ) +from .inference_predict import ( + inference_predict, +) +from .inference_preprocessor import ( + inference_preprocessor, +) from .model_evaluator import ( model_evaluator, ) from .model_trainer import ( model_trainer, ) -from .inference_preprocessor import ( - inference_preprocessor, -) -from .inference_predict import ( - inference_predict, -) diff --git a/template/steps/data_loader.py b/template/steps/data_loader.py index 3d56e2e..cc6df58 100644 --- a/template/steps/data_loader.py +++ b/template/steps/data_loader.py @@ -3,9 +3,8 @@ import pandas as pd from sklearn.datasets import load_breast_cancer from typing_extensions import Annotated -from zenml import step +from zenml import log_artifact_metadata, step from zenml.logger import get_logger -from zenml import log_artifact_metadata logger = get_logger(__name__) diff --git a/template/steps/data_preprocessor.py b/template/steps/data_preprocessor.py index 42af03a..34b3aeb 100644 --- a/template/steps/data_preprocessor.py +++ b/template/steps/data_preprocessor.py @@ -1,13 +1,13 @@ # {% include 'template/license_header' %} from typing import List, Optional, Tuple -from typing_extensions import Annotated import pandas as pd from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler +from typing_extensions import Annotated from utils.preprocess import ColumnsDropper, DataFrameCaster, NADropper -from zenml import step, log_artifact_metadata +from zenml import log_artifact_metadata, step @step diff --git a/template/steps/data_splitter.py b/template/steps/data_splitter.py index 121cfd2..dbab815 100644 --- a/template/steps/data_splitter.py +++ b/template/steps/data_splitter.py @@ -1,10 +1,10 @@ # {% include 'template/license_header' %} from typing import Tuple -from typing_extensions import Annotated import pandas as pd from sklearn.model_selection import train_test_split +from typing_extensions import Annotated from zenml import step diff --git a/template/steps/inference_predict.py b/template/steps/inference_predict.py index 773df08..e214b0f 100644 --- a/template/steps/inference_predict.py +++ b/template/steps/inference_predict.py @@ -15,11 +15,9 @@ # limitations under the License. # -from typing import Optional import pandas as pd from typing_extensions import Annotated - from zenml import get_step_context, step from zenml.logger import get_logger diff --git a/template/steps/inference_preprocessor.py b/template/steps/inference_preprocessor.py index 4fb11d7..c7a5ae1 100644 --- a/template/steps/inference_preprocessor.py +++ b/template/steps/inference_preprocessor.py @@ -18,7 +18,6 @@ import pandas as pd from sklearn.pipeline import Pipeline from typing_extensions import Annotated - from zenml import step diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index 383dd40..03db02b 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -2,7 +2,7 @@ import pandas as pd from sklearn.base import ClassifierMixin -from zenml import step, log_artifact_metadata +from zenml import log_artifact_metadata, step from zenml.logger import get_logger logger = get_logger(__name__) diff --git a/template/steps/model_trainer.py b/template/steps/model_trainer.py index 27c641f..e7580ca 100644 --- a/template/steps/model_trainer.py +++ b/template/steps/model_trainer.py @@ -1,12 +1,10 @@ # {% include 'template/license_header' %} -from typing_extensions import Annotated - import pandas as pd -from sklearn.tree import DecisionTreeClassifier from sklearn.base import ClassifierMixin -from zenml import ArtifactConfig, get_step_context, step -from zenml.client import Client +from sklearn.tree import DecisionTreeClassifier +from typing_extensions import Annotated +from zenml import ArtifactConfig, step from zenml.logger import get_logger logger = get_logger(__name__) @@ -32,8 +30,8 @@ def model_trainer( """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - - # Use the dataset to fetch the target + + # Use the dataset to fetch the target # context = get_step_context() # target = context.inputs["dataset_trn"].run_metadata['target'].value target = "target" From 2f7ef977e94a5e428b13d1ad9c19ca0d152f93b1 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Tue, 5 Dec 2023 17:23:22 +0100 Subject: [PATCH 08/48] Fixing --- template/configs/training.yaml | 1 - template/pipelines/inference.py | 34 ++++++------------------- template/pipelines/training.py | 6 +++-- template/steps/__init__.py | 3 +++ template/steps/model_evaluator.py | 9 +++++-- template/steps/model_promoter.py | 42 +++++++++++++++++++++++++++++++ 6 files changed, 63 insertions(+), 32 deletions(-) create mode 100644 template/steps/model_promoter.py diff --git a/template/configs/training.yaml b/template/configs/training.yaml index e014224..daa91a1 100644 --- a/template/configs/training.yaml +++ b/template/configs/training.yaml @@ -7,7 +7,6 @@ settings: # configuration of the Model Control Plane model_version: name: breast_cancer_classifier - version: latest license: Apache 2.0 description: Classification of Breast Cancer Dataset. tags: ["classification", "sklearn"] diff --git a/template/pipelines/inference.py b/template/pipelines/inference.py index 62d5823..798f8b2 100644 --- a/template/pipelines/inference.py +++ b/template/pipelines/inference.py @@ -4,8 +4,11 @@ from steps import ( data_loader, + inference_preprocessor, + inference_predict, ) -from zenml import pipeline +from zenml import pipeline, ExternalArtifact +from zenml.client import Client from zenml.logger import get_logger logger = get_logger(__name__) @@ -34,33 +37,9 @@ def _inference( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. + client = Client() random_state = client.get_artifact("dataset").run_metadata["random_state"].value - target = client.get_artifact("dataset_trn").run_metadata["target"].value - df_inference = data_loader(random_state=random_state, is_inference=True) - df_inference = inference_preprocessor( - dataset_inf=df_inference, - preprocess_pipeline=ExternalArtifact(name="preprocess_pipeline"), - target=target, - ) - inference_predict( - dataset_inf=df_inference, - ) - - -@pipeline -def _batch_inference(): - """ - Model batch inference pipeline. - - This is a pipeline that loads the inference data, processes - it, analyze for data drift and run inference. - """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Link all the steps together by calling them and passing the output - # of one step as the input of the next step. - ########## ETL stage ########## - random_state = client.get_artifact("dataset").run_metadata["random_state"].value - target = client.get_artifact("dataset_trn").run_metadata["target"].value + target = "target" df_inference = data_loader(random_state=random_state, is_inference=True) df_inference = inference_preprocessor( dataset_inf=df_inference, @@ -70,3 +49,4 @@ def _batch_inference(): inference_predict( dataset_inf=df_inference, ) + ### END CODE HERE ### diff --git a/template/pipelines/training.py b/template/pipelines/training.py index 8343c3c..20df9ef 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -3,7 +3,7 @@ from typing import Optional from uuid import UUID -from steps import model_evaluator, model_trainer +from steps import model_evaluator, model_trainer, model_promoter from zenml import ExternalArtifact, pipeline from zenml.logger import get_logger @@ -49,11 +49,13 @@ def _training( dataset_trn=dataset_trn, ) - model_evaluator( + acc = model_evaluator( model=model, dataset_trn=dataset_trn, dataset_tst=dataset_tst, min_train_accuracy=min_train_accuracy, min_test_accuracy=min_test_accuracy, ) + + model_promoter(accuracy=acc) ### END CODE HERE ### diff --git a/template/steps/__init__.py b/template/steps/__init__.py index ec171fe..2f85671 100644 --- a/template/steps/__init__.py +++ b/template/steps/__init__.py @@ -21,3 +21,6 @@ from .model_trainer import ( model_trainer, ) +from .model_promoter import ( + model_promoter, +) \ No newline at end of file diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index 03db02b..01160fe 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -2,7 +2,7 @@ import pandas as pd from sklearn.base import ClassifierMixin -from zenml import log_artifact_metadata, step +from zenml import step, log_artifact_metadata from zenml.logger import get_logger logger = get_logger(__name__) @@ -15,7 +15,7 @@ def model_evaluator( dataset_tst: pd.DataFrame, min_train_accuracy: float = 0.0, min_test_accuracy: float = 0.0, -) -> None: +) -> float: """Evaluate a trained model. This is an example of a model evaluation step that takes in a model artifact @@ -49,6 +49,9 @@ def model_evaluator( fail_on_accuracy_quality_gates: If `True` a `RuntimeException` is raised upon not meeting one of the minimal accuracy thresholds. + Retuns: + The model accuracy on the test set. + Raises: RuntimeError: if any of accuracies is lower than respective threshold """ @@ -86,4 +89,6 @@ def model_evaluator( metadata={"train_accuracy": float(trn_acc), "test_accuracy": float(tst_acc)}, artifact_name="model", ) + ### YOUR CODE ENDS HERE ### + return float(trn_acc) diff --git a/template/steps/model_promoter.py b/template/steps/model_promoter.py new file mode 100644 index 0000000..784e0cc --- /dev/null +++ b/template/steps/model_promoter.py @@ -0,0 +1,42 @@ +# {% include 'template/license_header' %} + +from zenml import get_step_context, step +from zenml.logger import get_logger + +logger = get_logger(__name__) + + +@step +def model_promoter(accuracy: float, stage: str = "production") -> bool: + """Dataset reader step. + + This is an example of a dataset reader step that load Breast Cancer dataset. + + This step is parameterized, which allows you to configure the step + independently of the step code, before running it in a pipeline. + In this example, the step can be configured with number of rows and logic + to drop target column or not. See the documentation for more information: + + https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + + Args: + accuracy: Accuracy of the model. + stage: Which stage to promote the model to. + + Returns: + Whether the model was promoted or not. + """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + if accuracy < 0.8: + logger.info( + f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model." + ) + is_promoted = False + else: + logger.info(f"Model promoted to {stage}!") + is_promoted = True + model_version = get_step_context().model_version + model_version.set_stage(stage, force=True) + + ### YOUR CODE ENDS HERE ### + return is_promoted From fd1aad60306f34f9f7eb1b47f98861bbd0023851 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Wed, 6 Dec 2023 17:04:00 +0100 Subject: [PATCH 09/48] Updated --- template/steps/inference_predict.py | 1 - 1 file changed, 1 deletion(-) diff --git a/template/steps/inference_predict.py b/template/steps/inference_predict.py index e214b0f..c29b352 100644 --- a/template/steps/inference_predict.py +++ b/template/steps/inference_predict.py @@ -53,7 +53,6 @@ def inference_predict( predictor = model_version.load_artifact("model") predictions = predictor.predict(dataset_inf) - breakpoint() predictions = pd.Series(predictions, name="predicted") ### YOUR CODE ENDS HERE ### From c8bf3e384df706ae9e57d14e5a9ae5c222361143 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 7 Dec 2023 18:13:23 +0100 Subject: [PATCH 10/48] Updated --- starter/README.md | 53 - starter/template/.dockerignore | 2 - starter/template/README.md | 113 -- starter/template/__init__.py | 2 - starter/template/pipelines/__init__.py | 7 - starter/template/pipelines/model_training.py | 54 - starter/template/requirements.txt | 2 - starter/template/run.py | 344 ---- starter/template/steps/__init__.py | 47 - starter/template/steps/data_loaders.py | 383 ---- starter/template/steps/model_trainers.py | 499 ------ ...f open_source_license %}LICENSE{% endif %} | 1 - .../__init__.py | 7 - .../model_metadata.py | 75 - .../__init__.py | 7 - .../model_metadata_materializer.py | 113 -- .../template/{{ _copier_conf.answers_file }} | 2 - template/README.md | 156 +- template/run.ipynb | 1563 ++++++++++++++--- template/steps/model_evaluator.py | 2 +- templates/globals.j2 | 14 - templates/license | 110 -- templates/license_header | 2 - 23 files changed, 1448 insertions(+), 2110 deletions(-) delete mode 100644 starter/README.md delete mode 100644 starter/template/.dockerignore delete mode 100644 starter/template/README.md delete mode 100644 starter/template/__init__.py delete mode 100644 starter/template/pipelines/__init__.py delete mode 100644 starter/template/pipelines/model_training.py delete mode 100644 starter/template/requirements.txt delete mode 100644 starter/template/run.py delete mode 100644 starter/template/steps/__init__.py delete mode 100644 starter/template/steps/data_loaders.py delete mode 100644 starter/template/steps/model_trainers.py delete mode 100644 starter/template/{% if open_source_license %}LICENSE{% endif %} delete mode 100644 starter/template/{% if use_custom_artifacts %}artifacts{% endif %}/__init__.py delete mode 100644 starter/template/{% if use_custom_artifacts %}artifacts{% endif %}/model_metadata.py delete mode 100644 starter/template/{% if use_custom_artifacts %}materializers{% endif %}/__init__.py delete mode 100644 starter/template/{% if use_custom_artifacts %}materializers{% endif %}/model_metadata_materializer.py delete mode 100644 starter/template/{{ _copier_conf.answers_file }} delete mode 100644 templates/globals.j2 delete mode 100644 templates/license delete mode 100644 templates/license_header diff --git a/starter/README.md b/starter/README.md deleted file mode 100644 index aa76203..0000000 --- a/starter/README.md +++ /dev/null @@ -1,53 +0,0 @@ -# 📜 ZenML Starter Project Template - -What would you need to get a quick understanding of the ZenML framework and -start building your own ML pipelines? The answer is a simple project template -to cover the basics of ZenML: a collection of steps and pipelines, a stack -configuration and, to top it all off, a simple but useful CLI. This is exactly -what the ZenML starter template is all about. - -This project template is a good starting point for anyone starting out with -ZenML. It showcases the following fundamental ZenML concepts in a relatable -ML context: - -* designing [ZenML pipeline steps](https://docs.zenml.io/user-guide/starter-guide#pipelines-and-steps) -in general, but also particularly useful for the following applications: - * data ingestion, data transformation and data train/test splitting - * model training and evaluation -* using [step parameterization and caching](https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions) -to design flexible and reusable steps -* using [custom data types for your artifacts and writing materializers for them](https://docs.zenml.io/user-guide/advanced-guide/artifact-management/handle-custom-data-types) -* constructing and running a [ZenML pipeline](https://docs.zenml.io/user-guide/starter-guide#pipelines-and-steps) -* accessing ZenML pipeline run artifacts in [the post-execution phase](https://docs.zenml.io/user-guide/starter-guide/fetch-runs-after-execution), -after a pipeline run has concluded -* best practices for implementing and running reproducible and reliable ML -pipelines with ZenML - -In addition to that, the entire project is implemented with the [scikit-learn](https://scikit-learn.org) -library and showcases how to use ZenML with a popular ML framework. It makes -heavy use of the tabular datasets and classification models that scikit-learn -provides, but the concepts and patterns it showcases are applicable to any -other ML framework. - -## 📃 Template Parameters - -| Parameter | Description | Default | -|-----------|-------------|---------| -| Name | The name of the person/entity holding the copyright | ZenML GmbH | -| Email | The email of the person/entity holding the copyright | info@zenml.io | -| Project Name | Short name for your project | ZenML Starter | -| Project Slug | A slugified version of the project name (automatically generated from the project name) | zenml_starter | -| Project Version | The version of your project | 0.1.0 | -| Project License | The license under which your project will be released (one of `Apache Software License 2.0`, `MIT license`, `BSD license`, `ISC license`, `GNU General Public License v3` and `Not open source`) | Apache Software License 2.0 | -| Auto-Format | Whether to automatically format and cleanup the generated code with [black](https://black.readthedocs.io/), [ruff](https://beta.ruff.rs/docs/) and [autoflake](https://github.com/PyCQA/autoflake) (yes/no). You also need to have these Python packages installed for this option to take effect. | no | -| Use ZenML Step Params | Whether to showcase using parameters for the ZenML steps in the project (yes/no). If selected, all generated ZenML pipeline steps will be parameterized. | yes | -| Use ZenML Materializers | Whether to showcase using custom data types for the ZenML artifacts in the project (yes/no). If selected, the generated code will demonstrate the use of custom artifact data types and materializers in the generated steps and pipelines. | yes | -| UCI Dataset | The name of the UCI provided scikit-learn dataset to use in the project (one of `Iris`, `Breast Cancer` and `Wine`) | Wine | -| Scikit-learn Model | The name of the scikit-learn classifier model to use in the project (one of `Logistic Regression`, `SVC`, `Linear SVC`, `Random Forest`, `KNN`, `Gaussian NB`, `Perceptron`, `SGD Classifier` and `Decision Tree`) | Logistic Regression | -| Runtime Configurable Dataset | Whether to make the dataset a configurable parameter of the data loader step and CLI (yes/no). Only has effect if the step parameters were also selected. | yes | -| Runtime Configurable Model | Whether to make the model a configurable parameter of the model trainer step and CLI (yes/no). Only has effect if the step parameters were also selected. | yes | - -## 🚀 Generate a ZenML Project - -Please see [the main README page](../README.md) for instructions on how to -generate a ZenML project from this template. diff --git a/starter/template/.dockerignore b/starter/template/.dockerignore deleted file mode 100644 index 455f4d7..0000000 --- a/starter/template/.dockerignore +++ /dev/null @@ -1,2 +0,0 @@ -.venv* -.requirements* \ No newline at end of file diff --git a/starter/template/README.md b/starter/template/README.md deleted file mode 100644 index 0d04b69..0000000 --- a/starter/template/README.md +++ /dev/null @@ -1,113 +0,0 @@ -# {{project_name}} - -This is a basic supervised learning ML project built with the -ZenML framework and its scikit-learn integration. The project trains one or more -scikit-learn classification models to make predictions on one of the tabular -classification datasets provided by the scikit-learn library. The project was -generated from the [starter ZenML project template](https://github.com/zenml-io/zenml-project-templates/tree/main/starter) -with the following optional features enabled: -{%- if use_step_params == "y" %} -- parameterized ZenML steps -- ability to control the ZenML step parameters using the CLI -{%- if configurable_dataset == "y" %} -- ability to dynamically select and use a different dataset at runtime using the -CLI and/or a data loader step parameter -{%- endif %} -{%- if configurable_model == "y" %} -- ability to dynamically select and train a different classification model at -runtime using the CLI and/or a model trainer step parameter -{%- endif %} -{%- endif %} -{%- if use_custom_artifacts == "y" %} -- using custom data types for step artifacts and implementing custom materializers -{%- endif %} - -## 👋 Introduction - -Welcome to your newly generated "{{project_name}}" project! This is -a great way to get started with ZenML. The project contains a collection of -basic ZenML steps, pipelines, stack configurations and other artifacts and -useful resources that can serve as a starting point for your journey with -ZenML. - -What to do first? You can start by giving the the project a quick run. The -project is ready to be used and can run as-is without any further code -changes! You can try it right away by installing ZenML, the scikit-learn -ZenML integration and then calling the CLI included in the project. We also -recommend that you start the ZenML UI locally to get a better sense of what -is going on under the hood: - -```bash -# Set up a Python virtual environment, if you haven't already -virtualenv .venv -source .venv/bin/activate -# Install requirements -pip install -r requirements.txt -# Start the ZenML UI locally (recommended, but optional); -# the default username is "admin" with an empty password -zenml up -# Run the pipeline included in the project -python run.py -``` - -When the pipeline is done running, you can check out the results in the ZenML -UI by following the link printed in the terminal (or you can go straight to -the [ZenML UI pipelines run page](http://127.0.0.1:8237/workspaces/default/all-runs?page=1). - -Next, you should: - -* look at the CLI help to see what you can do with the project: -```bash -python run.py --help -``` -* go back and [try out different parameters](https://github.com/zenml-io/zenml-project-templates/tree/main/starter#-template-parameters) -for your generated project. For example, you could enable generating step -parameters or custom materializers for your project, if you haven't already. -* take a look at [the project structure](#📜-project-structure) and the code -itself. The code is heavily commented and should be easy to follow. -* read the [ZenML documentation](https://docs.zenml.io) to learn more about -various ZenML concepts referenced in the code and to get a better sense of -what you can do with ZenML. -* start building your own ZenML project by modifying this code - -## 📦 What's in the box? - -The {{ project_name }} project showcases a basic ZenML model -training pipeline with all the usual components you would expect to find in -a simple machine learning project such as this one: - -- a data ingestion step that loads one of the datasets provided through -scikit-learn -- a data processing step that does some basic preprocessing (drops rows with -missing values, normalizes the data) -- a data splitting step that breaks the data into train and test sets -- a training step that trains one of the scikit-learn models on the train set -- a model evaluation step that evaluates the trained model on the train and test -sets and warns or fails the pipeline if the model performance is below a -certain threshold - -The project code is meant to be used as a template for your own projects. For -this reason, you will find a number of places in the code specifically marked -to indicate where you can add your own code: - -```python -### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### -... -### YOUR CODE ENDS HERE ### -``` - -## 📜 Project Structure - -The project loosely follows [the recommended ZenML project structure](https://docs.zenml.io/user-guide/starter-guide/follow-best-practices): - -``` -├── pipelines <- All pipelines in one place -│ ├── model_training.py <- The main (training) pipeline -├── steps <- All steps in one place -│ ├── data_loaders.py <- Data loader/processor/splitter steps -│ ├── model_trainers.py <- Model trainer/evaluator steps -├── .dockerignore -├── README.md <- This file -├── requirements.txt <- Python dependencies -└── run.py <- CLI entrypoint -``` diff --git a/starter/template/__init__.py b/starter/template/__init__.py deleted file mode 100644 index e08729f..0000000 --- a/starter/template/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -{% include 'templates/license_header' %} -__version__ = "{{ version }}" \ No newline at end of file diff --git a/starter/template/pipelines/__init__.py b/starter/template/pipelines/__init__.py deleted file mode 100644 index fa0b4e3..0000000 --- a/starter/template/pipelines/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -{% include 'templates/license_header' %} - -from pipelines.model_training import model_training_pipeline - -__all__ = [ - "model_training_pipeline", -] \ No newline at end of file diff --git a/starter/template/pipelines/model_training.py b/starter/template/pipelines/model_training.py deleted file mode 100644 index 143ba9f..0000000 --- a/starter/template/pipelines/model_training.py +++ /dev/null @@ -1,54 +0,0 @@ -{% include 'templates/license_header' %} - -from zenml.pipelines import pipeline - -@pipeline() -def model_training_pipeline( - data_loader, - data_processor, - data_splitter, - model_trainer, - model_evaluator, -): - """ - Model training pipeline recipe. - - This is a recipe for a pipeline that loads the data, processes it and - splits it into train and test sets, then trains and evaluates a model - on it. It is agnostic of the actual step implementations and just defines - how the artifacts are circulated through the steps by calling them in the - right order and passing the output of one step as the input of the next - step. - - The arguments that this function takes are instances of the steps that - are defined in the steps folder. Also note that the arguments passed to - the steps are step artifacts. If you use step parameters to configure the - steps, they must not be used here, but instead be used when the steps are - instantiated, before this function is called. - - Args: - data_loader: A data loader step instance that outputs a dataset. - data_processor: A data processor step instance that takes a dataset - as input and outputs a processed dataset. - data_splitter: A data splitter step instance that takes a dataset - as input and outputs a train and test set split. - model_trainer: A model trainer step instance that takes a train set - as input and outputs a trained model. - model_evaluator: A model evaluator step instance that takes a train/test - set split and a trained model as input and outputs model evaluation - metrics. - - """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Link all the steps together by calling them and passing the output - # of one step as the input of the next step. - dataset = data_loader() - processed_dataset = data_processor(dataset=dataset) - train_set, test_set = data_splitter(dataset=processed_dataset) - model = model_trainer(train_set=train_set) - model_evaluator( - model=model, - train_set=train_set, - test_set=test_set, - ) - ### YOUR CODE ENDS HERE ### diff --git a/starter/template/requirements.txt b/starter/template/requirements.txt deleted file mode 100644 index 9fcc501..0000000 --- a/starter/template/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -zenml[server] -scikit-learn \ No newline at end of file diff --git a/starter/template/run.py b/starter/template/run.py deleted file mode 100644 index 095b785..0000000 --- a/starter/template/run.py +++ /dev/null @@ -1,344 +0,0 @@ -{% include 'templates/license_header' %} - -import click -from typing import Any, Dict, Optional -from steps import ( - data_loader, - data_processor, - data_splitter, - model_trainer, - model_evaluator, -{%- if use_step_params %} -{%- if configurable_dataset %} - SklearnDataset, - DataLoaderStepParameters, -{%- endif %} - DataProcessorStepParameters, - DataSplitterStepParameters, - ModelTrainerStepParameters, - ModelEvaluatorStepParameters, -{%- if configurable_model %} - SklearnClassifierModel, -{%- endif %} -{%- endif %} -) -from pipelines import ( - model_training_pipeline, -) - -{% if use_step_params %} -def process_hyper_parameters(params: Optional[str] = None) -> Dict[str, Any]: - """Process hyper parameters entered by the user from the command line. - - This function is used to parse hyper parameters entered by the user in - the command line from a key-value string format (e.g. "C=0.1,max_iter=1000") - to a dictionary with the correct data types (int, float, bool). - - Args: - params: A string of comma-separated key-value pairs. - - Returns: - A dictionary of hyper parameters converted to the correct type (int, - float, bool) - """ - if not params: - return {} - try: - params = params.split(",") - params = [param.split("=") for param in params] - params = {key: value for key, value in params} - except ValueError: - raise ValueError( - "Invalid format for hyperparameters. " - "Expected a comma-separated list of key-value pairs " - "(e.g. 'C=0.1,max_iter=1000')." - ) - for key, value in params.items(): - try: - params[key] = int(value) - continue - except ValueError: - pass - try: - params[key] = float(value) - continue - except ValueError: - pass - if value.lower() == "true": - params[key] = True - continue - if value.lower() == "false": - params[key] = False - continue - return params - - -{% endif %} -@click.command(help=""" -{{ project_name }} CLI v{{ version }}. - -Run the {{ project_name }} model training pipeline with various -options. - -Examples: - - \b - # Run the pipeline with default options - python run.py - - \b - # Run the pipeline with caching disabled - python run.py --no-cache - -{%- if use_step_params and configurable_dataset %} - - \b - # Run the pipeline with a different dataset - python run.py --dataset=breast_cancer -{%- endif %} - -{%- if use_step_params and configurable_model %} - - \b - # Run the pipeline with a different model - python run.py --model=SVM -{%- endif %} - -{%- if use_step_params %} - - \b - # Run the pipeline with custom hyperparameters for the model training step - python run.py --hyperparameters="C=0.1,max_iter=1000" - - \b - # Run the pipeline with custom data splitter step parameters - python run.py --test-size=0.1 --no-stratify - - \b - # Run the pipeline with custom data processor step parameters - python run.py --drop-columns="alcohol,ash" --no-normalize - - \b - # Run the pipeline with a different random seed - python run.py --random-state=40 - - \b - # Change the model evaluation thresholds - python run.py --min-train-accuracy=0.98 --min-test-accuracy=0.98 --max-train-test-diff=0.05 --fail-on-eval-warnings -{%- endif %} -""" -) -@click.option( - "--no-cache", - is_flag=True, - default=False, - help="Disable caching for the pipeline run.", -) -{%- if use_step_params %} -{%- if configurable_dataset %} -@click.option( - "--dataset", - default="{{ sklearn_dataset_name }}", - type=click.Choice(SklearnDataset.values()), - help="The scikit-learn dataset to load.", -) -{%- endif %} -{%- if configurable_model %} -@click.option( - "--model", - default="{{ sklearn_model_name }}", - type=click.Choice(SklearnClassifierModel.values()), - help="The scikit-learn model to train.", -) -{%- endif %} -@click.option( - "--no-drop-na", - is_flag=True, - default=False, - help="Whether to skip dropping rows with missing values in the dataset.", -) -@click.option( - "--drop-columns", - default=None, - type=click.STRING, - help="Comma-separated list of columns to drop from the dataset.", -) -@click.option( - "--no-normalize", - is_flag=True, - default=False, - help="Whether to skip normalizing the dataset.", -) -@click.option( - "--test-size", - default=0.2, - type=click.FloatRange(0.0, 1.0), - help="Proportion of the dataset to include in the test split.", -) -@click.option( - "--no-shuffle", - is_flag=True, - default=False, - help="Whether to skip shuffling the data before splitting.", -) -@click.option( - "--no-stratify", - is_flag=True, - default=False, - help="Whether to skip stratifying the data before splitting.", -) -@click.option( - "--random-state", - default=42, - type=click.INT, - help="Controls the randomness during data shuffling and model training. " - "Pass an int for reproducible and cached output across multiple " - "pipeline runs.", -) -@click.option( - "--hyperparameters", - default=None, - type=click.STRING, - help="Comma-separated list of hyper-parameters to pass to the model " - "trainer (e.g. 'C=0.1,max_iter=1000').", -) -@click.option( - "--min-train-accuracy", - default=0.8, - type=click.FloatRange(0.0, 1.0), - help="Minimum training accuracy to pass to the model evaluator.", -) -@click.option( - "--min-test-accuracy", - default=0.8, - type=click.FloatRange(0.0, 1.0), - help="Minimum test accuracy to pass to the model evaluator.", -) -@click.option( - "--max-train-test-diff", - default=0.1, - type=click.FloatRange(0.0, 1.0), - help="Maximum difference between training and test accuracy to pass to " - "the model evaluator.", -) -@click.option( - "--fail-on-eval-warnings", - is_flag=True, - default=False, - help="Whether to fail the pipeline run if the model evaluation step " - "finds that the model is not accurate enough.", -) -{%- endif %} -def main( - no_cache: bool = False, -{%- if use_step_params %} -{%- if configurable_dataset %} - dataset: str = "{{ sklearn_dataset_name }}", -{%- endif %} -{%- if configurable_model %} - model: str = "{{ sklearn_model_name }}", -{%- endif %} - no_drop_na: bool = False, - drop_columns: Optional[str] = None, - no_normalize: bool = False, - test_size: float = 0.2, - no_shuffle: bool = False, - no_stratify: bool = False, - random_state: int = 42, - hyperparameters: Optional[str] = None, - min_train_accuracy: float = 0.8, - min_test_accuracy: float = 0.8, - max_train_test_diff: float = 0.1, - fail_on_eval_warnings: bool = False, -{%- endif %} -): - """Main entry point for the pipeline execution. - - This entrypoint is where everything comes together: - - * instantiating the steps and configuring them with the required - parameters (some of which may come from command line arguments) - * creating a pipeline instance that brings together all step instances - * launching the pipeline - * extracting and looking at the artifacts logged by the pipeline run - """ - - # Initialize a pipeline. This is also where we instantiate the steps and - # configure them with the required parameters. The step instances are - # then passed to the pipeline constructor. The result is a pipeline - # instance that is ready to be run. - pipeline = model_training_pipeline( -{%- if use_step_params %} -{%- if configurable_dataset %} - data_loader=data_loader( - params=DataLoaderStepParameters( - dataset=SklearnDataset(dataset), - ), - ), -{%- else %} - data_loader=data_loader(), -{%- endif %} - data_processor=data_processor( - params=DataProcessorStepParameters( - drop_na=not no_drop_na, - drop_columns=drop_columns.split(",") if drop_columns else [], - normalize=not no_normalize, - ), - ), - data_splitter=data_splitter( - params=DataSplitterStepParameters( - test_size=test_size, - shuffle=not no_shuffle, - stratify=not no_stratify, - random_state=random_state, - ), - ), - model_trainer=model_trainer( - params=ModelTrainerStepParameters( -{%- if configurable_model %} - model=SklearnClassifierModel(model), -{%- endif %} - random_state=random_state, - hyperparameters=process_hyper_parameters(hyperparameters), - ), - ), - model_evaluator=model_evaluator( - params=ModelEvaluatorStepParameters( - min_train_accuracy=min_train_accuracy, - min_test_accuracy=min_test_accuracy, - max_train_test_accuracy_difference=max_train_test_diff, - fail_on_warnings=fail_on_eval_warnings, - ), - ), -{%- else %} - data_loader=data_loader(), - data_splitter=data_splitter(), - data_processor=data_processor(), - model_trainer=model_trainer(), - model_evaluator=model_evaluator(), -{%- endif %} - ) - - pipeline_args = {} - if no_cache: - pipeline_args["enable_cache"] = False - - # Run the pipeline. This executes all steps in the pipeline in the - # correct order using the orchestrator stack component that is configured - # in your active ZenML stack. - pipeline.run(**pipeline_args) - - - # TODO: - # extract the evaluator result and show here - # Point to the dashboard URL (if running); instruct to start the dashboard - # info on how to use the CLI to show pipeline run details. - # add experiment tracker to steps (flag) - - # materialization: - # * - create a dummy class (statistics, report, visualization) and use that - # in the pipeline step and post-execution - - -if __name__ == "__main__": - main() diff --git a/starter/template/steps/__init__.py b/starter/template/steps/__init__.py deleted file mode 100644 index cc087fd..0000000 --- a/starter/template/steps/__init__.py +++ /dev/null @@ -1,47 +0,0 @@ -{% include 'templates/license_header' %} - -from steps.data_loaders import ( - data_loader, - data_processor, - data_splitter, -{%- if use_step_params %} -{%- if configurable_dataset %} - DataLoaderStepParameters, - SklearnDataset, -{%- endif %} - DataProcessorStepParameters, - DataSplitterStepParameters, -{%- endif %} -) -from steps.model_trainers import ( - model_trainer, - model_evaluator, -{%- if use_step_params %} -{%- if configurable_model %} - SklearnClassifierModel, -{%- endif %} - ModelTrainerStepParameters, - ModelEvaluatorStepParameters, -{%- endif %} -) - -__all__ = [ - "data_loader", - "data_processor", - "data_splitter", - "model_trainer", - "model_evaluator", -{%- if use_step_params %} -{%- if configurable_dataset %} - "DataLoaderStepParameters", - "SklearnDataset", -{%- endif %} - "DataProcessorStepParameters", - "DataSplitterStepParameters", -{%- if configurable_model %} - "SklearnClassifierModel", -{%- endif %} - "ModelTrainerStepParameters", - "ModelEvaluatorStepParameters", -{%- endif %} -] \ No newline at end of file diff --git a/starter/template/steps/data_loaders.py b/starter/template/steps/data_loaders.py deleted file mode 100644 index 26ac788..0000000 --- a/starter/template/steps/data_loaders.py +++ /dev/null @@ -1,383 +0,0 @@ -{% include 'templates/license_header' %} -{%- if use_step_params %} -from typing import List -{%- endif %} -import pandas as pd - -from sklearn.datasets import ( -{%- if use_step_params and configurable_dataset %} - load_wine, - load_breast_cancer, - load_iris, -{%- else %} - load_{{ sklearn_dataset_name }}, -{%- endif %} -) -from sklearn.model_selection import train_test_split -{%- if use_step_params %} -from zenml.enums import StrEnum -{%- endif %} -from zenml.steps import ( -{%- if use_step_params %} - BaseParameters, -{%- endif %} - Output, - step, -) -from zenml.logger import get_logger - -logger = get_logger(__name__) - -{% if use_step_params and configurable_dataset %} -class SklearnDataset(StrEnum): - """Built-in scikit-learn datasets.""" - wine = "wine" - iris = "iris" - breast_cancer = "breast_cancer" - -class DataLoaderStepParameters(BaseParameters): - """Parameters for the data loader step. - - This is an example of how to use step parameters to make your data loader - step configurable independently of the step code. This is useful for example - if you want to load different datasets or different versions of the same - dataset in your pipeline without having to change the step code. - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # The name of the built-in scikit-learn dataset to load. - dataset: SklearnDataset = SklearnDataset.{{ sklearn_dataset_name }} - ### YOUR CODE ENDS HERE ### - - class Config: - """Pydantic config class. - - This is used to configure the behavior of Pydantic, the library used to - parse and validate step parameters. See the documentation for more - information: - - https://pydantic-docs.helpmanual.io/usage/model_config/ - - It is recommended to explicitly forbid extra parameters here to ensure - that the step parameters are always valid. - """ - extra = "forbid" - - -@step -def data_loader( - params: DataLoaderStepParameters, -) -> pd.DataFrame: - """Data loader step. - - This is an example of a data loader step that is usually the first step - in your pipeline. It reads data from an external source like a file, - database or 3rd party library, then formats it and returns it as a step - output artifact. - - This step is parameterized using the `DataLoaderStepParameters` class, which - allows you to configure the step independently of the step code, before - running it in a pipeline. In this example, the step can be configured to - load different built-in scikit-learn datasets. See the documentation for - more information: - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Data loader steps should have caching disabled if they are not deterministic - (i.e. if they data they load from the external source can be different when - they are subsequently called, even if the step code and parameter values - don't change). - - Args: - params: Parameters for the data loader step. - - Returns: - The loaded dataset artifact. - """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Load the dataset indicated in the step parameters and format it as a - # pandas DataFrame - if params.dataset == SklearnDataset.wine: - dataset = load_wine(as_frame=True).frame - elif params.dataset == SklearnDataset.iris: - dataset = load_iris(as_frame=True).frame - elif params.dataset == SklearnDataset.breast_cancer: - dataset = load_breast_cancer(as_frame=True).frame - elif params.dataset == SklearnDataset.diabetes: - dataset = load_diabetes(as_frame=True).frame - logger.info(f"Loaded dataset {params.dataset.value}: %s", dataset.info()) - logger.info(dataset.head()) - ### YOUR CODE ENDS HERE ### - - return dataset -{% else %} -@step -def data_loader() -> Output( - dataset=pd.DataFrame, -): - """Data loader step. - - This is an example of a data loader step that is usually the first step - in your pipeline. It reads data from an external source like a file, - database or 3rd party library, then formats it and returns it as a step - output artifact. - - Data loader steps should have caching disabled if they are not deterministic - (i.e. if they data they load from the external source is different when - they are subsequently called, even if the step code doesn't change). - - As an alternative, try modelling the data source as a step parameter to make - your data loader deterministic and configurable without the need to change - the step implementation. See the documentation for more information: - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Returns: - The loaded dataset artifact. - """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Load the {{ sklearn_dataset_name }} dataset and format it as a pandas DataFrame - dataset = load_{{ sklearn_dataset_name }}(as_frame=True).frame - dataset.info() - logger.info(dataset.head()) - ### YOUR CODE ENDS HERE ### - - return dataset -{% endif %} -{% if use_step_params %} -class DataProcessorStepParameters(BaseParameters): - """Parameters for the data processor step. - - This is an example of how to use step parameters to make your data processor - step configurable independently of the step code. This is useful for example - if you want to change the way your process data in your pipeline without - having to change the step code. - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Whether to drop rows with missing values. - drop_na: bool = True - # Columns to drop from the dataset. - drop_columns: List[str] = [] - # Whether to normalize the data. - normalize: bool = True - ### YOUR CODE ENDS HERE ### - - class Config: - """Pydantic config class. - - This is used to configure the behavior of Pydantic, the library used to - parse and validate step parameters. See the documentation for more - information: - - https://pydantic-docs.helpmanual.io/usage/model_config/ - - It is recommended to explicitly forbid extra parameters here to ensure - that the step parameters are always valid. - """ - extra = "forbid" - - -@step -def data_processor( - params: DataProcessorStepParameters, - dataset: pd.DataFrame, -) -> pd.DataFrame: - """Data processor step. - - This is an example of a data processor step that prepares the data so that - it is suitable for model training. It takes in a dataset as an input step - artifact and performs any necessary preprocessing steps like cleaning, - feature engineering, feature selection, etc. It then returns the processed - dataset as a step output artifact. - - This step is parameterized using the `DataProcessorStepParameters` class, - which allows you to configure the step independently of the step code, - before running it in a pipeline. In this example, the step can be configured - to perform or skip different preprocessing steps (e.g. dropping rows with - missing values, dropping columns, normalizing the data, etc.). See the - documentation for more information: - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Args: - params: Parameters for the data processor step. - dataset: The dataset artifact to process. - - Returns: - The processed dataset artifact. - """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - if params.drop_na: - # Drop rows with missing values - dataset = dataset.dropna() - if params.drop_columns: - # Drop columns - dataset = dataset.drop(columns=params.drop_columns) - if params.normalize: - # Normalize the data - target = dataset.pop('target') - dataset = (dataset - dataset.mean()) / dataset.std() - dataset['target'] = target - ### YOUR CODE ENDS HERE ### - - return dataset - - -class DataSplitterStepParameters(BaseParameters): - """Parameters for the data splitter step. - - This is an example of how to use step parameters to make your data splitter - step configurable independently of the step code. This is useful for example - if you want to change the ratio for the data split or if you want to - control the random seed used for the split without having to change the step - code. - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # The proportion of the dataset to include in the test split. - test_size: float = 0.2 - # The random seed to use for the split. - random_state: int = 42 - # Whether to shuffle the dataset before splitting. - shuffle: bool = True - # Whether to stratify the split. - stratify: bool = True - ### YOUR CODE ENDS HERE ### - - class Config: - """Pydantic config class. - - This is used to configure the behavior of Pydantic, the library used to - parse and validate step parameters. See the documentation for more - information: - - https://pydantic-docs.helpmanual.io/usage/model_config/ - - It is recommended to explicitly forbid extra parameters here to ensure - that the step parameters are always valid. - """ - extra = "forbid" - - -@step -def data_splitter( - params: DataSplitterStepParameters, - dataset: pd.DataFrame, -) -> Output( - train_set=pd.DataFrame, - test_set=pd.DataFrame, -): - """Data splitter step. - - This is an example of a data splitter step that splits the dataset into - training and dev subsets to be used for model training and evaluation. It - takes in a dataset as an step input artifact and returns the training and - dev subsets as two separate step output artifacts. - - Data splitter steps should have a deterministic behavior, i.e. they should - use a fixed random seed and always return the same split when called with - the same input dataset. This is to ensure reproducibility of your pipeline - runs. - - This step is parameterized using the `DataSplitterStepParameters` class, - which allows you to configure the step independently of the step code, - before running it in a pipeline. In this example, the step can be configured - to use a different random seed, change the split ratio, or control whether - to shuffle or stratify the split. See the documentation for more - information: - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Args: - params: Parameters for the data splitter step. - dataset: The dataset to split. - - Returns: - The resulting training and dev subsets. - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Split the dataset into training and dev subsets - train_set, test_set = train_test_split( - dataset, - test_size=params.test_size, - shuffle=params.shuffle, - stratify=dataset['target'] if params.stratify else None, - random_state=params.random_state, - ) - ### YOUR CODE ENDS HERE ### - - return train_set, test_set -{% else %} -@step -def data_processor(dataset: pd.DataFrame) -> pd.DataFrame: - """Data processor step. - - This is an example of a data processor step that prepares the data so that - it is suitable for model training. It takes in a dataset as an step input - artifact and performs any necessary preprocessing steps like cleaning, feature - engineering, feature selection, etc. The processed dataset is then returned - as an step output artifact. - - Args: - dataset: The dataset artifact to process. - - Returns: - The processed dataset artifact. - """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Drop rows with missing values - dataset = dataset.dropna() - # Normalize the data - target = dataset.pop('target') - dataset = (dataset - dataset.mean()) / dataset.std() - dataset['target'] = target - ### YOUR CODE ENDS HERE ### - - return dataset - - -@step -def data_splitter(dataset: pd.DataFrame) -> Output( - train_set=pd.DataFrame, - test_set=pd.DataFrame, -): - """Data splitter step. - - This is an example of a data splitter step that splits the dataset into - training and dev subsets to be used for model training and evaluation. It - takes in a dataset as a step input artifact and returns the training and - dev subsets as two separate step output artifacts. - - Data splitter steps should have a deterministic behavior, i.e. they should - use a fixed random seed and always return the same split when called with - the same input dataset. This is to ensure reproducibility of your pipeline - runs. - - As an alternative to hard-coding the seed in the step code, try modelling - the random seed as a step parameter to make your data loader deterministic. - See the documentation for more information: - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Args: - dataset: The dataset to split. - - Returns: - The resulting training and dev subsets. - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Split the dataset into training and dev subsets - train_set, test_set = train_test_split( - dataset, - test_size=0.2, - shuffle=True, - stratify=dataset['target'], - random_state=42 - ) - ### YOUR CODE ENDS HERE ### - - return train_set, test_set -{%- endif %} diff --git a/starter/template/steps/model_trainers.py b/starter/template/steps/model_trainers.py deleted file mode 100644 index a8ae7b4..0000000 --- a/starter/template/steps/model_trainers.py +++ /dev/null @@ -1,499 +0,0 @@ -{% include 'templates/license_header' %} - -{%- if use_step_params %} -from typing import Any, Dict -{%- endif %} -import pandas as pd - -from sklearn.base import ClassifierMixin -{%- if use_step_params and configurable_model %} -from sklearn.linear_model import LogisticRegression -from sklearn.svm import SVC, LinearSVC -from sklearn.ensemble import RandomForestClassifier -from sklearn.neighbors import KNeighborsClassifier -from sklearn.naive_bayes import GaussianNB -from sklearn.linear_model import Perceptron -from sklearn.linear_model import SGDClassifier -from sklearn.tree import DecisionTreeClassifier -{%- elif sklearn_model_name == 'LogisticRegression' %} -from sklearn.linear_model import LogisticRegression -{%- elif sklearn_model_name == 'SVC' %} -from sklearn.svm import SVC -{%- elif sklearn_model_name == 'LinearSVC' %} -from sklearn.svm import LinearSVC -{%- elif sklearn_model_name == 'RandomForestClassifier' %} -from sklearn.ensemble import RandomForestClassifier -{%- elif sklearn_model_name == 'KNeighborsClassifier' %} -from sklearn.neighbors import KNeighborsClassifier -{%- elif sklearn_model_name == 'GaussianNB' %} -from sklearn.naive_bayes import GaussianNB -{%- elif sklearn_model_name == 'Perceptron' %} -from sklearn.linear_model import Perceptron -{%- elif sklearn_model_name == 'SGDClassifier' %} -from sklearn.linear_model import SGDClassifier -{%- elif sklearn_model_name == 'DecisionTreeClassifier' %} -from sklearn.tree import DecisionTreeClassifier -{%- endif %} -{% if use_custom_artifacts %} -from artifacts import ModelMetadata -from materializers import ModelMetadataMaterializer -{%- endif %} - -{%- if use_step_params %} -from zenml.enums import StrEnum -{%- endif %} -from zenml.logger import get_logger -from zenml.steps import ( -{%- if use_step_params %} - BaseParameters, -{%- endif %} - Output, - step, -) - -logger = get_logger(__name__) - -{% if use_step_params %} -{%- if configurable_model %} -class SklearnClassifierModel(StrEnum): - """Scikit-learn models used for classification.""" - LogisticRegression = "LogisticRegression" - SVC = "SVC" - LinearSVC = "LinearSVC" - RandomForestClassifier = "RandomForestClassifier" - KNeighborsClassifier = "KNeighborsClassifier" - GaussianNB = "GaussianNB" - Perceptron = "Perceptron" - SGDClassifier = "SGDClassifier" - DecisionTreeClassifier = "DecisionTreeClassifier" -{%- endif %} - -class ModelTrainerStepParameters(BaseParameters): - """Parameters for the model trainer step. - - This is an example of how to use step parameters to make your model trainer - step configurable independently of the step code. This is useful for example - if you want to try out different models in your pipeline without having to - change the step code. - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### -{%- if configurable_model %} - # The name of the scikit-learn classifier model to train. - model: SklearnClassifierModel = SklearnClassifierModel.{{ sklearn_model_name }} -{%- endif %} - # The random seed to use for reproducibility. - random_state: int = 42 - # The parameters to pass to the model constructor. - hyperparameters: Dict[str, Any] = {} - ### YOUR CODE ENDS HERE ### - - class Config: - """Pydantic config class. - - This is used to configure the behavior of Pydantic, the library used to - parse and validate step parameters. See the documentation for more - information: - - https://pydantic-docs.helpmanual.io/usage/model_config/ - - It is recommended to explicitly forbid extra parameters here to ensure - that the step parameters are always valid. - """ - extra = "forbid" - - -@step -def model_trainer( - params: ModelTrainerStepParameters, - train_set: pd.DataFrame, -) -> ClassifierMixin: - """Configure and train a model on the training dataset. - - This is an example of a model training step that takes in a dataset artifact - previously loaded and pre-processed by other steps in your pipeline, then - configures and trains a model on it. The model is then returned as a step - output artifact. - - Model training steps should have caching disabled if they are not - deterministic (i.e. if the model training involve some random processes - like initializing weights or shuffling data that are not controlled by - setting a fixed random seed). This example step ensures the outcome is - deterministic by initializing the model with a fixed random seed. - - This step is parameterized using the `ModelTrainerStepParameters` class, - which allows you to configure the step independently of the step code, -{%- if configurable_model %} - before running it in a pipeline. In this example, the step can be configured - to use a different model, change the random seed, or pass different - hyperparameters to the model constructor. See the documentation for more - information: -{%- else %} - before running it in a pipeline. In this example, the step can be configured - to change the random seed, or pass different hyperparameters to the model - constructor. See the documentation for more information: -{%- endif %} - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Args: - params: The parameters for the model trainer step. - train_set: The training data set artifact. - - Returns: - The trained model artifact. - """ - X_train = train_set.drop("target", axis=1) - Y_train = train_set["target"] - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Initialize the model with the hyperparameters indicated in the step - # parameters and train it on the training set. -{%- if configurable_model %} - if params.model == SklearnClassifierModel.LogisticRegression: - model = LogisticRegression( - random_state=params.random_state, - **params.hyperparameters, - ) - elif params.model == SklearnClassifierModel.SVC: - model = SVC( - random_state=params.random_state, - **params.hyperparameters, - ) - elif params.model == SklearnClassifierModel.LinearSVC: - model = LinearSVC( - random_state=params.random_state, - **params.hyperparameters, - ) - elif params.model == SklearnClassifierModel.RandomForestClassifier: - model = RandomForestClassifier( - random_state=params.random_state, - **params.hyperparameters, - ) - elif params.model == SklearnClassifierModel.KNeighborsClassifier: - model = KNeighborsClassifier(**params.hyperparameters) - elif params.model == SklearnClassifierModel.GaussianNB: - model = GaussianNB(**params.hyperparameters) - elif params.model == SklearnClassifierModel.Perceptron: - model = Perceptron( - random_state=params.random_state, - **params.hyperparameters, - ) - elif params.model == SklearnClassifierModel.SGDClassifier: - model = SGDClassifier( - random_state=params.random_state, - **params.hyperparameters - ) - elif params.model == SklearnClassifierModel.DecisionTreeClassifier: - model = DecisionTreeClassifier( - random_state=params.random_state, - **params.hyperparameters, - ) -{%- else %} -{%- if not sklearn_model_name in [ 'KNeighborsClassifier', 'GaussianNB' ] %} - model = {{ sklearn_model_name }}(random_state=42, **params.hyperparameters) -{%- else %} - model = {{ sklearn_model_name }}(**params.hyperparameters) -{%- endif %} -{%- endif %} - - logger.info(f"Training model {model}...") - model.fit(X_train, Y_train) - ### YOUR CODE ENDS HERE ### - - return model - - -class ModelEvaluatorStepParameters(BaseParameters): - """Parameters for the model evaluator step. - - This is an example of how to use step parameters to make your model - evaluator step configurable independently of the step code. This is useful - for example if you want to control the acceptable thresholds for your model - metrics in your pipeline without having to change the step code. - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # The minimum acceptable accuracy on the train set. - min_train_accuracy: float = 0.8 - # The minimum acceptable accuracy on the test set. - min_test_accuracy: float = 0.8 - # The maximum acceptable difference between train and test accuracy. - max_train_test_accuracy_difference: float = 0.1 - # Whether to raise an error and fail the pipeline step if the model - # performance does not meet the minimum criteria. - fail_on_warnings: bool = False - ### YOUR CODE ENDS HERE ### - - class Config: - """Pydantic config class. - - This is used to configure the behavior of Pydantic, the library used to - parse and validate step parameters. See the documentation for more - information: - - https://pydantic-docs.helpmanual.io/usage/model_config/ - - It is recommended to explicitly forbid extra parameters here to ensure - that the step parameters are always valid. - """ - extra = "forbid" - - -@step -{%- if use_custom_artifacts -%} -(output_materializers=ModelMetadataMaterializer) -{%- endif %} -def model_evaluator( - params: ModelEvaluatorStepParameters, - model: ClassifierMixin, - train_set: pd.DataFrame, - test_set: pd.DataFrame, -{%- if use_custom_artifacts %} -) -> ModelMetadata: -{%- else %} -) -> Output( - train_accuracy=float, - test_accuracy=float, -): -{%- endif %} - """Evaluate a trained model. - - This is an example of a model evaluation step that takes in a model artifact - previously trained by another step in your pipeline, and a training - and validation data set pair which it uses to evaluate the model's -{%- if use_custom_artifacts %} - performance. The step returns a custom type of artifact containing metadata - about the trained model. Note that using a custom data type also requires - implementing a custom materializer for it. See the `materializer` folder - or the following ZenML docs for more information about materializers: - - https://docs.zenml.io/user-guide/advanced-guide/artifact-management/handle-custom-data-types -{%- else %} - performance. The model metrics are then returned as step output artifacts - (in this case, the model accuracy on the train and test set). -{%- endif %} - - The suggested step implementation also outputs some warnings if the model - performance does not meet some minimum criteria. This is just an example of - how you can use steps to monitor your model performance and alert you if - something goes wrong. As an alternative, you can raise an exception in the - step to force the pipeline run to fail early and all subsequent steps to - be skipped. - - This step is parameterized using the `ModelEvaluatorStepParameters` class, - which allows you to configure the step independently of the step code, - before running it in a pipeline. In this example, the step can be configured - to use different values for the acceptable model performance thresholds and - to control whether the pipeline run should fail if the model performance - does not meet the minimum criteria. See the documentation for more - information: - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Args: - params: The parameters for the model evaluator step. - model: The pre-trained model artifact. - train_set: The training data set artifact. - test_set: The test data set artifact. - - Returns: -{%- if use_custom_artifacts %} - A model metadata artifact. -{%- else %} - The model accuracy on the train and test set. -{%- endif %} - """ - X_train = train_set.drop("target", axis=1) - Y_train = train_set["target"] - X_test = test_set.drop("target", axis=1) - Y_test = test_set["target"] - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Calculate the model accuracy on the train and test set - train_acc = model.score(X_train, Y_train) - logger.info(f"Train accuracy: {train_acc}") - test_acc = model.score(X_test, Y_test) - logger.info(f"Test accuracy: {test_acc}") - - messages = [] - if train_acc < params.min_train_accuracy: - messages.append( - f"Train accuracy is below {params.min_train_accuracy*100}% !" - ) - if test_acc < params.min_test_accuracy: - messages.append( - f"Test accuracy is below {params.min_test_accuracy*100}% !" - ) - if test_acc - train_acc > params.max_train_test_accuracy_difference: - messages.append( - f"Train accuracy is more than " - f"{params.max_train_test_accuracy_difference*100}% " - f"higher than test accuracy. The model is overfitting the training " - f"dataset." - ) - if params.fail_on_warnings and messages: - raise RuntimeError( - "Model performance did not meet the minimum criteria:\n" + - "\n".join(messages) - ) - else: - for message in messages: - logger.warning(message) -{% if use_custom_artifacts %} - model_metadata = ModelMetadata() - model_metadata.collect_metadata( - model = model, - train_accuracy = train_acc, - test_accuracy = test_acc, - ) - return model_metadata -{%- else %} - return train_acc, test_acc -{%- endif %} - ### YOUR CODE ENDS HERE ### - -{% else %} -@step -def model_trainer( - train_set: pd.DataFrame, -) -> ClassifierMixin: - """Configure and train a model on the training dataset. - - This is an example of a model training step that takes in a dataset artifact - previously loaded and pre-processed by other steps in your pipeline, then - configures and trains a model on it. The model is then returned as a step - output artifact. - - Model training steps should have caching disabled if they are not - deterministic (i.e. if the model training involve some random processes - like initializing weights or shuffling data that are not controlled by - setting a fixed random seed). This example step ensures the outcome is - deterministic by initializing the model with a fixed random seed. - - As an alternative, try modelling the random seed as a step parameter to make - your model training steps deterministic. Another way step parameters may be - useful here is to configure the model hyperparameters rather than - hard-coding them in the step implementation. See the documentation for more - information: - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Args: - train_set: The training data set artifact. - - Returns: - The trained model artifact. - """ - X_train = train_set.drop("target", axis=1) - Y_train = train_set["target"] - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Train a model on the training set. -{%- if not sklearn_model_name in [ 'KNeighborsClassifier', 'GaussianNB' ] %} - model = {{ sklearn_model_name }}(random_state=42) -{%- else %} - model = {{ sklearn_model_name }}() -{%- endif %} - logger.info(f"Training model {model}...") - - model.fit(X_train.to_numpy(), Y_train.to_numpy()) - ### YOUR CODE ENDS HERE ### - - return model - - -@step -{%- if use_custom_artifacts -%} -(output_materializers=ModelMetadataMaterializer) -{%- endif %} -def model_evaluator( - model: ClassifierMixin, - train_set: pd.DataFrame, - test_set: pd.DataFrame, -{%- if use_custom_artifacts %} -) -> ModelMetadata: -{%- else %} -) -> Output( - train_accuracy=float, - test_accuracy=float, -): -{%- endif %} - """Evaluate a trained model. - - This is an example of a model evaluation step that takes in a model artifact - previously trained by another step in your pipeline, and a training - and validation data set pair which it uses to evaluate the model's -{%- if use_custom_artifacts %} - performance. The step returns a custom type of artifact containing metadata - about the trained model. Note that using a custom data type also requires - implementing a custom materializer for it. See the `materializer` folder - or the following ZenML docs for more information about materializers: - - https://docs.zenml.io/user-guide/advanced-guide/artifact-management/handle-custom-data-types -{%- else %} - performance. The model metrics are then returned as step output artifacts - (in this case, the model accuracy on the train and test set). -{%- endif %} - - The suggested step implementation also outputs some warnings if the model - performance does not meet some minimum criteria. This is just an example of - how you can use steps to monitor your model performance and alert you if - something goes wrong. As an alternative, you can raise an exception in the - step to force the pipeline run to fail early and all subsequent steps to - be skipped. - - The threshold performance values used to evaluate the model are hard-coded - in this example. As an alternative, try modelling them as step parameters to - make your model evaluation steps more flexible. See the documentation for - more information: - - https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions - - Args: - model: The pre-trained model artifact. - train_set: The training data set artifact. - test_set: The test data set artifact. - - Returns: -{%- if use_custom_artifacts %} - A model metadata artifact. -{%- else %} - The model accuracy on the train and test set. -{%- endif %} - """ - X_train = train_set.drop("target", axis=1) - Y_train = train_set["target"] - X_test = test_set.drop("target", axis=1) - Y_test = test_set["target"] - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Calculate the model accuracy on the train and test set - train_acc = model.score(X_train, Y_train) - logger.info(f"Train accuracy: {train_acc}") - test_acc = model.score(X_test, Y_test) - logger.info(f"Train accuracy: {test_acc}") - - if train_acc < 0.8: - logger.warning("Train accuracy is below 80% !") - if test_acc < 0.8: - logger.warning("Test accuracy is below 80% !") - if test_acc - train_acc > 0.1: - logger.warning( - "Train accuracy is more than 10% higher than test accuracy. The " - "model is overfitting the training dataset." - ) -{%- if use_custom_artifacts %} - model_metadata = ModelMetadata() - model_metadata.collect_metadata( - model = model, - train_accuracy = train_acc, - test_accuracy = test_acc, - ) - return model_metadata -{%- else %} - return train_acc, test_acc -{%- endif %} - ### YOUR CODE ENDS HERE ### - -{%- endif %} diff --git a/starter/template/{% if open_source_license %}LICENSE{% endif %} b/starter/template/{% if open_source_license %}LICENSE{% endif %} deleted file mode 100644 index 3de332b..0000000 --- a/starter/template/{% if open_source_license %}LICENSE{% endif %} +++ /dev/null @@ -1 +0,0 @@ -{% include 'template/license' %} \ No newline at end of file diff --git a/starter/template/{% if use_custom_artifacts %}artifacts{% endif %}/__init__.py b/starter/template/{% if use_custom_artifacts %}artifacts{% endif %}/__init__.py deleted file mode 100644 index ff9f4ab..0000000 --- a/starter/template/{% if use_custom_artifacts %}artifacts{% endif %}/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -{% include 'templates/license_header' %} - -from artifacts.model_metadata import ModelMetadata - -__all__ = [ - "ModelMetadata", -] diff --git a/starter/template/{% if use_custom_artifacts %}artifacts{% endif %}/model_metadata.py b/starter/template/{% if use_custom_artifacts %}artifacts{% endif %}/model_metadata.py deleted file mode 100644 index 39d0082..0000000 --- a/starter/template/{% if use_custom_artifacts %}artifacts{% endif %}/model_metadata.py +++ /dev/null @@ -1,75 +0,0 @@ -{% include 'templates/license_header' %} -from typing import Any, Dict -from sklearn.base import ClassifierMixin - -class ModelMetadata: - """A custom artifact that stores model metadata. - - A model metadata object gathers together information that is collected - about the model being trained in a training pipeline run. This data type - is used for one of the artifacts returned by the model evaluation step. - - This is an example of a *custom artifact data type*: a type returned by - one of the pipeline steps that isn't natively supported by the ZenML - framework. Custom artifact data types are a common occurrence in ZenML, - usually encountered in one of the following circumstances: - - - you use a third party library that is not covered as a ZenML integration - and you model one or more step artifacts from the data types provided by - this library (e.g. datasets, models, data validation profiles, model - evaluation results/reports etc.) - - you need to use one of your own data types as a step artifact and it is - not one of the basic Python artifact data types supported by the ZenML - framework (e.g. str, int, float, dictionaries, lists, etc.) - - you want to extend one of the artifact data types already natively - supported by ZenML (e.g. pandas.DataFrame or sklearn.ClassifierMixin) - to customize it with your own data and/or behavior. - - In all above cases, the ZenML framework lacks one very important piece of - information: it doesn't "know" how to convert the data into a format that - can be saved in the artifact store (e.g. on a filesystem or persistent - storage service like S3 or GCS). Saving and loading artifacts from the - artifact store is something called "materialization" in ZenML terms and - you need to provide this missing information in the form of a custom - materializer - a class that implements loading/saving artifacts from/to - the artifact store. Take a look at the `materializers` folder to see how a - custom materializer is implemented for this artifact data type. - - More information about custom step artifact data types and ZenML - materializers is available in the docs: - - https://docs.zenml.io/user-guide/advanced-guide/artifact-management/handle-custom-data-types - - """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - def __init__(self) -> None: - self.metadata: Dict[str, Any] = {} - - def collect_metadata( - self, - model: ClassifierMixin, - train_accuracy: float, - test_accuracy: float, - ) -> None: - """Gathers and stores metadata about a model. - - Args: - model: trained model - train_accuracy: model accuracy measured on the train set - test_accuracy: model accuracy measured on the test set - """ - self.metadata = dict( - model_type = model.__class__.__name__, - train_accuracy = train_accuracy, - test_accuracy = test_accuracy, - ) - - def print_report(self) -> None: - """Print a user-friendly report from the model metadata.""" - print(f""" -Model type: {self.metadata.get('model_type')} -Accuracy on train set: {self.metadata.get('train_accuracy')} -Accuracy on test set: {self.metadata.get('test_accuracy')} -""") - ### YOUR CODE ENDS HERE ### diff --git a/starter/template/{% if use_custom_artifacts %}materializers{% endif %}/__init__.py b/starter/template/{% if use_custom_artifacts %}materializers{% endif %}/__init__.py deleted file mode 100644 index 07569a9..0000000 --- a/starter/template/{% if use_custom_artifacts %}materializers{% endif %}/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -{% include 'templates/license_header' %} - -from materializers.model_metadata_materializer import ModelMetadataMaterializer - -__all__ = [ - "ModelMetadataMaterializer", -] diff --git a/starter/template/{% if use_custom_artifacts %}materializers{% endif %}/model_metadata_materializer.py b/starter/template/{% if use_custom_artifacts %}materializers{% endif %}/model_metadata_materializer.py deleted file mode 100644 index 7f6e77e..0000000 --- a/starter/template/{% if use_custom_artifacts %}materializers{% endif %}/model_metadata_materializer.py +++ /dev/null @@ -1,113 +0,0 @@ -{% include 'templates/license_header' %} -import os -from typing import Type - -import yaml - -from artifacts import ModelMetadata - -from zenml.enums import ArtifactType -from zenml.io import fileio -from zenml.materializers.base_materializer import BaseMaterializer - - -class ModelMetadataMaterializer(BaseMaterializer): - """Custom materializer for the `ModelMetadata` artifact data type. - - A materializer instructs ZenML about how to store (de-materialize) - the information from an artifact data type (ModelMetadata in this example) - into the artifact store and, conversely, loading (materializing) it back - into the artifact data type. Take a look at the `artifacts` folder for - additional information about custom artifact data types. - - When using custom data types for your artifacts, you must also supply - a custom materializer class that implements two simple I/O operations: - - - saving an artifact object to the the artifact store - - loading an artifact object from the artifact store - - For both of these operations, the ZenML framework supplies a URI - (`self.uri`) identifying the location in the artifact store where the - artifact is/should be located. Implementing them means transferring - the in-memory data stored in the artifact to the provided URI and - vice-versa. ZenML puts at your disposal a series of I/O utilities capable of - universally handling these URLs in the `zenml.io.fileio`, - `zenml.utils.io_utils` and `zenml.utils.yaml_utils` Python modules. - - More information about custom step artifact data types and ZenML - materializers is available in the docs: - - https://docs.zenml.io/user-guide/advanced-guide/artifact-management/handle-custom-data-types - - """ - - # This needs to point to the artifact data type(s) associated with the - # materializer - ASSOCIATED_TYPES = (ModelMetadata,) - ASSOCIATED_ARTIFACT_TYPE = ArtifactType.STATISTICS - - def save(self, model_metadata: ModelMetadata) -> None: - """Save (de-materialize) a model metadata artifact to the artifact store. - - This operation takes the information in the artifact (`model_metadata`) - and stores it in the artifact store at the `self.uri` URI location. - - This is usually implemented in one of two ways: - - - shown here: using the `zenml.io.fileio.open()` function or one of the - `zenml.utils.yaml_utils` wrappers to write the artifact data - directly to a file in the artifact store, similar to how you would use - the standard `open()` Python I/O. - - saving the artifact to a temporary location on your local filesystem - and then copying it to the artifact store using the `zenml.io.fileio` - functions (e.g. `mkdir()`, `copy()`). This last method is used in - cases where artifact data types come from 3rd party libraries that are - not directly aware of ZenML's I/O and cannot be modified to use it. - - Args: - model_metadata: model metadata object to save to the artifact store. - """ - super().save(model_metadata) - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Dump the model metadata directly into the artifact store as a YAML file - with fileio.open(os.path.join(self.uri, 'model_metadata.yaml'), 'w') as f: - f.write(yaml.dump(model_metadata.metadata)) - ### YOUR CODE ENDS HERE ### - - def load(self, data_type: Type[ModelMetadata]) -> ModelMetadata: - """Load (materialize) a model metadata artifact from the artifact store. - - This operation takes the `self.uri` URI location in the artifact store - and loads the information present at that location in an artifact - object (`ModelMetadata`). - - This is usually implemented in one of two ways: - - - shown here: using the `zenml.io.fileio.open()` function or one of the - `zenml.utils.yaml_utils` wrappers to read the artifact data - directly from a file in the artifact store, similar to how you would use - the standard `open()` Python I/O. - - copying the artifact from the artifact store to a temporary location - on your local filesystem using the `zenml.io.fileio` functions (e.g. - `copy()`) and loading the information from the local file into the - artifact instance. This last method is used in cases where artifact data - types come from 3rd party libraries that are not directly aware of - ZenML's I/O and cannot be modified to use it. - - Args: - data_type: the artifact data type (model metadata) - - Returns: - A model metadata artifact instance materialized from the artifact - store. - """ - super().load(data_type) - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - with fileio.open(os.path.join(self.uri, 'data.txt'), 'r') as f: - model_metadata = ModelMetadata() - model_metadata.metadata = yaml.safe_load(f.read()) - ### YOUR CODE ENDS HERE ### - - return model_metadata diff --git a/starter/template/{{ _copier_conf.answers_file }} b/starter/template/{{ _copier_conf.answers_file }} deleted file mode 100644 index e0f7470..0000000 --- a/starter/template/{{ _copier_conf.answers_file }} +++ /dev/null @@ -1,2 +0,0 @@ -# Changes here will be overwritten by Copier; NEVER EDIT MANUALLY -{{ _copier_answers|to_nice_yaml }} \ No newline at end of file diff --git a/template/README.md b/template/README.md index aa76203..56ec468 100644 --- a/template/README.md +++ b/template/README.md @@ -1,53 +1,103 @@ -# 📜 ZenML Starter Project Template - -What would you need to get a quick understanding of the ZenML framework and -start building your own ML pipelines? The answer is a simple project template -to cover the basics of ZenML: a collection of steps and pipelines, a stack -configuration and, to top it all off, a simple but useful CLI. This is exactly -what the ZenML starter template is all about. - -This project template is a good starting point for anyone starting out with -ZenML. It showcases the following fundamental ZenML concepts in a relatable -ML context: - -* designing [ZenML pipeline steps](https://docs.zenml.io/user-guide/starter-guide#pipelines-and-steps) -in general, but also particularly useful for the following applications: - * data ingestion, data transformation and data train/test splitting - * model training and evaluation -* using [step parameterization and caching](https://docs.zenml.io/user-guide/starter-guide/cache-previous-executions) -to design flexible and reusable steps -* using [custom data types for your artifacts and writing materializers for them](https://docs.zenml.io/user-guide/advanced-guide/artifact-management/handle-custom-data-types) -* constructing and running a [ZenML pipeline](https://docs.zenml.io/user-guide/starter-guide#pipelines-and-steps) -* accessing ZenML pipeline run artifacts in [the post-execution phase](https://docs.zenml.io/user-guide/starter-guide/fetch-runs-after-execution), -after a pipeline run has concluded -* best practices for implementing and running reproducible and reliable ML -pipelines with ZenML - -In addition to that, the entire project is implemented with the [scikit-learn](https://scikit-learn.org) -library and showcases how to use ZenML with a popular ML framework. It makes -heavy use of the tabular datasets and classification models that scikit-learn -provides, but the concepts and patterns it showcases are applicable to any -other ML framework. - -## 📃 Template Parameters - -| Parameter | Description | Default | -|-----------|-------------|---------| -| Name | The name of the person/entity holding the copyright | ZenML GmbH | -| Email | The email of the person/entity holding the copyright | info@zenml.io | -| Project Name | Short name for your project | ZenML Starter | -| Project Slug | A slugified version of the project name (automatically generated from the project name) | zenml_starter | -| Project Version | The version of your project | 0.1.0 | -| Project License | The license under which your project will be released (one of `Apache Software License 2.0`, `MIT license`, `BSD license`, `ISC license`, `GNU General Public License v3` and `Not open source`) | Apache Software License 2.0 | -| Auto-Format | Whether to automatically format and cleanup the generated code with [black](https://black.readthedocs.io/), [ruff](https://beta.ruff.rs/docs/) and [autoflake](https://github.com/PyCQA/autoflake) (yes/no). You also need to have these Python packages installed for this option to take effect. | no | -| Use ZenML Step Params | Whether to showcase using parameters for the ZenML steps in the project (yes/no). If selected, all generated ZenML pipeline steps will be parameterized. | yes | -| Use ZenML Materializers | Whether to showcase using custom data types for the ZenML artifacts in the project (yes/no). If selected, the generated code will demonstrate the use of custom artifact data types and materializers in the generated steps and pipelines. | yes | -| UCI Dataset | The name of the UCI provided scikit-learn dataset to use in the project (one of `Iris`, `Breast Cancer` and `Wine`) | Wine | -| Scikit-learn Model | The name of the scikit-learn classifier model to use in the project (one of `Logistic Regression`, `SVC`, `Linear SVC`, `Random Forest`, `KNN`, `Gaussian NB`, `Perceptron`, `SGD Classifier` and `Decision Tree`) | Logistic Regression | -| Runtime Configurable Dataset | Whether to make the dataset a configurable parameter of the data loader step and CLI (yes/no). Only has effect if the step parameters were also selected. | yes | -| Runtime Configurable Model | Whether to make the model a configurable parameter of the model trainer step and CLI (yes/no). Only has effect if the step parameters were also selected. | yes | - -## 🚀 Generate a ZenML Project - -Please see [the main README page](../README.md) for instructions on how to -generate a ZenML project from this template. +# :running: MLOps 101 with ZenML + +Build your first MLOps pipelines with ZenML. + +## :earth_americas: Overview + +This starter project demonstrates a complete minimalist MLOps journey. It features: + +- A feature engineering pipeline that loads data and prepares it for training. +- A training pipeline that loads the preprocessed dataset and trains a model. +- A batch inference pipeline that runs predictions on the trained model with new data. + +Along the way we will also show you how to: + +- Structure your code into MLOps pipelines +- Automatically version, track, and cache data, models, and other artifacts +- Transition your ML models from development to production + +## :cloud: Run on Colab + +You can use Google Colab to see ZenML in action, no signup / installation required! + +Open In Colab + +## :computer: Run Locally + +To run locally, install ZenML and pull this quickstart: + +```shell +# Install ZenML +pip install "zenml[server]" + +# clone the ZenML repository +git clone https://github.com/zenml-io/zenml.git +cd zenml/examples/quickstart +``` + +### :arrow_forward: Run Locally +Now we're ready to start. You have two options for running the quickstart locally: + +#### Option 1 (*Recommended*) - Interactively explore the quickstart using Jupyter Notebook: +```bash +pip install notebook +jupyter notebook +# open notebooks/quickstart.ipynb +``` + +#### Option 2 - Execute the whole ML pipeline from a Python script: +```bash +# Install required zenml integrations +zenml integration install sklearn -y + +# Initialize ZenML +zenml init + +# Start the ZenServer to enable dashboard access +zenml up + +# Run the feature engineering pipeline +python run.py --feature-pipeline + +# Run the training pipeline +python run.py --training-pipeline + +# Run the training pipeline with versioned artifacts +python run.py --training-pipeline --train-dataset-version-name=1 --test-dataset-version-name=1 + +# Run the inference pipeline +python run.py --inference-pipeline +``` + +## :dart: Dashboard + +You can also take a look at our **dashboard** where you can inspect the quickstart +pipeline run and much more. Simply execute: + +```shell +# only once you've already run `zenml up` +zenml show +``` + +## :sponge: Clean up + +To clean up, simply spin down the ZenML server. + +```shell +zenml down +``` + +## :bulb: Learn More + +If you want to learn more about ZenML as a tool, then the +[:page_facing_up: **ZenML Docs**](https://docs.zenml.io/) are the perfect place +to get started. + +Already have an MLOps stack in mind? ZenML most likely has +[**:link: Integrations**](https://docs.zenml.io/stacks-and-components/component-guide) +for whatever tools you plan to use. + +Also, make sure to join our + Slack + Slack Community + to become part of the ZenML family! \ No newline at end of file diff --git a/template/run.ipynb b/template/run.ipynb index 456778e..f26233b 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -1,26 +1,452 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intro to MLOps using ZenML\n", + "\n", + "## 🌍 Overview\n", + "\n", + "This repository is a minimalistic MLOps project intended as a starting point to learn how to put ML workflows in production. It features: \n", + "\n", + "- A feature engineering pipeline that loads data and prepares it for training.\n", + "- A training pipeline that loads the preprocessed dataset and trains a model.\n", + "- A batch inference pipeline that runs predictions on the trained model with new data.\n", + "\n", + "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n", + "\n", + "\"Pipelines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run on Colab\n", + "\n", + "You can use Google Colab to see ZenML in action, no signup / installation\n", + "required!\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](\n", + "https://colab.research.google.com/github/zenml-io/zenml/blob/main/examples/quickstart/run.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 👶 Step 0. Install Requirements\n", + "\n", + "Let's install ZenML to get started. First we'll install the latest version of\n", + "ZenML as well as the `sklearn` integration of ZenML:" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "081d5616", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: zenml[server] in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (0.50.0)\n", + "Requirement already satisfied: alembic<1.9.0,>=1.8.1 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from zenml[server]) (1.8.1)\n", + "Requirement already satisfied: azure-mgmt-resource>=21.0.0 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from zenml[server]) (23.0.1)\n", + "Requirement already satisfied: bcrypt==4.0.1 in /home/htahir1/.virtualenvs/templatestarter/lib/python3.8/site-packages (from zenml[server]) (4.0.1)\n", + "Requirement already satisfied: click<8.1.4,>=8.0.1 in 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Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + } + ], + "source": [ + "!zenml integration install sklearn mlflow -y\n", + "\n", + "import IPython\n", + "IPython.Application.instance().kernel.do_shutdown(restart=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please wait for the installation to complete before running subsequent cells. At\n", + "the end of the installation, the notebook kernel will automatically restart." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Optional: If you are using ZenML Cloud, execute the following cell with your tenant URL. Otherwise ignore." ] }, { "cell_type": "code", "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "zenml_server_url = \"PLEASE_UPDATE_ME\" # in the form \"https://URL_TO_SERVER\"\n", + "\n", + "!zenml connect --url $zenml_server_url" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "081d5616", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[2;36mFound existing ZenML repository at path \u001b[0m\n", + "\u001b[2;32m'/home/htahir1/workspace/zenml_io/template-starter/template'\u001b[0m\u001b[2;36m.\u001b[0m\n", + "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", + "\u001b[2;36m/home/htahir1/workspace/zenml_io/template-starter/template.\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", + "/home/htahir1/workspace/zenml_io/template-starter/template.\n", + "\n", + "\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'default'\u001b[0mive stack to 'default'...\n", + "\u001b[2K\u001b[32m⠙\u001b[0m Setting the repository active stack to 'default'...t'...\u001b[0m\n", + "\u001b[1A\u001b[2K" + ] + } + ], + "source": [ + "# Initialize ZenML and set the default stack\n", + "!zenml init\n", + "\n", + "!zenml stack set default\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "id": "79f775f2", "metadata": {}, "outputs": [], "source": [ "# Do the imports at the top\n", - "\n", "import random\n", - "from zenml import ExternalArtifact, pipeline \n", + "from zenml import ExternalArtifact, pipeline, ModelVersion \n", "from zenml.client import Client\n", "from zenml.logger import get_logger\n", "from uuid import UUID\n", @@ -45,35 +471,346 @@ "client = Client()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 🥇 Step 1: Load your data and execute feature engineering\n", + "\n", + "We'll start off by importing our data. In this quickstart we'll be working with\n", + "[the Breast Cancer](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic) dataset\n", + "which is publicly available on the UCI Machine Learning Repository. The task is a classification\n", + "problem, to predict whether a patient is diagnosed with breast cancer or not.\n", + "\n", + "When you're getting started with a machine learning problem you'll want to do\n", + "something similar to this: import your data and get it in the right shape for\n", + "your training. ZenML mostly gets out of your way when you're writing your Python\n", + "code, as you'll see from the following cell." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sklearn.datasets import load_breast_cancer\n", + "from typing_extensions import Annotated\n", + "from zenml import step\n", + "from zenml.logger import get_logger\n", + "\n", + "logger = get_logger(__name__)\n", + "\n", + "\n", + "@step\n", + "def data_loader_simplified(\n", + " random_state: int, is_inference: bool = False, target: str = \"target\"\n", + ") -> Annotated[pd.DataFrame, \"dataset\"]: # We name the dataset \n", + " \"\"\"Dataset reader step.\"\"\"\n", + " dataset = load_breast_cancer(as_frame=True)\n", + " inference_size = int(len(dataset.target) * 0.05)\n", + " dataset: pd.DataFrame = dataset.frame\n", + " inference_subset = dataset.sample(inference_size, random_state=random_state)\n", + " if is_inference:\n", + " dataset = inference_subset\n", + " dataset.drop(columns=target, inplace=True)\n", + " else:\n", + " dataset.drop(inference_subset.index, inplace=True)\n", + " dataset.reset_index(drop=True, inplace=True)\n", + " logger.info(f\"Dataset with {len(dataset)} records loaded!\")\n", + " return dataset\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The whole function is decorated with the `@step` decorator, which\n", + "tells ZenML to track this function as a step in the pipeline. This means that\n", + "ZenML will automatically version, track, and cache the data that is produced by\n", + "this function as an `artifact`. This is a very powerful feature, as it means that you can\n", + "reproduce your data at any point in the future, even if the original data source\n", + "changes or disappears. \n", + "\n", + "Note the use of the `typing` module's `Annotated` type hint in the output of the\n", + "step. We're using this to give a name to the output of the step, which will make\n", + "it possible to access it via a keyword later on.\n", + "\n", + "You'll also notice that we have included type hints for the outputs\n", + "to the function. These are not only useful for anyone reading your code, but\n", + "help ZenML process your data in a way appropriate to the specific data types." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ZenML is built in a way that allows you to experiment with your data and build\n", + "your pipelines as you work, so if you want to call this function to see how it\n", + "works, you can just call it directly. Here we take a look at the first few rows\n", + "of your training dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.3001 0.14710 0.2419 \n", + "1 0.07864 0.0869 0.07017 0.1812 \n", + "2 0.15990 0.1974 0.12790 0.2069 \n", + "3 0.28390 0.2414 0.10520 0.2597 \n", + "4 0.13280 0.1980 0.10430 0.1809 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "0 0.07871 ... 17.33 184.60 2019.0 \n", + "1 0.05667 ... 23.41 158.80 1956.0 \n", + "2 0.05999 ... 25.53 152.50 1709.0 \n", + "3 0.09744 ... 26.50 98.87 567.7 \n", + "4 0.05883 ... 16.67 152.20 1575.0 \n", + "\n", + " worst smoothness worst compactness worst concavity worst concave points \\\n", + "0 0.1622 0.6656 0.7119 0.2654 \n", + "1 0.1238 0.1866 0.2416 0.1860 \n", + "2 0.1444 0.4245 0.4504 0.2430 \n", + "3 0.2098 0.8663 0.6869 0.2575 \n", + "4 0.1374 0.2050 0.4000 0.1625 \n", + "\n", + " worst symmetry worst fractal dimension target \n", + "0 0.4601 0.11890 0 \n", + "1 0.2750 0.08902 0 \n", + "2 0.3613 0.08758 0 \n", + "3 0.6638 0.17300 0 \n", + "4 0.2364 0.07678 0 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_loader_simplified(random_state=42).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Everything looks as we'd expect and the values are all in the right format 🥳.\n", + "\n", + "We're now at the point where can bring all this step and some others together into a single\n", + "pipeline, the top-level organising entity for code in ZenML. Creating such a pipeline is\n", + "as simple as adding a `@pipeline` decorator to a function. This specific\n", + "pipeline doesn't return a value, but that option is available to you if you need." + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "b50a9537", "metadata": {}, "outputs": [], "source": [ "@pipeline\n", - "def _feature_engineering(\n", + "def feature_engineering(\n", " test_size: float = 0.2,\n", " drop_na: Optional[bool] = None,\n", " normalize: Optional[bool] = None,\n", " drop_columns: Optional[List[str]] = None,\n", " target: Optional[str] = \"target\",\n", "):\n", - " \"\"\"\n", - " Feature engineering pipeline.\n", - "\n", - " This is a pipeline that loads the data, processes it and splits\n", - " it into train and test sets.\n", - "\n", - " Args:\n", - " test_size: Size of holdout set for training 0.0..1.0\n", - " drop_na: If `True` NA values will be removed from dataset\n", - " normalize: If `True` dataset will be normalized with MinMaxScaler\n", - " drop_columns: List of columns to drop from dataset\n", - " target: Name of target column in dataset\n", - " \"\"\"\n", - " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", + " \"\"\"Feature engineering pipeline.\"\"\"\n", " # Link all the steps together by calling them and passing the output\n", " # of one step as the input of the next step.\n", " raw_data = data_loader(random_state=random.randint(0, 100), target=target)\n", @@ -93,151 +830,630 @@ " return dataset_trn, dataset_tst" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're ready to run the pipeline now, which we can do just -- as with the step -- by calling the\n", + "pipeline function itself:" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "bc5feef4-7016-420e-9af9-2e87ff666f74", + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 3)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.950s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.650s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.506s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_07-16_50_18_998605\u001b[1;35m has finished in \u001b[0m\u001b[1;36m13.591s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/84da85e1-956e-4d47-8719-9ee1c3335ab5/runs/80ccff19-b000-434d-84f8-b93fd8cfbc45/dag\u001b[0m\n" + ] + } + ], "source": [ - "pipeline_args = {}\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"feature_engineering.yaml\")\n", - "fe_p_configured = _feature_engineering.with_options(**pipeline_args)" + "feature_engineering()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's run this again with a slightly different test size, to create another dataset:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "75cf3740-b2d8-4c4b-b91b-dc1637000880", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 4)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.236s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.466s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_07-16_50_36_291181\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.784s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/739a6ab3-b209-4947-8cc1-c3aafa4d9718/runs/ee337da9-e510-49df-957c-333b24cdf0b8/dag\u001b[0m\n" + ] + } + ], + "source": [ + "feature_engineering(test_size=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the data loader step was cached, while the rest of the pipeline was rerun. \n", + "This is because ZenML automatically determined that nothing had changed in the data loader step, \n", + "so it didn't need to rerun it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At this point you might be interested to view your pipeline runs in the ZenML\n", + "Dashboard. You can spin this up by executing the next cell. This will start a\n", + "server which you can access by clicking on the link that appears in the output\n", + "of the cell.\n", + "\n", + "Log into the Dashboard using default credentials (username 'default' and\n", + "password left blank). From there you can inspect the pipeline or the specific\n", + "pipeline run.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: \u001b[31m\u001b[1mYour ZenML client is already connected to a remote server. If you want to spin up a local ZenML server, please disconnect from the remote server first by running `zenml disconnect`.\u001b[0m\n" + ] + } + ], + "source": [ + "from zenml.environment import Environment\n", + "\n", + "if Environment.in_google_colab():\n", + " # run ZenML through a cloudflare tunnel to get a public endpoint\n", + " !zenml up --port 8237 & cloudflared tunnel --url http://localhost:8237\n", + "else:\n", + " !zenml up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also fetch the pipeline from the server and view our results directly in the notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature_engineering-2023_12_07-16_50_36_291181\n" + ] + } + ], + "source": [ + "client = Client()\n", + "run = client.get_pipeline(\"feature_engineering\").last_run\n", + "print(run.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also see the data artifacts that were produced by the last step of the pipeline:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'dataset_tst': ArtifactResponse(id=UUID('77b1c2eb-c9bd-4030-b42c-21d06927d2b9'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 7, 16, 50, 45), updated=datetime.datetime(2023, 12, 7, 16, 50, 45), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), version='80', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_tst/b277e9ac-2631-4038-86f5-a71b7da09104', type=), metadata=None, name='dataset_tst'),\n", + " 'dataset_trn': ArtifactResponse(id=UUID('92cbfbef-0bf7-4247-9448-73c429465b82'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 7, 16, 50, 44), updated=datetime.datetime(2023, 12, 7, 16, 50, 44), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), version='82', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_trn/b277e9ac-2631-4038-86f5-a71b7da09104', type=), metadata=None, name='dataset_trn'),\n", + " 'preprocess_pipeline': ArtifactResponse(id=UUID('bf3fd112-e5d0-4c0a-aaff-18307b2401fe'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 7, 16, 50, 46), updated=datetime.datetime(2023, 12, 7, 16, 50, 46), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), version='80', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/preprocess_pipeline/b277e9ac-2631-4038-86f5-a71b7da09104', type=), metadata=None, name='preprocess_pipeline')}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "run.steps[\"data_preprocessor\"].outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "ename": "ValidationError", + "evalue": "1 validation error for DistributionPackageSource\npackage_name\n field required (type=value_error.missing)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 30\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m \u001b[39m# Read one of the datasets:\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m run\u001b[39m.\u001b[39;49msteps[\u001b[39m\"\u001b[39;49m\u001b[39mdata_preprocessor\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49moutputs[\u001b[39m\"\u001b[39;49m\u001b[39mdataset_trn\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49mload()\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/models/v2/core/artifact.py:306\u001b[0m, in \u001b[0;36mArtifactResponse.load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Materializes (loads) the data stored in this artifact.\u001b[39;00m\n\u001b[1;32m 300\u001b[0m \n\u001b[1;32m 301\u001b[0m \u001b[39mReturns:\u001b[39;00m\n\u001b[1;32m 302\u001b[0m \u001b[39m The materialized data.\u001b[39;00m\n\u001b[1;32m 303\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 304\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mzenml\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39martifacts\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m load_artifact_from_response\n\u001b[0;32m--> 306\u001b[0m \u001b[39mreturn\u001b[39;00m load_artifact_from_response(\u001b[39mself\u001b[39;49m)\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/artifacts/utils.py:381\u001b[0m, in \u001b[0;36mload_artifact_from_response\u001b[0;34m(artifact)\u001b[0m\n\u001b[1;32m 373\u001b[0m logger\u001b[39m.\u001b[39mwarning(\n\u001b[1;32m 374\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mUnable to restore artifact store while trying to load artifact \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 375\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m`\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m`. If this artifact is stored in a remote artifact store, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 376\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mthis might lead to issues when trying to load the artifact.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 377\u001b[0m artifact\u001b[39m.\u001b[39mid,\n\u001b[1;32m 378\u001b[0m )\n\u001b[1;32m 380\u001b[0m \u001b[39m# breakpoint()\u001b[39;00m\n\u001b[0;32m--> 381\u001b[0m \u001b[39mreturn\u001b[39;00m _load_artifact_from_uri(\n\u001b[1;32m 382\u001b[0m materializer\u001b[39m=\u001b[39;49martifact\u001b[39m.\u001b[39;49mmaterializer,\n\u001b[1;32m 383\u001b[0m data_type\u001b[39m=\u001b[39;49martifact\u001b[39m.\u001b[39;49mdata_type,\n\u001b[1;32m 384\u001b[0m uri\u001b[39m=\u001b[39;49martifact\u001b[39m.\u001b[39;49muri,\n\u001b[1;32m 385\u001b[0m )\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/artifacts/utils.py:468\u001b[0m, in \u001b[0;36m_load_artifact_from_uri\u001b[0;34m(materializer, data_type, uri)\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[39m# Resolve the artifact class\u001b[39;00m\n\u001b[1;32m 466\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 467\u001b[0m \u001b[39m# breakpoint()\u001b[39;00m\n\u001b[0;32m--> 468\u001b[0m artifact_class \u001b[39m=\u001b[39m source_utils\u001b[39m.\u001b[39;49mload(data_type)\n\u001b[1;32m 469\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mModuleNotFoundError\u001b[39;00m, \u001b[39mAttributeError\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m logger\u001b[39m.\u001b[39merror(\n\u001b[1;32m 471\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mZenML cannot locate and import the data type of this \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 472\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39martifact \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mdata_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 473\u001b[0m )\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/utils/source_utils.py:75\u001b[0m, in \u001b[0;36mload\u001b[0;34m(source)\u001b[0m\n\u001b[1;32m 73\u001b[0m import_root \u001b[39m=\u001b[39m get_source_root()\n\u001b[1;32m 74\u001b[0m \u001b[39melif\u001b[39;00m source\u001b[39m.\u001b[39mtype \u001b[39m==\u001b[39m SourceType\u001b[39m.\u001b[39mDISTRIBUTION_PACKAGE:\n\u001b[0;32m---> 75\u001b[0m source \u001b[39m=\u001b[39m DistributionPackageSource\u001b[39m.\u001b[39;49mparse_obj(source)\n\u001b[1;32m 76\u001b[0m \u001b[39mif\u001b[39;00m source\u001b[39m.\u001b[39mversion:\n\u001b[1;32m 77\u001b[0m current_package_version \u001b[39m=\u001b[39m _get_package_version(\n\u001b[1;32m 78\u001b[0m package_name\u001b[39m=\u001b[39msource\u001b[39m.\u001b[39mpackage_name\n\u001b[1;32m 79\u001b[0m )\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValidationError\u001b[0m: 1 validation error for DistributionPackageSource\npackage_name\n field required (type=value_error.missing)" + ] + } + ], + "source": [ + "# Read one of the datasets. This is the one with a 0.3 test split\n", + "run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also get the artifacts directly. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ArtifactResponse(id=UUID('92cbfbef-0bf7-4247-9448-73c429465b82'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 7, 16, 50, 44), updated=datetime.datetime(2023, 12, 7, 16, 50, 44), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), version='82', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_trn/b277e9ac-2631-4038-86f5-a71b7da09104', type=), metadata=None, name='dataset_trn')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset_trn_artifact = client.get_artifact(\"dataset_trn\")\n", + "dataset_tst_artifact = client.get_artifact(\"dataset_tst\")\n", + "\n", + "dataset_trn_artifact" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use these artifacts from above in our next pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ⌚ Step 2: Training pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our data it makes sense to train some models to get a sense of\n", + "how difficult the task is. The Breast Cancer dataset is sufficiently large and complex \n", + "that it's unlikely we'll be able to train a model that behaves perfectly since the problem \n", + "is inherently complex, but we can get a sense of what a reasonable baseline looks like.\n", + "\n", + "We'll start with two simple models, a SGD Classifier and a Random Forest\n", + "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", + "same data and then compare their performance." + ] + }, + { + "cell_type": "code", + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "latest_run = fe_p_configured()" + "import pandas as pd\n", + "from sklearn.base import ClassifierMixin\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import SGDClassifier\n", + "from typing_extensions import Annotated\n", + "from zenml import ArtifactConfig, step\n", + "from zenml.logger import get_logger\n", + "\n", + "logger = get_logger(__name__)\n", + "\n", + "\n", + "@step\n", + "def model_trainer(\n", + " dataset_trn: pd.DataFrame,\n", + " model_type: str = \"sgd\",\n", + ") -> Annotated[ClassifierMixin, ArtifactConfig(name=\"model\", is_model_artifact=True)]:\n", + " \"\"\"Configure and train a model on the training dataset.\"\"\"\n", + " target = \"target\"\n", + " if model_type == \"sgd\":\n", + " model = SGDClassifier()\n", + " elif model_type == \"rf\":\n", + " model = RandomForestClassifier()\n", + " else:\n", + " raise ValueError(f\"Unknown model type {model_type}\") \n", + "\n", + " logger.info(f\"Training model {model}...\")\n", + "\n", + " model.fit(\n", + " dataset_trn.drop(columns=[target]),\n", + " dataset_trn[target],\n", + " )\n", + " return model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our two training steps both return different kinds of `sklearn` classifier\n", + "models, so we use the generic `ClassifierMixin` type hint for the return type." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ZenML allows you to load any version of any dataset that is tracked by the framework\n", + "directly into a pipeline using the `ExternalArtifact` interface. This is very convenient\n", + "in this case, as we'd like to send our preprocessed dataset from the older pipeline directly\n", + "into the training pipeline." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "@pipeline\n", - "def _training(\n", + "def training(\n", " train_dataset_id: Optional[UUID] = None,\n", " test_dataset_id: Optional[UUID] = None,\n", + " model_type: str = \"sgd\",\n", " min_train_accuracy: float = 0.0,\n", " min_test_accuracy: float = 0.0,\n", "):\n", - " \"\"\"\n", - " Model training pipeline.\n", - "\n", - " This is a pipeline that loads the data, processes it and splits\n", - " it into train and test sets, then search for best hyperparameters,\n", - " trains and evaluates a model.\n", - "\n", - " Args:\n", - " test_size: Size of holdout set for training 0.0..1.0\n", - " drop_na: If `True` NA values will be removed from dataset\n", - " normalize: If `True` dataset will be normalized with MinMaxScaler\n", - " drop_columns: List of columns to drop from dataset\n", - " \"\"\"\n", - " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", - " # Link all the steps together by calling them and passing the output\n", - " # of one step as the input of the next step.\n", - " \n", - " # Execute Feature Engineering Pipeline\n", + " \"\"\"Model training pipeline.\"\"\" \n", " if train_dataset_id is None or test_dataset_id is None:\n", - " dataset_trn, dataset_tst = _feature_engineering()\n", + " # If we dont pass the IDs, this will run the feature engineering pipeline \n", + " dataset_trn, dataset_tst = feature_engineering()\n", " else:\n", + " # Load the datasets from an older pipeline\n", " dataset_trn = ExternalArtifact(id=train_dataset_id)\n", - " dataset_tst = ExternalArtifact(id=test_dataset_id)\n", - " \n", - " model = model_trainer(\n", + " dataset_tst = ExternalArtifact(id=test_dataset_id) \n", + "\n", + " trained_model = model_trainer(\n", " dataset_trn=dataset_trn,\n", + " model_type=model_type,\n", " )\n", "\n", " model_evaluator(\n", - " model=model,\n", + " model=trained_model,\n", " dataset_trn=dataset_trn,\n", " dataset_tst=dataset_tst,\n", " min_train_accuracy=min_train_accuracy,\n", " min_test_accuracy=min_test_accuracy,\n", - " )\n" + " )" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "pipeline_args = {}\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"training.yaml\")\n", - "fe_t_configured = _training.with_options(**pipeline_args)" + "The end goal of this quick baseline evaluation is to understand which of the two\n", + "models performs better. We'll use the `evaluator` step to compare the two\n", + "models. This step takes in the model from the trainer step, and computes its score\n", + "over the testing set." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 13)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.695s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", + "\u001b[1;35mTest accuracy=95.71%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.661s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_07-17_07_26_910917\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.706s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/c5622964-5dae-488c-a3d8-8564f4b47e30/runs/bae238a7-7df5-407b-a70a-6ed3ddcd2d60/dag\u001b[0m\n" + ] + } + ], "source": [ - "fe_t_configured()" + "# Use a random forest model\n", + "training(model_type=\"rf\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)\n", + "\n", + "rf_run = client.get_pipeline(\"training\").last_run" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 14)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.781s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTrain accuracy=87.57%\u001b[0m\n", + "\u001b[1;35mTest accuracy=90.18%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.756s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_07-17_07_43_757223\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.332s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/ba6e6157-88ba-4347-80bb-89bca2f7fb1b/runs/27dcce8a-3787-4daa-a1bb-e24ebee82bc9/dag\u001b[0m\n" + ] + } + ], "source": [ - "from typing import Optional\n", - "\n", - "import pandas as pd\n", - "from typing_extensions import Annotated\n", - "\n", - "from zenml import get_step_context, step\n", - "from zenml.logger import get_logger\n", - "\n", - "logger = get_logger(__name__)\n", - "\n", - "\n", - "@step\n", - "def inference_predict(\n", - " dataset_inf: pd.DataFrame,\n", - ") -> Annotated[pd.Series, \"predictions\"]:\n", - " \"\"\"Predictions step.\n", + "# Use a SGD classifier\n", + "training(model_type=\"sgd\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)\n", "\n", - " This is an example of a predictions step that takes the data in and returns\n", - " predicted values.\n", - "\n", - " This step is parameterized, which allows you to configure the step\n", - " independently of the step code, before running it in a pipeline.\n", - " In this example, the step can be configured to use different input data.\n", - " See the documentation for more information:\n", - "\n", - " https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines\n", - "\n", - " Args:\n", - " dataset_inf: The inference dataset.\n", - "\n", - " Returns:\n", - " The predictions as pandas series\n", - " \"\"\"\n", - " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", - " model_version = get_step_context().model_version\n", + "sgd_run = client.get_pipeline(\"training\").last_run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see from the logs already how our model training went: the\n", + "`RandomForestClassifier` performed considerably better than the `SGDClassifier`.\n", + "We can use the ZenML `Client` to verify this:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The evaluator returns a float value with the accuracy\n", + "rf_run.steps[\"model_evaluator\"].output.load() > sgd_run.steps[\"model_evaluator\"].output.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ⌚ Step 3: Associating a model with your pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see it is relatively easy to train ML models using ZenML pipelines. But it can be somewhat clunky to track\n", + "all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*,\n", + "which is a central register of all your ML models.\n", "\n", - " print(model_version)\n", + "You can easily create a ZenML Model and associate it with your pipelines using the `ModelVersion` object:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "pipeline_settings = {}\n", + "pipeline_settings[\"model_version\"] = ModelVersion(\n", + " name=\"breast_cancer_classifier\",\n", + " license=\"Apache 2.0\",\n", + " description=\"A breast cancer classifier\",\n", + " tags=[\"classification\", \"sklearn\"],\n", + ")\n", "\n", - " # run prediction from memory\n", - " predictor = model_version.load_artifact(\"model\")\n", - " predictions = predictor.predict(dataset_inf)\n", + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "training_configured = training.with_options(**pipeline_settings)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 14)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m13\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_07-17_08_57_860304\u001b[1;35m has finished in \u001b[0m\u001b[1;36m6.124s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/ba6e6157-88ba-4347-80bb-89bca2f7fb1b/runs/ce39950a-313c-434c-bc3a-a62f2e5ed4bd/dag\u001b[0m\n" + ] + } + ], + "source": [ + "# We can now run this as usual\n", + "training_configured(model_type=\"sgd\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 13)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m14\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_07-17_09_08_682638\u001b[1;35m has finished in \u001b[0m\u001b[1;36m5.989s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/c5622964-5dae-488c-a3d8-8564f4b47e30/runs/85f154cf-675d-45ac-87b3-b36673b63fe7/dag\u001b[0m\n" + ] + } + ], + "source": [ + "# We can now run this as usual\n", + "training_configured(model_type=\"rf\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "\n", - " print(predictions)\n", - " predictions = pd.Series(predictions, name=\"predicted\")\n", - " ### YOUR CODE ENDS HERE ###\n", + "You can list your ZenML model and their versions as follows:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name='breast_cancer_classifier' license='Apache 2.0' description='Classification of Breast Cancer Dataset.' audience=None use_cases=None limitations=None trade_offs=None ethics=None id=UUID('952d7089-dac6-4402-874a-89d81e308e33') created=datetime.datetime(2023, 12, 7, 14, 17, 13) updated=datetime.datetime(2023, 12, 7, 14, 17, 13) missing_permissions=False user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io') workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default') tags=[TagResponseModel(id=UUID('0030e89b-b3a1-4267-a2d8-16fad8a39860'), created=datetime.datetime(2023, 11, 30, 9, 45, 52), updated=datetime.datetime(2023, 11, 30, 9, 45, 52), missing_permissions=False, name='classification', color=, tagged_count=1), TagResponseModel(id=UUID('e308de9a-819a-4079-8c09-336f55c095c8'), created=datetime.datetime(2023, 11, 30, 9, 45, 52), updated=datetime.datetime(2023, 11, 30, 9, 45, 52), missing_permissions=False, name='sklearn', color=, tagged_count=1)] latest_version='14'\n", + "Model breast_cancer_classifier has 14 versions\n" + ] + } + ], + "source": [ + "client = Client()\n", + "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", + "print(zenml_model)\n", "\n", - " return predictions\n" + "print(f\"Model {zenml_model.name} has {len(zenml_model.versions)} versions\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see a new model version was created when the `training` pipeline was run. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fe_t_configured()" ] }, { @@ -247,7 +1463,7 @@ "outputs": [], "source": [ "@pipeline\n", - "def _batch_inference():\n", + "def batch_inference():\n", " \"\"\"\n", " Model batch inference pipeline.\n", "\n", @@ -281,7 +1497,7 @@ "source": [ "pipeline_args = {}\n", "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"inference.yaml\")\n", - "fe_b_configured = _batch_inference.with_options(**pipeline_args)" + "fe_b_configured = batch_inference.with_options(**pipeline_args)" ] }, { @@ -300,68 +1516,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "markdown", - "id": "51690802-31a7-4e6d-9f88-e6457c6c4a96", - "metadata": {}, - "source": [ - "# Huggingface Model to Sagemaker Endpoint: Automating MLOps with ZenML\n", - "Deploying Huggingface models to AWS Sagemaker endpoints typically only requires a few lines of code. However, there's a growing demand to not just deploy, but to seamlessly automate the entire flow from training to production with comprehensive lineage tracking. ZenML adeptly fills this niche, providing an end-to-end MLOps solution for Huggingface users wishing to deploy to Sagemaker. Below, we’ll walk through the architecture that ZenML employs to bring a Huggingface model into production with AWS Sagemaker. Of course all of this can be adapted to not just Sagemaker, but any other model deployment service like GCP Vertex or Azure ML Platform.\n", - "\n", - "This blog post showcases one way of using ZenML pipelines to achieve this:\n", - "\n", - "- Create and version a dataset in a feature_engineering_pipeline.\n", - "- Train/Finetune a BERT-based Sentiment Analysis NLP model and push to Huggingface Hub in a training_pipeline.\n", - "- Promote this model to Production by comparing to previous models in a promotion_pipeline.\n", - "- Deploy the model at the Production Stage to a AWS Sagemaker endpoint with a deployment_pipeline.\n", - "\n", - "\"Pipelines" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "500e3c24-b105-4a69-b2fc-e0ce1f1c1d46", - "metadata": {}, - "outputs": [], - "source": [ - "# Do the imports at the top\n", - "\n", - "import numpy as np\n", - "from datasets import DatasetDict, load_dataset\n", - "from typing_extensions import Annotated\n", - "from zenml import step\n", - "from zenml.logger import get_logger\n", - "\n", - "import os\n", - "from typing import Optional\n", - "from datetime import datetime as dt\n", - "\n", - "from zenml import pipeline\n", - "from zenml.model import ModelConfig\n", - "\n", - "from steps import (\n", - " data_loader,\n", - " notify_on_failure,\n", - " tokenization_step,\n", - " tokenizer_loader,\n", - " generate_reference_and_comparison_datasets,\n", - ")\n", - "from zenml.integrations.evidently.metrics import EvidentlyMetricConfig\n", - "from zenml.integrations.evidently.steps import (\n", - " EvidentlyColumnMapping,\n", - " evidently_report_step,\n", - ")\n", - "\n", - "from pipelines import (\n", - " sentinment_analysis_deploy_pipeline,\n", - " sentinment_analysis_promote_pipeline,\n", - " sentinment_analysis_training_pipeline,\n", - ")\n", - "\n", - "logger = get_logger(__name__)" - ] - }, { "cell_type": "markdown", "id": "fc77b660-e206-46b1-a924-407e797a8f47", @@ -373,16 +1527,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "id": "31edaf46-6981-42be-99b7-9bdd91c160d5", - "metadata": {}, - "source": [ - "## 👶 Step 1: Start with feature engineering\n", - "\n", - "Automated feature engineering forms the foundation of this MLOps workflow. Thats why the first pipeline is the feature engineering pipeline. This pipeline loads some data from Huggingface and uses a base tokenizer to create a tokenized dataset. The data loader step is a simple Python function that returns a Huggingface dataloader object:" - ] - }, { "cell_type": "code", "execution_count": null, @@ -782,50 +1926,31 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "606fdb3c-4eca-4d32-bccb-280743d15528", + "cell_type": "markdown", + "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", "metadata": {}, - "outputs": [], "source": [ - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"deploying_config.yaml\")\n", + "## Congratulations!\n", "\n", - "# Deploying pipeline has new ZenML model config\n", - "model_config = ModelConfig(\n", - " name=zenml_model_name,\n", - " version=ModelStages.PRODUCTION,\n", - ")\n", - "pipeline_args[\"model_config\"] = model_config\n", - "pipeline_args[\"enable_cache\"] = False\n", - "run_args_deploying = {}\n", - "pipeline_args[\n", - " \"run_name\"\n", - "] = f\"sentinment_analysis_deploy_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87f1f982-ab96-4207-8e7e-e318473587e9", - "metadata": {}, - "outputs": [], - "source": [ - "sentinment_analysis_deploy_pipeline.with_options(**pipeline_args)(\n", - " **run_args_deploying\n", - ")" + "You just built two ML pipelines! You trained two models, evaluated them against\n", + "a test set, registered the best one with the ZenML model control plane,\n", + "and served some predictions. You also learned how to iterate on your models and\n", + "data by using some of the ZenML utility abstractions. You saw how to view your\n", + "artifacts and stacks via the CLI as well as the ZenML Dashboard.\n", + "\n", + "And that is just the tip of the iceberg of what ZenML can do; check out the [**docs**](https://docs.zenml.io/) to learn more\n", + "about the capabilities of ZenML.\n", + "\n", + "## What to do now\n", + "\n", + "* If you have questions or feedback... join our [**Slack Community**](https://zenml.io/slack) and become part of the ZenML family!\n", + "* If you want to try ZenML in a real-world setting... check out the [ZenML Cloud](https://cloud.zenml.io/)." ] }, { "cell_type": "markdown", - "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", "metadata": {}, - "source": [ - "ZenML builds upon the straightforward deployment capability of Huggingface models to AWS Sagemaker, and transforms it into a sophisticated, repeatable, and transparent MLOps workflow. It takes charge of the intricate steps necessary for modern ML systems, ensuring that software engineering leads can focus on iteration and innovation rather than operational intricacies.\n", - "\n", - "To delve deeper into each stage, refer to the comprehensive guide on GitHub[: zenml-io/zenml-huggingface-sagemak](https://github.com/zenml-io/zenml-huggingface-sagemaker)er. Additionally[, this YouTube playli](https://www.youtube.com/watch?v=Q1EH2H8Akgo&list=PLhNrLW_IWplw6dBbmGcL828-atJMu3CwF)st provides a detailed visual walkthrough of the entire pipeline: Huggingface to Sagemaker ZenML tutorial.\n", - "\n", - "Interested in standardizing your MLOps workflows? ZenML Cloud is now available to all - get a managed ZenML server with important features such as RBAC and pipeline trigge[rs. Book a ](https://zenml.io/book-a-demo)demo with us now to learn how you can create your own MLOps pipelines today." - ] + "source": [] } ], "metadata": { diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index 01160fe..dbb8bca 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -91,4 +91,4 @@ def model_evaluator( ) ### YOUR CODE ENDS HERE ### - return float(trn_acc) + return float(tst_acc) diff --git a/templates/globals.j2 b/templates/globals.j2 deleted file mode 100644 index cef7b18..0000000 --- a/templates/globals.j2 +++ /dev/null @@ -1,14 +0,0 @@ -{# -This is where global variables can be defined, which then can be used in one -or more templates. This is especially useful for variables that are generated -randomly, such as random names and passwords. - -For example, if the variable "foo" is defined here: - - {% set foo = "bar" %} - -it can be used in any template by including this file at the top: - - {% include "global_variables" %} - This is my foo: {{ foo }} -#} diff --git a/templates/license b/templates/license deleted file mode 100644 index 3697641..0000000 --- a/templates/license +++ /dev/null @@ -1,110 +0,0 @@ -{% if open_source_license == 'mit' -%} -MIT License - -Copyright (c) {{ full_name }} {% now 'local', '%Y' %} - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. -{% elif open_source_license == 'bsd' %} - -BSD License - -Copyright (c) {{ full_name }} {% now 'local', '%Y' %}. All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, -are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, this - list of conditions and the following disclaimer in the documentation and/or - other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from this - software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. -IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, -INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, -DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY -OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE -OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED -OF THE POSSIBILITY OF SUCH DAMAGE. -{% elif open_source_license == 'isc' -%} -ISC License - -Copyright (c) {{ full_name }} {% now 'local', '%Y' %} - -Permission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies. - -THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. -{% elif open_source_license == 'apache' -%} -Apache Software License 2.0 - -Copyright (c) {{ full_name }} {% now 'local', '%Y' %}. All rights reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - -http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -{% elif open_source_license == 'gpl3' -%} -GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - {{ project_short_description }} - Copyright (C) {{ full_name }} {% now 'local', '%Y' %} - - This program is free software: you can redistribute it and/or modify - it under the terms of the GNU General Public License as published by - the Free Software Foundation, either version 3 of the License, or - (at your option) any later version. - - This program is distributed in the hope that it will be useful, - but WITHOUT ANY WARRANTY; without even the implied warranty of - MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - GNU General Public License for more details. - - You should have received a copy of the GNU General Public License - along with this program. If not, see . - -Also add information on how to contact you by electronic and paper mail. - - You should also get your employer (if you work as a programmer) or school, -if any, to sign a "copyright disclaimer" for the program, if necessary. -For more information on this, and how to apply and follow the GNU GPL, see -. - - The GNU General Public License does not permit incorporating your program -into proprietary programs. If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. -{% endif %} \ No newline at end of file diff --git a/templates/license_header b/templates/license_header deleted file mode 100644 index e02240c..0000000 --- a/templates/license_header +++ /dev/null @@ -1,2 +0,0 @@ -{%- macro license() %}{% include 'templates/license' %}{% endmacro -%} -# {{ license() | replace('\n', '\n# ') }} \ No newline at end of file From 7d3e4bc207505d12b06d94248e876c8fb37c6162 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 8 Dec 2023 10:29:56 +0100 Subject: [PATCH 11/48] new notebook --- template/run.ipynb | 1646 +++++++++++++++++++++----------------------- 1 file changed, 800 insertions(+), 846 deletions(-) diff --git a/template/run.ipynb b/template/run.ipynb index f26233b..e0ac72e 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -44,124 +44,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - 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Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." - ] - } - ], + "outputs": [], "source": [ "!zenml integration install sklearn mlflow -y\n", "\n", @@ -417,15 +112,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[?25l\u001b[2;36mFound existing ZenML repository at path \u001b[0m\n", - "\u001b[2;32m'/home/htahir1/workspace/zenml_io/template-starter/template'\u001b[0m\u001b[2;36m.\u001b[0m\n", + "\u001b[?25l\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", + "/home/htahir1/workspace/zenml_io/template-starter/template.\n", + "\u001b[1;35mSetting the repo active workspace to 'default'.\u001b[0m\n", + "\u001b[33mSetting the repo active stack to default.\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mZenML repository initialized at \u001b[0m\n", + "\u001b[2;35m/home/htahir1/workspace/zenml_io/template-starter/\u001b[0m\u001b[2;95mtemplate.\u001b[0m\n", "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", "\u001b[2;36m/home/htahir1/workspace/zenml_io/template-starter/template.\u001b[0m\n", "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", "/home/htahir1/workspace/zenml_io/template-starter/template.\n", "\n", - "\u001b[2K\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'default'\u001b[0mive stack to 'default'...\n", - "\u001b[2K\u001b[32m⠙\u001b[0m Setting the repository active stack to 'default'...t'...\u001b[0m\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mThe local active stack was initialized to \u001b[0m\u001b[2;32m'default'\u001b[0m\u001b[2;36m. This local configuration \u001b[0m\n", + "\u001b[2;36mwill only take effect when you're running ZenML from the initialized repository \u001b[0m\n", + "\u001b[2;36mroot, or from a subdirectory. For more information on repositories and \u001b[0m\n", + "\u001b[2;36mconfigurations, please visit \u001b[0m\n", + "\u001b[2;4;94mhttps://docs.zenml.io/user-guide/starter-guide/understand-stacks.\u001b[0m\n", + "\u001b[?25l\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'default'\u001b[0m\n", + "\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to 'default'...t'...\u001b[0m\n", "\u001b[1A\u001b[2K" ] } @@ -434,24 +138,27 @@ "# Initialize ZenML and set the default stack\n", "!zenml init\n", "\n", - "!zenml stack set default\n" + "!zenml stack set default" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "id": "79f775f2", "metadata": {}, "outputs": [], "source": [ "# Do the imports at the top\n", + "from typing_extensions import Annotated\n", + "from sklearn.datasets import load_breast_cancer\n", + "\n", "import random\n", - "from zenml import ExternalArtifact, pipeline, ModelVersion \n", + "import pandas as pd\n", + "from zenml import step, ExternalArtifact, pipeline, ModelVersion, get_step_context\n", "from zenml.client import Client\n", "from zenml.logger import get_logger\n", "from uuid import UUID\n", "\n", - "import os\n", "from typing import Optional, List\n", "\n", "from zenml import pipeline\n", @@ -461,13 +168,14 @@ " data_preprocessor,\n", " data_splitter,\n", " model_evaluator,\n", - " model_trainer,\n", - " inference_predict,\n", " inference_preprocessor\n", ")\n", "\n", + "from zenml.logger import get_logger\n", + "\n", "logger = get_logger(__name__)\n", "\n", + "# Initialize the ZenML client to fetch objects from the ZenML Server\n", "client = Client()" ] }, @@ -485,24 +193,17 @@ "When you're getting started with a machine learning problem you'll want to do\n", "something similar to this: import your data and get it in the right shape for\n", "your training. ZenML mostly gets out of your way when you're writing your Python\n", - "code, as you'll see from the following cell." + "code, as you'll see from the following cell.\n", + "\n", + "\"Feature" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "from sklearn.datasets import load_breast_cancer\n", - "from typing_extensions import Annotated\n", - "from zenml import step\n", - "from zenml.logger import get_logger\n", - "\n", - "logger = get_logger(__name__)\n", - "\n", - "\n", "@step\n", "def data_loader_simplified(\n", " random_state: int, is_inference: bool = False, target: str = \"target\"\n", @@ -554,14 +255,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" ] }, @@ -774,13 +474,14 @@ "[5 rows x 31 columns]" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data_loader_simplified(random_state=42).head()" + "df = data_loader_simplified(random_state=42)\n", + "df.head()" ] }, { @@ -789,7 +490,7 @@ "source": [ "Everything looks as we'd expect and the values are all in the right format 🥳.\n", "\n", - "We're now at the point where can bring all this step and some others together into a single\n", + "We're now at the point where can bring this step (and some others) together into a single\n", "pipeline, the top-level organising entity for code in ZenML. Creating such a pipeline is\n", "as simple as adding a `@pipeline` decorator to a function. This specific\n", "pipeline doesn't return a value, but that option is available to you if you need." @@ -797,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "b50a9537", "metadata": {}, "outputs": [], @@ -834,13 +535,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We're ready to run the pipeline now, which we can do just -- as with the step -- by calling the\n", + "We're ready to run the pipeline now, which we can do just as with the step - by calling the\n", "pipeline function itself:" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -848,21 +549,21 @@ "output_type": "stream", "text": [ "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 3)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.950s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.267s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.650s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.414s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.506s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_07-16_50_18_998605\u001b[1;35m has finished in \u001b[0m\u001b[1;36m13.591s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/84da85e1-956e-4d47-8719-9ee1c3335ab5/runs/80ccff19-b000-434d-84f8-b93fd8cfbc45/dag\u001b[0m\n" + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.527s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_08-09_19_56_071661\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.394s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" ] } ], @@ -879,7 +580,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -887,20 +588,21 @@ "output_type": "stream", "text": [ "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 4)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.294s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.236s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.398s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.466s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_07-16_50_36_291181\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.784s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/739a6ab3-b209-4947-8cc1-c3aafa4d9718/runs/ee337da9-e510-49df-957c-333b24cdf0b8/dag\u001b[0m\n" + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.529s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_08-09_19_58_112527\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.384s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" ] } ], @@ -912,7 +614,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that the data loader step was cached, while the rest of the pipeline was rerun. \n", + "Notice that the data loader step was **cached**, while the rest of the pipeline was rerun. \n", "This is because ZenML automatically determined that nothing had changed in the data loader step, \n", "so it didn't need to rerun it." ] @@ -933,17 +635,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error: \u001b[31m\u001b[1mYour ZenML client is already connected to a remote server. If you want to spin up a local ZenML server, please disconnect from the remote server first by running `zenml disconnect`.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "from zenml.environment import Environment\n", "\n", @@ -958,19 +652,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also fetch the pipeline from the server and view our results directly in the notebook:" + "\"Feature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also fetch the pipeline from the server and view the results directly in the notebook:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "feature_engineering-2023_12_07-16_50_36_291181\n" + "feature_engineering-2023_12_08-09_19_58_112527\n" ] } ], @@ -989,18 +690,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'dataset_tst': ArtifactResponse(id=UUID('77b1c2eb-c9bd-4030-b42c-21d06927d2b9'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 7, 16, 50, 45), updated=datetime.datetime(2023, 12, 7, 16, 50, 45), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), version='80', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_tst/b277e9ac-2631-4038-86f5-a71b7da09104', type=), metadata=None, name='dataset_tst'),\n", - " 'dataset_trn': ArtifactResponse(id=UUID('92cbfbef-0bf7-4247-9448-73c429465b82'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 7, 16, 50, 44), updated=datetime.datetime(2023, 12, 7, 16, 50, 44), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), version='82', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_trn/b277e9ac-2631-4038-86f5-a71b7da09104', type=), metadata=None, name='dataset_trn'),\n", - " 'preprocess_pipeline': ArtifactResponse(id=UUID('bf3fd112-e5d0-4c0a-aaff-18307b2401fe'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 7, 16, 50, 46), updated=datetime.datetime(2023, 12, 7, 16, 50, 46), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), version='80', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/preprocess_pipeline/b277e9ac-2631-4038-86f5-a71b7da09104', type=), metadata=None, name='preprocess_pipeline')}" + "{'preprocess_pipeline': ArtifactResponse(id=UUID('31d08c04-22f0-44ae-89ad-d0530114a043'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 8, 9, 19, 59, 419488), updated=datetime.datetime(2023, 12, 8, 9, 19, 59, 419491), user=UserResponse(id=UUID('d47f2134-eb09-41f8-9b76-4f00340c0d1b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 19, 40146), updated=datetime.datetime(2023, 12, 8, 9, 18, 19, 40148), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), version='2', uri='/home/htahir1/.config/zenml/local_stores/b640139f-5df3-4de5-8a01-0d16f0589628/data_preprocessor/preprocess_pipeline/ed50c2a3-71b1-4ef7-a590-8b4b717732e3', type=), metadata=None, name='preprocess_pipeline'),\n", + " 'dataset_trn': ArtifactResponse(id=UUID('5f934376-6357-4f8a-a813-b7996a379ef1'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 8, 9, 19, 59, 64014), updated=datetime.datetime(2023, 12, 8, 9, 19, 59, 64016), user=UserResponse(id=UUID('d47f2134-eb09-41f8-9b76-4f00340c0d1b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 19, 40146), updated=datetime.datetime(2023, 12, 8, 9, 18, 19, 40148), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), version='2', uri='/home/htahir1/.config/zenml/local_stores/b640139f-5df3-4de5-8a01-0d16f0589628/data_preprocessor/dataset_trn/ed50c2a3-71b1-4ef7-a590-8b4b717732e3', type=), metadata=None, name='dataset_trn'),\n", + " 'dataset_tst': ArtifactResponse(id=UUID('e4c66672-7e64-401e-b3e5-f9a8ab8aa6d5'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 8, 9, 19, 59, 254549), updated=datetime.datetime(2023, 12, 8, 9, 19, 59, 254551), user=UserResponse(id=UUID('d47f2134-eb09-41f8-9b76-4f00340c0d1b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 19, 40146), updated=datetime.datetime(2023, 12, 8, 9, 18, 19, 40148), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), version='2', uri='/home/htahir1/.config/zenml/local_stores/b640139f-5df3-4de5-8a01-0d16f0589628/data_preprocessor/dataset_tst/ed50c2a3-71b1-4ef7-a590-8b4b717732e3', type=), metadata=None, name='dataset_tst')}" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1011,25 +712,395 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { - "ename": "ValidationError", - "evalue": "1 validation error for DistributionPackageSource\npackage_name\n field required (type=value_error.missing)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 30\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m \u001b[39m# Read one of the datasets:\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m run\u001b[39m.\u001b[39;49msteps[\u001b[39m\"\u001b[39;49m\u001b[39mdata_preprocessor\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49moutputs[\u001b[39m\"\u001b[39;49m\u001b[39mdataset_trn\u001b[39;49m\u001b[39m\"\u001b[39;49m]\u001b[39m.\u001b[39;49mload()\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/models/v2/core/artifact.py:306\u001b[0m, in \u001b[0;36mArtifactResponse.load\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Materializes (loads) the data stored in this artifact.\u001b[39;00m\n\u001b[1;32m 300\u001b[0m \n\u001b[1;32m 301\u001b[0m \u001b[39mReturns:\u001b[39;00m\n\u001b[1;32m 302\u001b[0m \u001b[39m The materialized data.\u001b[39;00m\n\u001b[1;32m 303\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 304\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mzenml\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39martifacts\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mutils\u001b[39;00m \u001b[39mimport\u001b[39;00m load_artifact_from_response\n\u001b[0;32m--> 306\u001b[0m \u001b[39mreturn\u001b[39;00m load_artifact_from_response(\u001b[39mself\u001b[39;49m)\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/artifacts/utils.py:381\u001b[0m, in \u001b[0;36mload_artifact_from_response\u001b[0;34m(artifact)\u001b[0m\n\u001b[1;32m 373\u001b[0m logger\u001b[39m.\u001b[39mwarning(\n\u001b[1;32m 374\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mUnable to restore artifact store while trying to load artifact \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 375\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m`\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m`. If this artifact is stored in a remote artifact store, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 376\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mthis might lead to issues when trying to load the artifact.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 377\u001b[0m artifact\u001b[39m.\u001b[39mid,\n\u001b[1;32m 378\u001b[0m )\n\u001b[1;32m 380\u001b[0m \u001b[39m# breakpoint()\u001b[39;00m\n\u001b[0;32m--> 381\u001b[0m \u001b[39mreturn\u001b[39;00m _load_artifact_from_uri(\n\u001b[1;32m 382\u001b[0m materializer\u001b[39m=\u001b[39;49martifact\u001b[39m.\u001b[39;49mmaterializer,\n\u001b[1;32m 383\u001b[0m data_type\u001b[39m=\u001b[39;49martifact\u001b[39m.\u001b[39;49mdata_type,\n\u001b[1;32m 384\u001b[0m uri\u001b[39m=\u001b[39;49martifact\u001b[39m.\u001b[39;49muri,\n\u001b[1;32m 385\u001b[0m )\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/artifacts/utils.py:468\u001b[0m, in \u001b[0;36m_load_artifact_from_uri\u001b[0;34m(materializer, data_type, uri)\u001b[0m\n\u001b[1;32m 465\u001b[0m \u001b[39m# Resolve the artifact class\u001b[39;00m\n\u001b[1;32m 466\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 467\u001b[0m \u001b[39m# breakpoint()\u001b[39;00m\n\u001b[0;32m--> 468\u001b[0m artifact_class \u001b[39m=\u001b[39m source_utils\u001b[39m.\u001b[39;49mload(data_type)\n\u001b[1;32m 469\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mModuleNotFoundError\u001b[39;00m, \u001b[39mAttributeError\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 470\u001b[0m logger\u001b[39m.\u001b[39merror(\n\u001b[1;32m 471\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mZenML cannot locate and import the data type of this \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 472\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39martifact \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mdata_type\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 473\u001b[0m )\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/zenml/utils/source_utils.py:75\u001b[0m, in \u001b[0;36mload\u001b[0;34m(source)\u001b[0m\n\u001b[1;32m 73\u001b[0m import_root \u001b[39m=\u001b[39m get_source_root()\n\u001b[1;32m 74\u001b[0m \u001b[39melif\u001b[39;00m source\u001b[39m.\u001b[39mtype \u001b[39m==\u001b[39m SourceType\u001b[39m.\u001b[39mDISTRIBUTION_PACKAGE:\n\u001b[0;32m---> 75\u001b[0m source \u001b[39m=\u001b[39m DistributionPackageSource\u001b[39m.\u001b[39;49mparse_obj(source)\n\u001b[1;32m 76\u001b[0m \u001b[39mif\u001b[39;00m source\u001b[39m.\u001b[39mversion:\n\u001b[1;32m 77\u001b[0m current_package_version \u001b[39m=\u001b[39m _get_package_version(\n\u001b[1;32m 78\u001b[0m package_name\u001b[39m=\u001b[39msource\u001b[39m.\u001b[39mpackage_name\n\u001b[1;32m 79\u001b[0m )\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/.virtualenvs/templatestarter/lib/python3.8/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mValidationError\u001b[0m: 1 validation error for DistributionPackageSource\npackage_name\n field required (type=value_error.missing)" - ] + "data": { + "text/html": [ + "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
42814.53019.3494.25659.70.083880.078000.0881700.029250.14730.05746...28.39108.10830.50.108900.264900.377900.095940.24710.074631
37019.55023.21128.901174.00.101000.131800.1856000.102100.19890.05884...30.44142.001313.00.125100.241400.382900.182500.25760.076020
5714.25022.1596.42645.70.104900.200800.2135000.086530.19490.07292...29.51119.10959.50.164000.624700.692200.178500.28440.113200
33211.66017.0773.70421.00.075610.036300.0083060.011620.16710.05731...19.7483.61542.50.099580.064760.030460.042620.27310.068251
2418.61020.25122.101094.00.094400.106600.1490000.077310.16970.05699...27.26139.901403.00.133800.211700.344600.149000.23410.074210
..................................................................
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378 rows × 31 columns

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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "428 14.530 19.34 94.25 659.7 0.08388 \n", + "370 19.550 23.21 128.90 1174.0 0.10100 \n", + "57 14.250 22.15 96.42 645.7 0.10490 \n", + "332 11.660 17.07 73.70 421.0 0.07561 \n", + "24 18.610 20.25 122.10 1094.0 0.09440 \n", + ".. ... ... ... ... ... \n", + "71 18.050 16.15 120.20 1006.0 0.10650 \n", + "106 8.726 15.83 55.84 230.9 0.11500 \n", + "270 16.240 18.77 108.80 805.1 0.10660 \n", + "435 13.380 30.72 86.34 557.2 0.09245 \n", + "102 9.777 16.99 62.50 290.2 0.10370 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "428 0.07800 0.088170 0.02925 0.1473 \n", + "370 0.13180 0.185600 0.10210 0.1989 \n", + "57 0.20080 0.213500 0.08653 0.1949 \n", + "332 0.03630 0.008306 0.01162 0.1671 \n", + "24 0.10660 0.149000 0.07731 0.1697 \n", + ".. ... ... ... ... \n", + "71 0.21460 0.168400 0.10800 0.2152 \n", + "106 0.08201 0.041320 0.01924 0.1649 \n", + "270 0.18020 0.194800 0.09052 0.1876 \n", + "435 0.07426 0.028190 0.03264 0.1375 \n", + "102 0.08404 0.043340 0.01778 0.1584 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "428 0.05746 ... 28.39 108.10 830.5 \n", + "370 0.05884 ... 30.44 142.00 1313.0 \n", + "57 0.07292 ... 29.51 119.10 959.5 \n", + "332 0.05731 ... 19.74 83.61 542.5 \n", + "24 0.05699 ... 27.26 139.90 1403.0 \n", + ".. ... ... ... ... ... \n", + "71 0.06673 ... 18.91 150.10 1610.0 \n", + "106 0.07633 ... 19.62 64.48 284.4 \n", + "270 0.06684 ... 25.09 126.90 1031.0 \n", + "435 0.06016 ... 41.61 96.69 705.6 \n", + "102 0.07065 ... 21.47 71.68 367.0 \n", + "\n", + " worst smoothness worst compactness worst concavity \\\n", + "428 0.10890 0.26490 0.37790 \n", + "370 0.12510 0.24140 0.38290 \n", + "57 0.16400 0.62470 0.69220 \n", + "332 0.09958 0.06476 0.03046 \n", + "24 0.13380 0.21170 0.34460 \n", + ".. ... ... ... \n", + "71 0.14780 0.56340 0.37860 \n", + "106 0.17240 0.23640 0.24560 \n", + "270 0.13650 0.47060 0.50260 \n", + "435 0.11720 0.14210 0.07003 \n", + "102 0.14670 0.17650 0.13000 \n", + "\n", + " worst concave points worst symmetry worst fractal dimension target \n", + "428 0.09594 0.2471 0.07463 1 \n", + "370 0.18250 0.2576 0.07602 0 \n", + "57 0.17850 0.2844 0.11320 0 \n", + "332 0.04262 0.2731 0.06825 1 \n", + "24 0.14900 0.2341 0.07421 0 \n", + ".. ... ... ... ... \n", + "71 0.21020 0.3751 0.11080 0 \n", + "106 0.10500 0.2926 0.10170 1 \n", + "270 0.17320 0.2770 0.10630 0 \n", + "435 0.07763 0.2196 0.07675 1 \n", + "102 0.05334 0.2533 0.08468 1 \n", + "\n", + "[378 rows x 31 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1041,30 +1112,68 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can also get the artifacts directly. " + "We can also get the artifacts directly. Each time you create a new pipeline run, a new `artifact version` is created.\n", + "\n", + "You can fetch these artifact and their versions using the `client`: " ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "ArtifactResponse(id=UUID('92cbfbef-0bf7-4247-9448-73c429465b82'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 7, 16, 50, 44), updated=datetime.datetime(2023, 12, 7, 16, 50, 44), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), version='82', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_trn/b277e9ac-2631-4038-86f5-a71b7da09104', type=), metadata=None, name='dataset_trn')" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" + "ename": "AttributeError", + "evalue": "'ArtifactResponse' object has no attribute 'versions'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 33\u001b[0m line \u001b[0;36m5\n\u001b[1;32m 2\u001b[0m dataset_tst_artifact \u001b[39m=\u001b[39m client\u001b[39m.\u001b[39mget_artifact(\u001b[39m\"\u001b[39m\u001b[39mdataset_tst\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 3\u001b[0m preprocessing_pipeline_artifact \u001b[39m=\u001b[39m client\u001b[39m.\u001b[39mget_artifact(\u001b[39m\"\u001b[39m\u001b[39mpreprocess_pipeline\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThe training set has \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(dataset_trn_artifact\u001b[39m.\u001b[39mversions)\u001b[39m}\u001b[39;00m\u001b[39m records, and the test set has \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(dataset_tst_artifact\u001b[39m.\u001b[39mversions)\u001b[39m}\u001b[39;00m\u001b[39m records.\u001b[39m\u001b[39m\"\u001b[39m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'ArtifactResponse' object has no attribute 'versions'" + ] } ], "source": [ "dataset_trn_artifact = client.get_artifact(\"dataset_trn\")\n", "dataset_tst_artifact = client.get_artifact(\"dataset_tst\")\n", + "preprocessing_pipeline_artifact = client.get_artifact(\"preprocess_pipeline\")\n", + "\n", + "f\"The training set has {len(dataset_trn_artifact.versions)} records, and the test set has {len(dataset_tst_artifact.versions)} records.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you started with a fresh install, then you would have two versions corresponding\n", + "to the two pipelines that we ran above. Let's select the latter one to move forward: " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'ArtifactResponse' object has no attribute 'latest_version'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 35\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m \u001b[39m# Choose the last version\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m dataset_trn_artifact_version \u001b[39m=\u001b[39m dataset_trn_artifact\u001b[39m.\u001b[39;49mlatest_version\n\u001b[1;32m 3\u001b[0m dataset_tst_artifact_version \u001b[39m=\u001b[39m dataset_tst_artifact\u001b[39m.\u001b[39mlatest_version\n\u001b[1;32m 5\u001b[0m \u001b[39m# Load it again to verify\u001b[39;00m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'ArtifactResponse' object has no attribute 'latest_version'" + ] + } + ], + "source": [ + "# Choose the last version\n", + "dataset_trn_artifact_version = dataset_trn_artifact.latest_version\n", + "dataset_tst_artifact_version = dataset_tst_artifact.latest_version\n", "\n", - "dataset_trn_artifact" + "# Load it again to verify\n", + "dataset_trn_artifact_version.load()" ] }, { @@ -1092,12 +1201,14 @@ "\n", "We'll start with two simple models, a SGD Classifier and a Random Forest\n", "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", - "same data and then compare their performance." + "same data and then compare their performance.\n", + "\n", + "\"Training" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1155,7 +1266,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1202,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1210,38 +1321,41 @@ "output_type": "stream", "text": [ "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 13)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.695s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.310s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", "\u001b[1;35mTest accuracy=95.71%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.661s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_07-17_07_26_910917\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.706s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/c5622964-5dae-488c-a3d8-8564f4b47e30/runs/bae238a7-7df5-407b-a70a-6ed3ddcd2d60/dag\u001b[0m\n" + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.140s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_08-09_21_18_513475\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.623s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" ] } ], "source": [ - "# Use a random forest model\n", - "training(model_type=\"rf\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)\n", + "# Use a random forest model with the chosen datasets.\n", + "# We need to pass the ID's of the datasets into the function\n", + "training(\n", + " model_type=\"rf\",\n", + " train_dataset_id=dataset_trn_artifact.id, # dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact.id # dataset_tst_artifact_version.id\n", + ")\n", "\n", "rf_run = client.get_pipeline(\"training\").last_run" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1249,25 +1363,23 @@ "output_type": "stream", "text": [ "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 14)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.781s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.152s\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=87.57%\u001b[0m\n", - "\u001b[1;35mTest accuracy=90.18%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.756s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_07-17_07_43_757223\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.332s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/ba6e6157-88ba-4347-80bb-89bca2f7fb1b/runs/27dcce8a-3787-4daa-a1bb-e24ebee82bc9/dag\u001b[0m\n" + "\u001b[1;35mTrain accuracy=57.94%\u001b[0m\n", + "\u001b[1;35mTest accuracy=53.37%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.207s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_08-09_21_20_671807\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.519s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" ] } ], @@ -1289,7 +1401,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1298,7 +1410,7 @@ "True" ] }, - "execution_count": 32, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1312,7 +1424,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ⌚ Step 3: Associating a model with your pipeline" + "# 💯 Step 3: Associating a model with your pipeline" ] }, { @@ -1323,31 +1435,29 @@ "all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*,\n", "which is a central register of all your ML models.\n", "\n", - "You can easily create a ZenML Model and associate it with your pipelines using the `ModelVersion` object:" + "You can easily create a ZenML `Model` and associate it with your pipelines using the `ModelVersion` object:" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "pipeline_settings = {}\n", + "\n", + "# Lets add some metadata to the model to make it identifiable\n", "pipeline_settings[\"model_version\"] = ModelVersion(\n", " name=\"breast_cancer_classifier\",\n", " license=\"Apache 2.0\",\n", " description=\"A breast cancer classifier\",\n", - " tags=[\"classification\", \"sklearn\"],\n", - ")\n", - "\n", - "# the `with_options` method allows us to pass in pipeline settings\n", - "# and returns a configured pipeline\n", - "training_configured = training.with_options(**pipeline_settings)" + " tags=[\"breast_cancer\", \"classifier\"],\n", + ")" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1355,31 +1465,39 @@ "output_type": "stream", "text": [ "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 14)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m13\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 2)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m was created implicitly.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36msgd\u001b[1;35m was created.\u001b[0m\n", "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_07-17_08_57_860304\u001b[1;35m has finished in \u001b[0m\u001b[1;36m6.124s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/ba6e6157-88ba-4347-80bb-89bca2f7fb1b/runs/ce39950a-313c-434c-bc3a-a62f2e5ed4bd/dag\u001b[0m\n" + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_08-09_21_36_798853\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.252s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" ] } ], "source": [ + "# Let's train the SGD model and set the version name to \"sgd\"\n", + "pipeline_settings[\"model_version\"].version = \"sgd\"\n", + "\n", + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "training_configured = training.with_options(**pipeline_settings)\n", + "\n", "# We can now run this as usual\n", "training_configured(model_type=\"sgd\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1387,25 +1505,32 @@ "output_type": "stream", "text": [ "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 13)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36m14\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36mrf\u001b[1;35m was created.\u001b[0m\n", "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_07-17_09_08_682638\u001b[1;35m has finished in \u001b[0m\u001b[1;36m5.989s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/c5622964-5dae-488c-a3d8-8564f4b47e30/runs/85f154cf-675d-45ac-87b3-b36673b63fe7/dag\u001b[0m\n" + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_08-09_21_38_756106\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.238s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" ] } ], "source": [ - "# We can now run this as usual\n", + "# Let's train the RF model and set the version name to \"rf\"\n", + "pipeline_settings[\"model_version\"].version = \"rf\"\n", + "\n", + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "training_configured = training.with_options(**pipeline_settings)\n", + "\n", + "# Let's run it again to make sure we have two versions\n", "training_configured(model_type=\"rf\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)" ] }, @@ -1413,516 +1538,344 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "You can list your ZenML model and their versions as follows:\n", - "\n" + "This time, running both pipelines has created two associated **model versions**.\n", + "You can list your ZenML model and their versions as follows:" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "name='breast_cancer_classifier' license='Apache 2.0' description='Classification of Breast Cancer Dataset.' audience=None use_cases=None limitations=None trade_offs=None ethics=None id=UUID('952d7089-dac6-4402-874a-89d81e308e33') created=datetime.datetime(2023, 12, 7, 14, 17, 13) updated=datetime.datetime(2023, 12, 7, 14, 17, 13) missing_permissions=False user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 7, 16, 6, 42), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io') workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default') tags=[TagResponseModel(id=UUID('0030e89b-b3a1-4267-a2d8-16fad8a39860'), created=datetime.datetime(2023, 11, 30, 9, 45, 52), updated=datetime.datetime(2023, 11, 30, 9, 45, 52), missing_permissions=False, name='classification', color=, tagged_count=1), TagResponseModel(id=UUID('e308de9a-819a-4079-8c09-336f55c095c8'), created=datetime.datetime(2023, 11, 30, 9, 45, 52), updated=datetime.datetime(2023, 11, 30, 9, 45, 52), missing_permissions=False, name='sklearn', color=, tagged_count=1)] latest_version='14'\n", - "Model breast_cancer_classifier has 14 versions\n" + "name='breast_cancer_classifier' license='Apache 2.0' description='A breast cancer classifier' audience=None use_cases=None limitations=None trade_offs=None ethics=None id=UUID('22a5cae2-b9f8-4289-9773-74b848436c93') created=datetime.datetime(2023, 12, 8, 9, 21, 36, 672757) updated=datetime.datetime(2023, 12, 8, 9, 21, 36, 672759) missing_permissions=False user=UserResponse(id=UUID('d47f2134-eb09-41f8-9b76-4f00340c0d1b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 19, 40146), updated=datetime.datetime(2023, 12, 8, 9, 18, 19, 40148), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default') workspace=WorkspaceResponse(id=UUID('791ceb8f-8439-47cd-a76d-c61746290ce9'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 18, 797852), updated=datetime.datetime(2023, 12, 8, 9, 18, 18, 797854)), metadata=None, name='default') tags=[TagResponseModel(id=UUID('9adb1133-3932-4180-aa8a-8c77c3ea30af'), created=datetime.datetime(2023, 12, 8, 9, 21, 36, 674655), updated=datetime.datetime(2023, 12, 8, 9, 21, 36, 674656), missing_permissions=False, name='breast_cancer', color=, tagged_count=1), TagResponseModel(id=UUID('36b58849-a05e-4ceb-8c8f-b596878b09a5'), created=datetime.datetime(2023, 12, 8, 9, 21, 36, 697963), updated=datetime.datetime(2023, 12, 8, 9, 21, 36, 697965), missing_permissions=False, name='classifier', color=, tagged_count=1)] latest_version='rf'\n", + "Model breast_cancer_classifier has 2 versions\n" ] + }, + { + "data": { + "text/plain": [ + "('sgd', 'rf')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "client = Client()\n", "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", "print(zenml_model)\n", "\n", - "print(f\"Model {zenml_model.name} has {len(zenml_model.versions)} versions\")" + "print(f\"Model {zenml_model.name} has {len(zenml_model.versions)} versions\")\n", + "\n", + "zenml_model.versions[0].version, zenml_model.versions[1].version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "You can see a new model version was created when the `training` pipeline was run. " + "The interesting part is that ZenML went ahead and linked all artifacts produced by the\n", + "pipelines to that model version, including the two pickle files that represent our\n", + "SGD and RandomForest classifier. We can see all artifacts directly from the model\n", + "version object:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fe_t_configured()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "@pipeline\n", - "def batch_inference():\n", - " \"\"\"\n", - " Model batch inference pipeline.\n", - "\n", - " This is a pipeline that loads the inference data, processes\n", - " it, analyze for data drift and run inference.\n", - " \"\"\"\n", - " ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###\n", - " # Link all the steps together by calling them and passing the output\n", - " # of one step as the input of the next step.\n", - " ########## ETL stage ##########\n", - " random_state = client.get_artifact(\"dataset\").run_metadata[\"random_state\"].value\n", - " target = client.get_artifact(\"dataset_trn\").run_metadata['target'].value\n", - " df_inference = data_loader(\n", - " random_state=random_state, is_inference=True\n", - " )\n", - " df_inference = inference_preprocessor(\n", - " dataset_inf=df_inference,\n", - " preprocess_pipeline=ExternalArtifact(name=\"preprocess_pipeline\"),\n", - " target=target,\n", - " )\n", - " inference_predict(\n", - " dataset_inf=df_inference,\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pipeline_args = {}\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"inference.yaml\")\n", - "fe_b_configured = batch_inference.with_options(**pipeline_args)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fe_b_configured()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "fc77b660-e206-46b1-a924-407e797a8f47", + "execution_count": 27, "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier()" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# 🍳Breaking it down\n", + "# Let's load the RF version\n", + "rf_zenml_model_version = client.get_model_version(\"breast_cancer_classifier\", \"rf\")\n", "\n", + "# We can now load our classifier directly as well\n", + "random_forest_classifier = rf_zenml_model_version.get_artifact(\"model\").load()\n", "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35de0e4c-b6f8-4b68-927a-f40e4130dc93", - "metadata": {}, - "outputs": [], - "source": [ - "@step\n", - "def data_loader() -> Annotated[DatasetDict, \"dataset\"]:\n", - " logger.info(f\"Loading dataset airline_reviews... \")\n", - " hf_dataset = load_dataset(\"Shayanvsf/US_Airline_Sentiment\")\n", - " hf_dataset = hf_dataset.rename_column(\"airline_sentiment\", \"label\")\n", - " hf_dataset = hf_dataset.remove_columns(\n", - " [\"airline_sentiment_confidence\", \"negativereason_confidence\"]\n", - " )\n", - " return hf_dataset" + "random_forest_classifier" ] }, { "cell_type": "markdown", - "id": "49e4462c-1e64-48d3-bae7-76696a958646", "metadata": {}, "source": [ - "Notice that you can give each dataset a name with Python’s Annotated object. The DatasetDict is a native Huggingface dataset which ZenML knows how to persist through steps. This flow ensures reproducibility and version control for every dataset iteration.\n", + "If you are a ZenML Cloud user, you can see all of this visualized in the dashboard:\n", "\n", - "Also notice this is a simple Python function, that can be called with the `entrypoint` wrapper:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18144a6b-c266-453d-82c8-b5d6aa1be0aa", - "metadata": {}, - "outputs": [], - "source": [ - "hf_dataset = data_loader.entrypoint()\n", - "print(hf_dataset)" + "\"Model" ] }, { "cell_type": "markdown", - "id": "31330d3c-044f-4912-8d36-74146f48cecf", "metadata": {}, "source": [ - "Now we put this a full feature engineering pipeline. Each run of the feature engineering pipeline produces a new dataset to use for the training pipeline. ZenML versions this data as it flows through the pipeline.\n", + "There is a lot more you can do with ZenML models, including the ability to\n", + "track metrics by adding metadata to it, or having them persist in a model\n", + "registry. However, these topics can be explored more in the\n", + "[ZenML docs](https://docs.zenml.io).\n", "\n", - "\"Pipelines" - ] - }, - { - "cell_type": "markdown", - "id": "9511bd84-1e97-42db-9b75-06285cc6904c", - "metadata": {}, - "source": [ - "### Set your stack" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "76f3a7e7-0d85-43b3-9e9f-4c7f20ea65e6", - "metadata": {}, - "outputs": [], - "source": [ - "!zenml stack describe hf-sagemaker-local" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04b0bf69-70c6-4408-b18c-95df9e030c0c", - "metadata": {}, - "outputs": [], - "source": [ - "!zenml stack set hf-sagemaker-local" + "For now, we will use the ZenML model control plane to promote our best\n", + "model to `production`. You can do this by simply setting the `stage` of\n", + "your chosen model version to the `production` tag." ] }, { "cell_type": "code", - "execution_count": null, - "id": "de5398a4-a9ec-42d6-bbd6-390244c52d13", + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "!zenml stack get" + "# Set our best classifier to production\n", + "rf_zenml_model_version.set_stage(\"production\", force=True)" ] }, { "cell_type": "markdown", - "id": "152f718d-70c2-4a29-a73e-37db85675cb8", - "metadata": {}, - "source": [ - "### Run the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7ca6c41e-e4b3-46d2-8264-9a453ac9aa3c", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "@pipeline(on_failure=notify_on_failure)\n", - "def sentinment_analysis_feature_engineering_pipeline(\n", - " lower_case: Optional[bool] = True,\n", - " padding: Optional[str] = \"max_length\",\n", - " max_seq_length: Optional[int] = 128,\n", - " text_column: Optional[str] = \"text\",\n", - " label_column: Optional[str] = \"label\",\n", - "):\n", - " # Link all the steps together by calling them and passing the output\n", - " # of one step as the input of the next step.\n", - "\n", - " ########## Load Dataset stage ##########\n", - " dataset = data_loader()\n", - "\n", - " ########## Data Quality stage ##########\n", - " reference_dataset, comparison_dataset = generate_reference_and_comparison_datasets(\n", - " dataset\n", - " )\n", - " text_data_report = evidently_report_step.with_options(\n", - " parameters=dict(\n", - " column_mapping=EvidentlyColumnMapping(\n", - " target=\"label\",\n", - " text_features=[\"text\"],\n", - " ),\n", - " metrics=[\n", - " EvidentlyMetricConfig.metric(\"DataQualityPreset\"),\n", - " EvidentlyMetricConfig.metric(\n", - " \"TextOverviewPreset\", column_name=\"text\"\n", - " ),\n", - " ],\n", - " # We need to download the NLTK data for the TextOverviewPreset\n", - " download_nltk_data=True,\n", - " ),\n", - " )\n", - " text_data_report(reference_dataset, comparison_dataset)\n", - "\n", - " ########## Tokenization stage ##########\n", - " tokenizer = tokenizer_loader(lower_case=lower_case)\n", - " tokenized_data = tokenization_step(\n", - " dataset=dataset,\n", - " tokenizer=tokenizer,\n", - " padding=padding,\n", - " max_seq_length=max_seq_length,\n", - " text_column=text_column,\n", - " label_column=label_column,\n", - " )\n", - " return tokenizer, tokenized_data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c8a5be7-ebaa-41c4-ac23-4afc6e7e06aa", "metadata": {}, - "outputs": [], "source": [ - "# Run a pipeline with the required parameters. \n", - "no_cache: bool = True\n", - "zenml_model_name: str = \"distil_bert_sentiment_analysis\"\n", - "max_seq_length = 512\n", - "\n", - "# This executes all steps in the pipeline in the correct order using the orchestrator\n", - "# stack component that is configured in your active ZenML stack.\n", - "model_config = ModelConfig(\n", - " name=zenml_model_name,\n", - " license=\"Apache 2.0\",\n", - " description=\"Show case Model Control Plane.\",\n", - " create_new_model_version=True,\n", - " delete_new_version_on_failure=True,\n", - " tags=[\"sentiment_analysis\", \"huggingface\"],\n", - ")\n", - "\n", - "pipeline_args = {}\n", - "\n", - "if no_cache:\n", - " pipeline_args[\"enable_cache\"] = False\n", - "\n", - "# Execute Feature Engineering Pipeline\n", - "pipeline_args[\"model_config\"] = model_config\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"feature_engineering_config.yaml\")\n", - "run_args_feature = {\n", - " \"max_seq_length\": max_seq_length,\n", - "}\n", - "pipeline_args[\n", - " \"run_name\"\n", - "] = f\"sentinment_analysis_feature_engineering_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"\n", - "p = sentinment_analysis_feature_engineering_pipeline.with_options(**pipeline_args)\n", - "p(**run_args_feature)" + "Of course, normally one would only promote the model by comparing to all other model\n", + "versions and doing some other tests. But that's a bit more advanced use-case. See the\n", + "[e2e_batch example](https://github.com/zenml-io/zenml/tree/main/examples/e2e) to get\n", + "more insight into that sort of flow!" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "0e7c1ea2-64fe-478a-9963-17c7b7f62110", + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from zenml.client import Client\n", - "from IPython.display import display, HTML\n", - "\n", - "client = Client()\n", - "# CHANGE THIS TO THE LATEST RUN ID\n", - "latest_run = client.get_pipeline_run(\"sentinment_analysis_feature_engineering_pipeline_run_2023_11_21_10_55_56\")\n", - "html = latest_run.steps[\"evidently_report_step\"].outputs['report_html'].load()\n", - "display(HTML(html))" + "Once the model is promoted, we can now consume the right model version in our\n", + "batch inference pipeline directly. Let's see how that works." ] }, { "cell_type": "markdown", - "id": "78ab8771-4421-4975-a3d5-12892a56b805", "metadata": {}, "source": [ - "## 💪 Step 2: Train the model with Huggingface Hub as the model registry\n", - " " + "# 🫅 Step 4: Consuming the model in production" ] }, { "cell_type": "markdown", - "id": "2843efa8-32b6-4b13-ac85-33c99cc94e3e", "metadata": {}, "source": [ - "Once the feature engineering pipeline has run a few times, we have many datasets to choose from. We can feed our desired one into a function that trains the model on the data. Thanks to the ZenML Huggingface integration, this data is loaded directly from the ZenML artifact store.\n", - "\n", - "\"Pipelines\n", + "The batch inference pipeline simply takes the model marked as `production` and runs inference on it\n", + "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", + "and generate predictions:\n", "\n", - "On the left side, we see our local MLOps stack, which defines our infrastructure and tooling we are using for this particular pipeline. ZenML makes it easy to run on a local stack on your development machine, or switch out the stack to run on a AWS Kubeflow-based stack (if you want to scale up).\n", - "\n", - "On the right side is the new kid on the block - the ZenML Model Control Plane. The Model Control Plane is a new feature in ZenML that allows users to have a complete overview of their machine learning models. It allows teams to consolidate all artifacts related to their ML models into one place, and manage its lifecycle easily as you can see from this view from the ZenML Cloud:" + "\"Inference\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "4c99b20f-8e3b-4119-86e9-33dd1395470a", + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"trainer_config.yaml\")\n", - "\n", - "pipeline_args[\"enable_cache\"] = True\n", - "\n", - "run_args_train = {\n", - " \"num_epochs\": 1,\n", - " \"train_batch_size\": 64,\n", - " \"eval_batch_size\": 64,\n", - " \"learning_rate\": 2e-4,\n", - " \"weight_decay\": 0.01,\n", - " \"max_seq_length\": 512,\n", - "}\n", + "@step\n", + "def inference_predict(dataset_inf: pd.DataFrame) -> Annotated[pd.Series, \"predictions\"]:\n", + " \"\"\"Predictions step\"\"\"\n", + " # Get the model_version\n", + " model_version = get_step_context().model_version\n", "\n", - "# Use versioned artifacts from the last step\n", - "# run_args_train[\"dataset_artifact_id\"] = latest_run.steps['tokenization_step'].output.id\n", - "# run_args_train[\"tokenizer_artifact_id\"] = latest_run.steps['tokenizer_loader'].output.id\n", + " # run prediction from memory\n", + " predictor = model_version.load_artifact(\"model\")\n", + " predictions = predictor.predict(dataset_inf)\n", "\n", - "# Configure the model\n", - "pipeline_args[\"model_config\"] = model_config\n", + " predictions = pd.Series(predictions, name=\"predicted\")\n", + " ### YOUR CODE ENDS HERE ###\n", "\n", - "pipeline_args[\n", - " \"run_name\"\n", - "] = f\"sentinment_analysis_training_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" + " return predictions\n" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "96592299-0090-4d2a-962e-6ca232c1fb75", - "metadata": {}, - "outputs": [], - "source": [ - "sentinment_analysis_training_pipeline.with_options(**pipeline_args)(\n", - " **run_args_train\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e24e29de-6d1b-41da-9ab2-ca2b32f1f540", - "metadata": {}, - "outputs": [], - "source": [ - "### Check out a new stack\n", - "!zenml stack describe hf-sagemaker-airflow" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7c9a5bee-8465-4d41-888a-093f1f6a2ef1", + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "### Change the stack\n", - "!zenml stack set hf-sagemaker-airflow" + "Apart from the loading the model, we must also load the preprocessing pipeline that we ran in feature engineering,\n", + "so that we can do the exact steps that we did on training time, in inference time. Let's bring it all together:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "d3772c50-1c90-4ffc-8394-c9cfca16cc53", + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ - "sentinment_analysis_training_pipeline.with_options(**pipeline_args)(\n", - " **run_args_train\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "be79f454-a45d-4f5f-aa93-330d52069124", - "metadata": {}, - "source": [ - "## 🫅 Step 3: Promote the model to production\n" + "@pipeline\n", + "def inference(preprocess_pipeline_id: UUID):\n", + " \"\"\"Model batch inference pipeline\"\"\"\n", + " random_state = client.get_artifact(\"dataset\").run_metadata[\"random_state\"].value\n", + " target = client.get_artifact(\"dataset_trn\").run_metadata['target'].value\n", + " df_inference = data_loader(\n", + " random_state=random_state, is_inference=True\n", + " )\n", + " df_inference = inference_preprocessor(\n", + " dataset_inf=df_inference,\n", + " # We use the preprocess pipeline from the feature engineering pipeline\n", + " preprocess_pipeline=ExternalArtifact(id=preprocess_pipeline_id),\n", + " target=target,\n", + " )\n", + " inference_predict(\n", + " dataset_inf=df_inference,\n", + " )\n" ] }, { "cell_type": "markdown", - "id": "5a09b432-7a66-473e-bdb6-ffdca730498b", "metadata": {}, "source": [ - "Following training, the automated promotion pipeline evaluates models against predefined metrics, identifying and marking the most performant one as 'Production ready'. This is another common use case for the Model Control Plane; we store the relevant metrics there to access them easily later.\n", - "\n", - "\"Pipelines" + "The way to load the right model is to pass in the `production` stage into the `ModelVersion` config this time.\n", + "This will ensure to always load the production model, decoupled from all other pipelines:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "5bac7ae5-70d0-449c-929c-e175c3062f2d", - "metadata": {}, - "outputs": [], - "source": [ - "!zenml stack set hf-sagemaker-local" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "170c9ef6-4e6f-4e50-ac37-e05bef8570ea", + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" + ] + } + ], "source": [ - "run_args_promoting = {}\n", - "model_config = ModelConfig(name=zenml_model_name)\n", - "pipeline_args[\"config_path\"] = os.path.join(\"configs\", \"promoting_config.yaml\")\n", - "\n", - "pipeline_args[\"model_config\"] = model_config\n", + "pipeline_settings = {}\n", "\n", - "pipeline_args[\n", - " \"run_name\"\n", - "] = f\"sentinment_analysis_promoting_pipeline_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}\"" + "# Lets add some metadata to the model to make it identifiable\n", + "pipeline_settings[\"model_version\"] = ModelVersion(\n", + " name=\"breast_cancer_classifier\",\n", + " version=\"production\", # We can pass in the stage name here!\n", + " license=\"Apache 2.0\",\n", + " description=\"A breast cancer classifier\",\n", + " tags=[\"breast_cancer\", \"classifier\"],\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, - "id": "e6df11e2-4591-4186-a8f8-243f9c4d1e3d", + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36minference_predict\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36minference-2023_12_08-09_24_59_254310\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.265s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], "source": [ - "sentinment_analysis_promote_pipeline.with_options(**pipeline_args)(\n", - " **run_args_promoting\n", + "# the `with_options` method allows us to pass in pipeline settings\n", + "# and returns a configured pipeline\n", + "inference_configured = inference.with_options(**pipeline_settings)\n", + "\n", + "# Let's run it again to make sure we have two versions\n", + "# We need to pass in the ID of the preprocessing done in the feature engineering pipeline\n", + "# in order to avoid training-serving skew\n", + "inference_configured(\n", + " preprocess_pipeline_id=preprocessing_pipeline_artifact.id\n", ")" ] }, { "cell_type": "markdown", - "id": "6efc4968-35fd-42e3-ba62-d8e1557aa0d6", "metadata": {}, "source": [ - "## 💯 Step 4: Deploy the model to AWS Sagemaker Endpoints\n" - ] - }, - { - "cell_type": "markdown", - "id": "577aff86-bde9-48d4-9b52-209cfed9fd4e", - "metadata": {}, - "source": [ - "This is the final step to automate the deployment of the slated production model to a Sagemaker endpoint. The deployment pipelines handles the complexities of AWS interactions and ensures that the model, along with its full history and context, is transitioned into a live environment ready for use. Here again we use the Model Control Plane interface to query the Huggingface revision and use that information to push to Huggingface Hub.\n", - "\n", - "\"Pipelines\n" + "ZenML automatically links all artifacts to the `production` model version as well, including the predictions\n", + "that were returned in the pipeline. This completes the MLOps loop of training to inference:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "1513ab5f-de05-4344-9d2c-fedbfbd21ef0", + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 1\n", + "2 1\n", + "3 1\n", + "4 1\n", + "5 1\n", + "6 1\n", + "7 1\n", + "8 0\n", + "9 0\n", + "10 1\n", + "11 1\n", + "12 1\n", + "13 0\n", + "14 0\n", + "15 0\n", + "16 1\n", + "17 1\n", + "18 0\n", + "19 1\n", + "20 0\n", + "21 1\n", + "22 1\n", + "23 1\n", + "24 1\n", + "25 0\n", + "26 0\n", + "27 1\n", + "Name: series, dtype: int64" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "!zenml stack set hf-sagemaker-local" + "# Fetch production model\n", + "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", + "\n", + "# Get the predictions artifact\n", + "production_model_version.get_artifact(\"predictions\").load()" ] }, { @@ -1932,25 +1885,26 @@ "source": [ "## Congratulations!\n", "\n", - "You just built two ML pipelines! You trained two models, evaluated them against\n", + "You're a legit MLOps engineer now! You trained two models, evaluated them against\n", "a test set, registered the best one with the ZenML model control plane,\n", "and served some predictions. You also learned how to iterate on your models and\n", "data by using some of the ZenML utility abstractions. You saw how to view your\n", - "artifacts and stacks via the CLI as well as the ZenML Dashboard.\n", + "artifacts and stacks via the client as well as the ZenML Dashboard.\n", + "\n", + "## Further exploration\n", + "\n", + "This was just the tip of the iceberg of what ZenML can do; check out the [**docs**](https://docs.zenml.io/) to learn more\n", + "about the capabilities of ZenML. For example, you might want to:\n", "\n", - "And that is just the tip of the iceberg of what ZenML can do; check out the [**docs**](https://docs.zenml.io/) to learn more\n", - "about the capabilities of ZenML.\n", + "- Run the same pipeline on a cloud stack in production.\n", + "- Track your metrics in an experiment tracker like [MLflow]().\n", + "- Learn how to transition your code from this notebook setting to a production setting.\n", "\n", - "## What to do now\n", + "## What next?\n", "\n", "* If you have questions or feedback... join our [**Slack Community**](https://zenml.io/slack) and become part of the ZenML family!\n", - "* If you want to try ZenML in a real-world setting... check out the [ZenML Cloud](https://cloud.zenml.io/)." + "* If you want to quickly get started with ZenML, check out the [ZenML Cloud](https://zenml.io/cloud)." ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] } ], "metadata": { From f3ef39b88cc6c3f4c9b430d7d7d7b427fa78c74d Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 8 Dec 2023 10:30:10 +0100 Subject: [PATCH 12/48] new notebook --- template/run.ipynb | 1070 +++----------------------------------------- 1 file changed, 65 insertions(+), 1005 deletions(-) diff --git a/template/run.ipynb b/template/run.ipynb index e0ac72e..a5b4025 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -104,36 +104,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "081d5616", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[?25l\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/home/htahir1/workspace/zenml_io/template-starter/template.\n", - "\u001b[1;35mSetting the repo active workspace to 'default'.\u001b[0m\n", - "\u001b[33mSetting the repo active stack to default.\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mZenML repository initialized at \u001b[0m\n", - "\u001b[2;35m/home/htahir1/workspace/zenml_io/template-starter/\u001b[0m\u001b[2;95mtemplate.\u001b[0m\n", - "\u001b[2;32m⠋\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", - "\u001b[2;36m/home/htahir1/workspace/zenml_io/template-starter/template.\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/home/htahir1/workspace/zenml_io/template-starter/template.\n", - "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mThe local active stack was initialized to \u001b[0m\u001b[2;32m'default'\u001b[0m\u001b[2;36m. This local configuration \u001b[0m\n", - "\u001b[2;36mwill only take effect when you're running ZenML from the initialized repository \u001b[0m\n", - "\u001b[2;36mroot, or from a subdirectory. For more information on repositories and \u001b[0m\n", - "\u001b[2;36mconfigurations, please visit \u001b[0m\n", - "\u001b[2;4;94mhttps://docs.zenml.io/user-guide/starter-guide/understand-stacks.\u001b[0m\n", - "\u001b[?25l\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'default'\u001b[0m\n", - "\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to 'default'...t'...\u001b[0m\n", - "\u001b[1A\u001b[2K" - ] - } - ], + "outputs": [], "source": [ "# Initialize ZenML and set the default stack\n", "!zenml init\n", @@ -143,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -200,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -255,230 +229,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" - ] - }, - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...17.33184.602019.00.16220.66560.71190.26540.46010.118900
120.5717.77132.901326.00.084740.078640.08690.070170.18120.05667...23.41158.801956.00.12380.18660.24160.18600.27500.089020
219.6921.25130.001203.00.109600.159900.19740.127900.20690.05999...25.53152.501709.00.14440.42450.45040.24300.36130.087580
311.4220.3877.58386.10.142500.283900.24140.105200.25970.09744...26.5098.87567.70.20980.86630.68690.25750.66380.173000
420.2914.34135.101297.00.100300.132800.19800.104300.18090.05883...16.67152.201575.00.13740.20500.40000.16250.23640.076780
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In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "feature_engineering()" ] @@ -580,32 +310,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.294s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.398s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.529s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_08-09_19_58_112527\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.384s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "feature_engineering(test_size=0.3)" ] @@ -664,17 +371,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "feature_engineering-2023_12_08-09_19_58_112527\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "client = Client()\n", "run = client.get_pipeline(\"feature_engineering\").last_run\n", @@ -690,419 +389,18 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'preprocess_pipeline': ArtifactResponse(id=UUID('31d08c04-22f0-44ae-89ad-d0530114a043'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 8, 9, 19, 59, 419488), updated=datetime.datetime(2023, 12, 8, 9, 19, 59, 419491), user=UserResponse(id=UUID('d47f2134-eb09-41f8-9b76-4f00340c0d1b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 19, 40146), updated=datetime.datetime(2023, 12, 8, 9, 18, 19, 40148), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), version='2', uri='/home/htahir1/.config/zenml/local_stores/b640139f-5df3-4de5-8a01-0d16f0589628/data_preprocessor/preprocess_pipeline/ed50c2a3-71b1-4ef7-a590-8b4b717732e3', type=), metadata=None, name='preprocess_pipeline'),\n", - " 'dataset_trn': ArtifactResponse(id=UUID('5f934376-6357-4f8a-a813-b7996a379ef1'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 8, 9, 19, 59, 64014), updated=datetime.datetime(2023, 12, 8, 9, 19, 59, 64016), user=UserResponse(id=UUID('d47f2134-eb09-41f8-9b76-4f00340c0d1b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 19, 40146), updated=datetime.datetime(2023, 12, 8, 9, 18, 19, 40148), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), version='2', uri='/home/htahir1/.config/zenml/local_stores/b640139f-5df3-4de5-8a01-0d16f0589628/data_preprocessor/dataset_trn/ed50c2a3-71b1-4ef7-a590-8b4b717732e3', type=), metadata=None, name='dataset_trn'),\n", - " 'dataset_tst': ArtifactResponse(id=UUID('e4c66672-7e64-401e-b3e5-f9a8ab8aa6d5'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 8, 9, 19, 59, 254549), updated=datetime.datetime(2023, 12, 8, 9, 19, 59, 254551), user=UserResponse(id=UUID('d47f2134-eb09-41f8-9b76-4f00340c0d1b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 19, 40146), updated=datetime.datetime(2023, 12, 8, 9, 18, 19, 40148), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), version='2', uri='/home/htahir1/.config/zenml/local_stores/b640139f-5df3-4de5-8a01-0d16f0589628/data_preprocessor/dataset_tst/ed50c2a3-71b1-4ef7-a590-8b4b717732e3', type=), metadata=None, name='dataset_tst')}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "run.steps[\"data_preprocessor\"].outputs" ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
42814.53019.3494.25659.70.083880.078000.0881700.029250.14730.05746...28.39108.10830.50.108900.264900.377900.095940.24710.074631
37019.55023.21128.901174.00.101000.131800.1856000.102100.19890.05884...30.44142.001313.00.125100.241400.382900.182500.25760.076020
5714.25022.1596.42645.70.104900.200800.2135000.086530.19490.07292...29.51119.10959.50.164000.624700.692200.178500.28440.113200
33211.66017.0773.70421.00.075610.036300.0083060.011620.16710.05731...19.7483.61542.50.099580.064760.030460.042620.27310.068251
2418.61020.25122.101094.00.094400.106600.1490000.077310.16970.05699...27.26139.901403.00.133800.211700.344600.149000.23410.074210
..................................................................
7118.05016.15120.201006.00.106500.214600.1684000.108000.21520.06673...18.91150.101610.00.147800.563400.378600.210200.37510.110800
1068.72615.8355.84230.90.115000.082010.0413200.019240.16490.07633...19.6264.48284.40.172400.236400.245600.105000.29260.101701
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43513.38030.7286.34557.20.092450.074260.0281900.032640.13750.06016...41.6196.69705.60.117200.142100.070030.077630.21960.076751
1029.77716.9962.50290.20.103700.084040.0433400.017780.15840.07065...21.4771.68367.00.146700.176500.130000.053340.25330.084681
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378 rows × 31 columns

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" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "428 14.530 19.34 94.25 659.7 0.08388 \n", - "370 19.550 23.21 128.90 1174.0 0.10100 \n", - "57 14.250 22.15 96.42 645.7 0.10490 \n", - "332 11.660 17.07 73.70 421.0 0.07561 \n", - "24 18.610 20.25 122.10 1094.0 0.09440 \n", - ".. ... ... ... ... ... \n", - "71 18.050 16.15 120.20 1006.0 0.10650 \n", - "106 8.726 15.83 55.84 230.9 0.11500 \n", - "270 16.240 18.77 108.80 805.1 0.10660 \n", - "435 13.380 30.72 86.34 557.2 0.09245 \n", - "102 9.777 16.99 62.50 290.2 0.10370 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "428 0.07800 0.088170 0.02925 0.1473 \n", - "370 0.13180 0.185600 0.10210 0.1989 \n", - "57 0.20080 0.213500 0.08653 0.1949 \n", - "332 0.03630 0.008306 0.01162 0.1671 \n", - "24 0.10660 0.149000 0.07731 0.1697 \n", - ".. ... ... ... ... \n", - "71 0.21460 0.168400 0.10800 0.2152 \n", - "106 0.08201 0.041320 0.01924 0.1649 \n", - "270 0.18020 0.194800 0.09052 0.1876 \n", - "435 0.07426 0.028190 0.03264 0.1375 \n", - "102 0.08404 0.043340 0.01778 0.1584 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "428 0.05746 ... 28.39 108.10 830.5 \n", - "370 0.05884 ... 30.44 142.00 1313.0 \n", - "57 0.07292 ... 29.51 119.10 959.5 \n", - "332 0.05731 ... 19.74 83.61 542.5 \n", - "24 0.05699 ... 27.26 139.90 1403.0 \n", - ".. ... ... ... ... ... \n", - "71 0.06673 ... 18.91 150.10 1610.0 \n", - "106 0.07633 ... 19.62 64.48 284.4 \n", - "270 0.06684 ... 25.09 126.90 1031.0 \n", - "435 0.06016 ... 41.61 96.69 705.6 \n", - "102 0.07065 ... 21.47 71.68 367.0 \n", - "\n", - " worst smoothness worst compactness worst concavity \\\n", - "428 0.10890 0.26490 0.37790 \n", - "370 0.12510 0.24140 0.38290 \n", - "57 0.16400 0.62470 0.69220 \n", - "332 0.09958 0.06476 0.03046 \n", - "24 0.13380 0.21170 0.34460 \n", - ".. ... ... ... \n", - "71 0.14780 0.56340 0.37860 \n", - "106 0.17240 0.23640 0.24560 \n", - "270 0.13650 0.47060 0.50260 \n", - "435 0.11720 0.14210 0.07003 \n", - "102 0.14670 0.17650 0.13000 \n", - "\n", - " worst concave points worst symmetry worst fractal dimension target \n", - "428 0.09594 0.2471 0.07463 1 \n", - "370 0.18250 0.2576 0.07602 0 \n", - "57 0.17850 0.2844 0.11320 0 \n", - "332 0.04262 0.2731 0.06825 1 \n", - "24 0.14900 0.2341 0.07421 0 \n", - ".. ... ... ... ... \n", - "71 0.21020 0.3751 0.11080 0 \n", - "106 0.10500 0.2926 0.10170 1 \n", - "270 0.17320 0.2770 0.10630 0 \n", - "435 0.07763 0.2196 0.07675 1 \n", - "102 0.05334 0.2533 0.08468 1 \n", - "\n", - "[378 rows x 31 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Read one of the datasets. This is the one with a 0.3 test split\n", "run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].load()" @@ -1119,21 +417,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'ArtifactResponse' object has no attribute 'versions'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 33\u001b[0m line \u001b[0;36m5\n\u001b[1;32m 2\u001b[0m dataset_tst_artifact \u001b[39m=\u001b[39m client\u001b[39m.\u001b[39mget_artifact(\u001b[39m\"\u001b[39m\u001b[39mdataset_tst\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 3\u001b[0m preprocessing_pipeline_artifact \u001b[39m=\u001b[39m client\u001b[39m.\u001b[39mget_artifact(\u001b[39m\"\u001b[39m\u001b[39mpreprocess_pipeline\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThe training set has \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(dataset_trn_artifact\u001b[39m.\u001b[39mversions)\u001b[39m}\u001b[39;00m\u001b[39m records, and the test set has \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlen\u001b[39m(dataset_tst_artifact\u001b[39m.\u001b[39mversions)\u001b[39m}\u001b[39;00m\u001b[39m records.\u001b[39m\u001b[39m\"\u001b[39m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'ArtifactResponse' object has no attribute 'versions'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dataset_trn_artifact = client.get_artifact(\"dataset_trn\")\n", "dataset_tst_artifact = client.get_artifact(\"dataset_tst\")\n", @@ -1152,21 +438,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'ArtifactResponse' object has no attribute 'latest_version'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/home/htahir1/workspace/zenml_io/template-starter/template/run.ipynb Cell 35\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m \u001b[39m# Choose the last version\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m dataset_trn_artifact_version \u001b[39m=\u001b[39m dataset_trn_artifact\u001b[39m.\u001b[39;49mlatest_version\n\u001b[1;32m 3\u001b[0m dataset_tst_artifact_version \u001b[39m=\u001b[39m dataset_tst_artifact\u001b[39m.\u001b[39mlatest_version\n\u001b[1;32m 5\u001b[0m \u001b[39m# Load it again to verify\u001b[39;00m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'ArtifactResponse' object has no attribute 'latest_version'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Choose the last version\n", "dataset_trn_artifact_version = dataset_trn_artifact.latest_version\n", @@ -1208,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1266,7 +540,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1313,34 +587,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.310s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", - "\u001b[1;35mTest accuracy=95.71%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.140s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_08-09_21_18_513475\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.623s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Use a random forest model with the chosen datasets.\n", "# We need to pass the ID's of the datasets into the function\n", @@ -1355,34 +604,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.152s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=57.94%\u001b[0m\n", - "\u001b[1;35mTest accuracy=53.37%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.207s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_08-09_21_20_671807\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.519s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Use a SGD classifier\n", "training(model_type=\"sgd\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)\n", @@ -1401,20 +625,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# The evaluator returns a float value with the accuracy\n", "rf_run.steps[\"model_evaluator\"].output.load() > sgd_run.steps[\"model_evaluator\"].output.load()" @@ -1440,7 +653,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1457,32 +670,9 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 2)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m was created implicitly.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36msgd\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_08-09_21_36_798853\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.252s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Let's train the SGD model and set the version name to \"sgd\"\n", "pipeline_settings[\"model_version\"].version = \"sgd\"\n", @@ -1497,31 +687,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36mrf\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_08-09_21_38_756106\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.238s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Let's train the RF model and set the version name to \"rf\"\n", "pipeline_settings[\"model_version\"].version = \"rf\"\n", @@ -1544,28 +712,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "name='breast_cancer_classifier' license='Apache 2.0' description='A breast cancer classifier' audience=None use_cases=None limitations=None trade_offs=None ethics=None id=UUID('22a5cae2-b9f8-4289-9773-74b848436c93') created=datetime.datetime(2023, 12, 8, 9, 21, 36, 672757) updated=datetime.datetime(2023, 12, 8, 9, 21, 36, 672759) missing_permissions=False user=UserResponse(id=UUID('d47f2134-eb09-41f8-9b76-4f00340c0d1b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 19, 40146), updated=datetime.datetime(2023, 12, 8, 9, 18, 19, 40148), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default') workspace=WorkspaceResponse(id=UUID('791ceb8f-8439-47cd-a76d-c61746290ce9'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 12, 8, 9, 18, 18, 797852), updated=datetime.datetime(2023, 12, 8, 9, 18, 18, 797854)), metadata=None, name='default') tags=[TagResponseModel(id=UUID('9adb1133-3932-4180-aa8a-8c77c3ea30af'), created=datetime.datetime(2023, 12, 8, 9, 21, 36, 674655), updated=datetime.datetime(2023, 12, 8, 9, 21, 36, 674656), missing_permissions=False, name='breast_cancer', color=, tagged_count=1), TagResponseModel(id=UUID('36b58849-a05e-4ceb-8c8f-b596878b09a5'), created=datetime.datetime(2023, 12, 8, 9, 21, 36, 697963), updated=datetime.datetime(2023, 12, 8, 9, 21, 36, 697965), missing_permissions=False, name='classifier', color=, tagged_count=1)] latest_version='rf'\n", - "Model breast_cancer_classifier has 2 versions\n" - ] - }, - { - "data": { - "text/plain": [ - "('sgd', 'rf')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", "print(zenml_model)\n", @@ -1587,23 +736,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "RandomForestClassifier()" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Let's load the RF version\n", "rf_zenml_model_version = client.get_model_version(\"breast_cancer_classifier\", \"rf\")\n", @@ -1639,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1685,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1715,7 +850,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1748,17 +883,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "pipeline_settings = {}\n", "\n", @@ -1774,37 +901,9 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36minference_predict\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36minference-2023_12_08-09_24_59_254310\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.265s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", @@ -1828,48 +927,9 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 1\n", - "2 1\n", - "3 1\n", - "4 1\n", - "5 1\n", - "6 1\n", - "7 1\n", - "8 0\n", - "9 0\n", - "10 1\n", - "11 1\n", - "12 1\n", - "13 0\n", - "14 0\n", - "15 0\n", - "16 1\n", - "17 1\n", - "18 0\n", - "19 1\n", - "20 0\n", - "21 1\n", - "22 1\n", - "23 1\n", - "24 1\n", - "25 0\n", - "26 0\n", - "27 1\n", - "Name: series, dtype: int64" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Fetch production model\n", "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", From 639ad9e71c985b529b270d945de359825293dc2c Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 8 Dec 2023 17:34:37 +0100 Subject: [PATCH 13/48] new notebook --- template/configs/feature_engineering.yaml | 7 ---- template/pipelines/__init__.py | 6 +-- template/pipelines/feature_engineering.py | 6 ++- template/pipelines/inference.py | 47 ++++++++++----------- template/pipelines/training.py | 6 +-- template/run.ipynb | 50 ++++++++++++++++++----- template/run.py | 20 ++++----- template/steps/data_loader.py | 6 +-- template/steps/data_preprocessor.py | 12 +++--- template/steps/inference_predict.py | 8 ++-- 10 files changed, 92 insertions(+), 76 deletions(-) diff --git a/template/configs/feature_engineering.yaml b/template/configs/feature_engineering.yaml index daa91a1..b0e559f 100644 --- a/template/configs/feature_engineering.yaml +++ b/template/configs/feature_engineering.yaml @@ -3,10 +3,3 @@ settings: docker: required_integrations: - sklearn - -# configuration of the Model Control Plane -model_version: - name: breast_cancer_classifier - license: Apache 2.0 - description: Classification of Breast Cancer Dataset. - tags: ["classification", "sklearn"] diff --git a/template/pipelines/__init__.py b/template/pipelines/__init__.py index 9c07547..16ae363 100644 --- a/template/pipelines/__init__.py +++ b/template/pipelines/__init__.py @@ -1,5 +1,5 @@ # {% include 'template/license_header' %} -from .feature_engineering import _feature_engineering -from .inference import _inference -from .training import _training +from .feature_engineering import feature_engineering +from .inference import inference +from .training import training diff --git a/template/pipelines/feature_engineering.py b/template/pipelines/feature_engineering.py index 5465382..bd6946b 100644 --- a/template/pipelines/feature_engineering.py +++ b/template/pipelines/feature_engineering.py @@ -15,7 +15,7 @@ @pipeline -def _feature_engineering( +def feature_engineering( test_size: float = 0.2, drop_na: Optional[bool] = None, normalize: Optional[bool] = None, @@ -38,7 +38,8 @@ def _feature_engineering( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. - raw_data = data_loader(random_state=random.randint(0, 100), target=target) + random_state = random.randint(0, 100) + raw_data = data_loader(random_state=random_state, target=target) dataset_trn, dataset_tst = data_splitter( dataset=raw_data, test_size=test_size, @@ -50,5 +51,6 @@ def _feature_engineering( normalize=normalize, drop_columns=drop_columns, target=target, + random_state=random_state, ) return dataset_trn, dataset_tst diff --git a/template/pipelines/inference.py b/template/pipelines/inference.py index 798f8b2..da937f6 100644 --- a/template/pipelines/inference.py +++ b/template/pipelines/inference.py @@ -1,5 +1,6 @@ # {% include 'template/license_header' %} +from uuid import UUID from typing import List, Optional from steps import ( @@ -7,7 +8,7 @@ inference_preprocessor, inference_predict, ) -from zenml import pipeline, ExternalArtifact +from zenml import pipeline, ArtifactVersionResponse, get_pipeline_context from zenml.client import Client from zenml.logger import get_logger @@ -15,38 +16,38 @@ @pipeline -def _inference( - test_size: float = 0.2, - drop_na: Optional[bool] = None, - normalize: Optional[bool] = None, - drop_columns: Optional[List[str]] = None, -): +def inference(): """ - Model training pipeline. - - This is a pipeline that loads the data, processes it and splits - it into train and test sets, then search for best hyperparameters, - trains and evaluates a model. - - Args: - test_size: Size of holdout set for training 0.0..1.0 - drop_na: If `True` NA values will be removed from dataset - normalize: If `True` dataset will be normalized with MinMaxScaler - drop_columns: List of columns to drop from dataset + Model inference pipeline. + + This is a pipeline that loads the inference data, processes it with + the same preprocessing pipeline used in training, and runs inference + with the trained model. """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Link all the steps together by calling them and passing the output - # of one step as the input of the next step. client = Client() - random_state = client.get_artifact("dataset").run_metadata["random_state"].value - target = "target" + + # Get the production model artifact + model: ArtifactVersionResponse = get_pipeline_context().model_version.get_artifact("model") + + # Get the preprocess pipeline artifact associated with this version + preprocess_pipeline: ArtifactVersionResponse = get_pipeline_context().model_version.get_artifact("preprocess_pipeline") + + # Use the metadata of feature engineering pipeline artifact + # to get the random state and target column + random_state = client.get_artifact_version(preprocess_pipeline.id).run_metadata["random_state"].value + target = client.get_artifact_version(preprocess_pipeline.id).run_metadata['target'].value + + # Link all the steps together by calling them and passing the output + # of one step as the input of the next step. df_inference = data_loader(random_state=random_state, is_inference=True) df_inference = inference_preprocessor( dataset_inf=df_inference, - preprocess_pipeline=ExternalArtifact(name="preprocess_pipeline"), + preprocess_pipeline=preprocess_pipeline, target=target, ) inference_predict( + model=model, dataset_inf=df_inference, ) ### END CODE HERE ### diff --git a/template/pipelines/training.py b/template/pipelines/training.py index 20df9ef..a82760d 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -8,14 +8,14 @@ from zenml.logger import get_logger from pipelines import ( - _feature_engineering, + feature_engineering, ) logger = get_logger(__name__) @pipeline -def _training( +def training( train_dataset_id: Optional[UUID] = None, test_dataset_id: Optional[UUID] = None, min_train_accuracy: float = 0.0, @@ -40,7 +40,7 @@ def _training( # Execute Feature Engineering Pipeline if train_dataset_id is None or test_dataset_id is None: - dataset_trn, dataset_tst = _feature_engineering() + dataset_trn, dataset_tst = feature_engineering() else: dataset_trn = ExternalArtifact(id=train_dataset_id) dataset_tst = ExternalArtifact(id=test_dataset_id) diff --git a/template/run.ipynb b/template/run.ipynb index a5b4025..f17a725 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -442,9 +442,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Choose the last version\n", - "dataset_trn_artifact_version = dataset_trn_artifact.latest_version\n", - "dataset_tst_artifact_version = dataset_tst_artifact.latest_version\n", + "# Choose the first version for now\n", + "dataset_trn_artifact_version = dataset_trn_artifact.versions['1']\n", + "dataset_tst_artifact_version = dataset_tst_artifact.versions['1']\n", "\n", "# Load it again to verify\n", "dataset_trn_artifact_version.load()" @@ -595,8 +595,8 @@ "# We need to pass the ID's of the datasets into the function\n", "training(\n", " model_type=\"rf\",\n", - " train_dataset_id=dataset_trn_artifact.id, # dataset_trn_artifact_version.id,\n", - " test_dataset_id=dataset_tst_artifact.id # dataset_tst_artifact_version.id\n", + " train_dataset_id=dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact_version.id\n", ")\n", "\n", "rf_run = client.get_pipeline(\"training\").last_run" @@ -609,7 +609,11 @@ "outputs": [], "source": [ "# Use a SGD classifier\n", - "training(model_type=\"sgd\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)\n", + "training(\n", + " model_type=\"sgd\",\n", + " train_dataset_id=dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact_version.id\n", + ")\n", "\n", "sgd_run = client.get_pipeline(\"training\").last_run" ] @@ -682,7 +686,11 @@ "training_configured = training.with_options(**pipeline_settings)\n", "\n", "# We can now run this as usual\n", - "training_configured(model_type=\"sgd\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)" + "training_configured(\n", + " model_type=\"sgd\",\n", + " train_dataset_id=dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact_version.id\n", + ")" ] }, { @@ -699,7 +707,11 @@ "training_configured = training.with_options(**pipeline_settings)\n", "\n", "# Let's run it again to make sure we have two versions\n", - "training_configured(model_type=\"rf\", train_dataset_id=dataset_trn_artifact.id, test_dataset_id=dataset_tst_artifact.id)" + "training_configured(\n", + " model_type=\"rf\",\n", + " train_dataset_id=dataset_trn_artifact_version.id,\n", + " test_dataset_id=dataset_tst_artifact_version.id\n", + ")" ] }, { @@ -753,7 +765,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you are a ZenML Cloud user, you can see all of this visualized in the dashboard:\n", + "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", "\n", "\"Model" ] @@ -792,6 +804,13 @@ "more insight into that sort of flow!" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Model" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -857,8 +876,8 @@ "@pipeline\n", "def inference(preprocess_pipeline_id: UUID):\n", " \"\"\"Model batch inference pipeline\"\"\"\n", - " random_state = client.get_artifact(\"dataset\").run_metadata[\"random_state\"].value\n", - " target = client.get_artifact(\"dataset_trn\").run_metadata['target'].value\n", + " random_state = client.get_artifact_version(id=preprocess_pipeline_id).metadata[\"random_state\"].value\n", + " target = client.get_artifact_version(id=preprocess_pipeline_id).run_metadata['target'].value\n", " df_inference = data_loader(\n", " random_state=random_state, is_inference=True\n", " )\n", @@ -938,6 +957,15 @@ "production_model_version.get_artifact(\"predictions\").load()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also see all predictions ever created as a complete history in the dashboard:\n", + "\n", + "\"Model" + ] + }, { "cell_type": "markdown", "id": "594ee4fc-f102-4b99-bdc3-2f1670c87679", diff --git a/template/run.py b/template/run.py index 44f0798..ea2452e 100644 --- a/template/run.py +++ b/template/run.py @@ -5,9 +5,9 @@ import click from pipelines import ( - _feature_engineering, - _inference, - _training, + feature_engineering, + inference, + training, ) from zenml.client import Client from zenml.logger import get_logger @@ -115,7 +115,7 @@ def main( config_folder, "feature_engineering.yaml" ) run_args_feature = {} - _feature_engineering.with_options(**pipeline_args)(**run_args_feature) + feature_engineering.with_options(**pipeline_args)(**run_args_feature) logger.info("Feature Engineering pipeline finished successfully!") # Execute Training Pipeline @@ -133,25 +133,25 @@ def main( and test_dataset_version_name is not None ) client = Client() - train_dataset_artifact = client.get_artifact( + train_dataset_artifact_version = client.get_artifact_version( train_dataset_name, train_dataset_version_name ) # If train dataset is specified, test dataset must be specified - test_dataset_artifact = client.get_artifact( + test_dataset_artifact_version = client.get_artifact_version( test_dataset_name, test_dataset_version_name ) # Use versioned artifacts - run_args_train["train_dataset_id"] = train_dataset_artifact.id - run_args_train["test_dataset_id"] = test_dataset_artifact.id + run_args_train["train_dataset_id"] = train_dataset_artifact_version.id + run_args_train["test_dataset_id"] = test_dataset_artifact_version.id - _training.with_options(**pipeline_args)(**run_args_train) + training.with_options(**pipeline_args)(**run_args_train) logger.info("Training pipeline finished successfully!") if inference_pipeline: pipeline_args = {} pipeline_args["config_path"] = os.path.join(config_folder, "inference.yaml") run_args_inference = {} - _inference.with_options(**pipeline_args)(**run_args_inference) + inference.with_options(**pipeline_args)(**run_args_inference) logger.info("Inference pipeline finished successfully!") diff --git a/template/steps/data_loader.py b/template/steps/data_loader.py index cc6df58..941cbe2 100644 --- a/template/steps/data_loader.py +++ b/template/steps/data_loader.py @@ -3,7 +3,7 @@ import pandas as pd from sklearn.datasets import load_breast_cancer from typing_extensions import Annotated -from zenml import log_artifact_metadata, step +from zenml import step from zenml.logger import get_logger logger = get_logger(__name__) @@ -45,9 +45,5 @@ def data_loader( dataset.drop(inference_subset.index, inplace=True) dataset.reset_index(drop=True, inplace=True) logger.info(f"Dataset with {len(dataset)} records loaded!") - - # Recording metadata for this dataset - log_artifact_metadata(metadata={"random_state": random_state, target: target}) - ### YOUR CODE ENDS HERE ### return dataset diff --git a/template/steps/data_preprocessor.py b/template/steps/data_preprocessor.py index 34b3aeb..78a0c72 100644 --- a/template/steps/data_preprocessor.py +++ b/template/steps/data_preprocessor.py @@ -14,6 +14,7 @@ def data_preprocessor( dataset_trn: pd.DataFrame, dataset_tst: pd.DataFrame, + random_state: int, drop_na: Optional[bool] = None, normalize: Optional[bool] = None, drop_columns: Optional[List[str]] = None, @@ -45,6 +46,7 @@ def data_preprocessor( drop_na: If `True` all NA rows will be dropped. normalize: If `True` all numeric fields will be normalized. drop_columns: List of column names to drop. + random_state: Random state for sampling. Returns: The processed datasets (dataset_trn, dataset_tst) and fitted `Pipeline` object. @@ -64,14 +66,10 @@ def data_preprocessor( dataset_trn = preprocess_pipeline.fit_transform(dataset_trn) dataset_tst = preprocess_pipeline.transform(dataset_tst) - # Log metadata of target to both datasets + # Log metadata so we can load it in the inference pipeline log_artifact_metadata( - artifact_name="dataset_trn", - metadata={"target": target}, - ) - log_artifact_metadata( - artifact_name="dataset_tst", - metadata={"target": target}, + artifact_name="preprocess_pipeline", + metadata={"random_state": random_state, "target": target}, ) ### YOUR CODE ENDS HERE ### diff --git a/template/steps/inference_predict.py b/template/steps/inference_predict.py index c29b352..9e05e21 100644 --- a/template/steps/inference_predict.py +++ b/template/steps/inference_predict.py @@ -15,7 +15,7 @@ # limitations under the License. # - +from typing import Any import pandas as pd from typing_extensions import Annotated from zenml import get_step_context, step @@ -26,6 +26,7 @@ @step def inference_predict( + model: Any, dataset_inf: pd.DataFrame, ) -> Annotated[pd.Series, "predictions"]: """Predictions step. @@ -47,11 +48,8 @@ def inference_predict( The predictions as pandas series """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - model_version = get_step_context().model_version - # run prediction from memory - predictor = model_version.load_artifact("model") - predictions = predictor.predict(dataset_inf) + predictions = model.predict(dataset_inf) predictions = pd.Series(predictions, name="predicted") ### YOUR CODE ENDS HERE ### From cf08f35139469088e203aa48013ef521ccc21a1a Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Tue, 12 Dec 2023 13:25:41 +0100 Subject: [PATCH 14/48] Images --- template/assets/cloud_mcp.png | Bin 0 -> 237746 bytes template/assets/cloud_mcp.png:Zone.Identifier | 0 template/assets/cloud_mcp_predictions.png | Bin 0 -> 320242 bytes template/assets/cloud_mcp_screenshot.png | Bin 0 -> 302436 bytes .../assets/feature_engineering_pipeline.png | Bin 0 -> 212830 bytes ...e_engineering_pipeline.png:Zone.Identifier | 0 template/assets/inference_pipeline.png | Bin 0 -> 257666 bytes .../inference_pipeline.png:Zone.Identifier | 0 template/assets/pipeline_overview.png | Bin 0 -> 407607 bytes .../pipeline_overview.png:Zone.Identifier | 0 template/assets/training_pipeline.png | Bin 0 -> 70547 bytes .../training_pipeline.png:Zone.Identifier | 0 template/run.ipynb | 1453 +++++++++++++++-- 13 files changed, 1358 insertions(+), 95 deletions(-) create mode 100644 template/assets/cloud_mcp.png create mode 100644 template/assets/cloud_mcp.png:Zone.Identifier create mode 100644 template/assets/cloud_mcp_predictions.png create mode 100644 template/assets/cloud_mcp_screenshot.png create mode 100644 template/assets/feature_engineering_pipeline.png create mode 100644 template/assets/feature_engineering_pipeline.png:Zone.Identifier create 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0000000..e69de29 diff --git a/template/run.ipynb b/template/run.ipynb index f17a725..7227876 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -16,7 +16,7 @@ "\n", "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n", "\n", - "\"Pipelines" + "\"Pipelines" ] }, { @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -169,12 +169,12 @@ "your training. ZenML mostly gets out of your way when you're writing your Python\n", "code, as you'll see from the following cell.\n", "\n", - "\"Feature" + "\"Feature" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -229,9 +229,230 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "

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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...17.33184.602019.00.16220.66560.71190.26540.46010.118900
120.5717.77132.901326.00.084740.078640.08690.070170.18120.05667...23.41158.801956.00.12380.18660.24160.18600.27500.089020
219.6921.25130.001203.00.109600.159900.19740.127900.20690.05999...25.53152.501709.00.14440.42450.45040.24300.36130.087580
311.4220.3877.58386.10.142500.283900.24140.105200.25970.09744...26.5098.87567.70.20980.86630.68690.25750.66380.173000
420.2914.34135.101297.00.100300.132800.19800.104300.18090.05883...16.67152.201575.00.13740.20500.40000.16250.23640.076780
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5 rows × 31 columns

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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.3001 0.14710 0.2419 \n", + "1 0.07864 0.0869 0.07017 0.1812 \n", + "2 0.15990 0.1974 0.12790 0.2069 \n", + "3 0.28390 0.2414 0.10520 0.2597 \n", + "4 0.13280 0.1980 0.10430 0.1809 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "0 0.07871 ... 17.33 184.60 2019.0 \n", + "1 0.05667 ... 23.41 158.80 1956.0 \n", + "2 0.05999 ... 25.53 152.50 1709.0 \n", + "3 0.09744 ... 26.50 98.87 567.7 \n", + "4 0.05883 ... 16.67 152.20 1575.0 \n", + "\n", + " worst smoothness worst compactness worst concavity worst concave points \\\n", + "0 0.1622 0.6656 0.7119 0.2654 \n", + "1 0.1238 0.1866 0.2416 0.1860 \n", + "2 0.1444 0.4245 0.4504 0.2430 \n", + "3 0.2098 0.8663 0.6869 0.2575 \n", + "4 0.1374 0.2050 0.4000 0.1625 \n", + "\n", + " worst symmetry worst fractal dimension target \n", + "0 0.4601 0.11890 0 \n", + "1 0.2750 0.08902 0 \n", + "2 0.3613 0.08758 0 \n", + "3 0.6638 0.17300 0 \n", + "4 0.2364 0.07678 0 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = data_loader_simplified(random_state=42)\n", "df.head()" @@ -251,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "b50a9537", "metadata": {}, "outputs": [], @@ -267,7 +488,8 @@ " \"\"\"Feature engineering pipeline.\"\"\"\n", " # Link all the steps together by calling them and passing the output\n", " # of one step as the input of the next step.\n", - " raw_data = data_loader(random_state=random.randint(0, 100), target=target)\n", + " random_state = random.randint(0, 100)\n", + " raw_data = data_loader(random_state=random_state, target=target)\n", " dataset_trn, dataset_tst = data_splitter(\n", " dataset=raw_data,\n", " test_size=test_size,\n", @@ -279,9 +501,8 @@ " normalize=normalize,\n", " drop_columns=drop_columns,\n", " target=target,\n", - " )\n", - " \n", - " return dataset_trn, dataset_tst" + " random_state=random_state,\n", + " )" ] }, { @@ -294,9 +515,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 5)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.904s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.611s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m13.879s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_12-12_12_46_332864\u001b[1;35m has finished in \u001b[0m\u001b[1;36m23.070s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/088b8ebf-5f03-471d-975f-129e56e3f193/runs/05cb3ef9-007d-4ecf-8bdb-e68d63aafbc6/dag\u001b[0m\n" + ] + } + ], "source": [ "feature_engineering()" ] @@ -310,9 +554,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 6)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.851s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.672s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.927s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_12-12_13_19_797666\u001b[1;35m has finished in \u001b[0m\u001b[1;36m14.861s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/985306fd-51ce-4204-96ee-079e3f57ddb5/runs/07999a33-49ec-4997-87cd-0bece20398cd/dag\u001b[0m\n" + ] + } + ], "source": [ "feature_engineering(test_size=0.3)" ] @@ -355,13 +622,6 @@ " !zenml up" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Feature" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -371,9 +631,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature_engineering-2023_12_12-12_13_19_797666\n" + ] + } + ], "source": [ "client = Client()\n", "run = client.get_pipeline(\"feature_engineering\").last_run\n", @@ -389,18 +657,419 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'preprocess_pipeline': ArtifactVersionResponse(id=UUID('7a085583-a612-4195-9835-176048dabc16'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 12, 15, 4, 26), updated=datetime.datetime(2023, 12, 12, 15, 4, 26), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 12, 14, 17, 15), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), artifact=ArtifactResponse(id=UUID('428da721-89f8-4d1d-93b7-0ad1108803c7'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 12, 10, 29, 43), updated=datetime.datetime(2023, 12, 12, 10, 29, 43)), metadata=None, name='preprocess_pipeline'), version='108', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/preprocess_pipeline/f5740f18-a486-41d1-8bca-f59f4fcd9e12', type=, materializer=Source(module='zenml.integrations.sklearn.materializers.sklearn_materializer', attribute='SklearnMaterializer', type=), data_type=Source(module='sklearn.pipeline', attribute='Pipeline', type=, version='1.2.2', package_name='scikit-learn')), metadata=None),\n", + " 'dataset_trn': ArtifactVersionResponse(id=UUID('813c995f-42f8-456a-8ad7-b918cef567e7'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 12, 15, 4, 23), updated=datetime.datetime(2023, 12, 12, 15, 4, 23), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 12, 14, 17, 15), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), artifact=ArtifactResponse(id=UUID('2f71c5a3-333a-4aa0-b5b4-341c26ee7f77'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 12, 10, 29, 43), updated=datetime.datetime(2023, 12, 12, 10, 29, 43)), metadata=None, name='dataset_trn'), version='110', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_trn/f5740f18-a486-41d1-8bca-f59f4fcd9e12', type=, materializer=Source(module='zenml.materializers.pandas_materializer', attribute='PandasMaterializer', type=), data_type=Source(module='pandas.core.frame', attribute='DataFrame', type=, version='2.0.3', package_name='pandas')), metadata=None),\n", + " 'dataset_tst': ArtifactVersionResponse(id=UUID('ef032295-f2fb-4f8e-b99e-4cf726373136'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 12, 15, 4, 24), updated=datetime.datetime(2023, 12, 12, 15, 4, 24), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 12, 14, 17, 15), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), artifact=ArtifactResponse(id=UUID('b4b9cd40-169b-4f56-867d-c20c7beb4a7d'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 12, 10, 29, 43), updated=datetime.datetime(2023, 12, 12, 10, 29, 43)), metadata=None, name='dataset_tst'), version='108', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_tst/f5740f18-a486-41d1-8bca-f59f4fcd9e12', type=, materializer=Source(module='zenml.materializers.pandas_materializer', attribute='PandasMaterializer', type=), data_type=Source(module='pandas.core.frame', attribute='DataFrame', type=, version='2.0.3', package_name='pandas')), metadata=None)}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "run.steps[\"data_preprocessor\"].outputs" ] }, { "cell_type": "code", - 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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
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37012.10017.7278.07446.20.102900.097580.047830.0332600.19370.06161...25.8088.33559.50.14320.17730.160300.062660.30490.070811
578.59820.9854.66221.80.124300.089630.030000.0092590.18280.06757...27.0462.06273.90.16390.16980.090010.027780.29720.077121
33211.95014.9677.23426.70.115800.120600.011710.0178700.24590.06581...17.7283.09496.20.12930.18850.031220.047660.31240.075901
2417.14016.40116.00912.70.118600.227600.222900.1401000.30400.07413...21.40152.401461.00.15450.39490.385300.255000.40660.105900
..................................................................
7116.07019.65104.10817.70.091680.084240.097690.0663800.17980.05391...24.56128.801223.00.15000.20450.282900.152000.26500.063870
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378 rows × 31 columns

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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "428 12.000 28.23 76.77 442.5 0.08437 \n", + "370 12.100 17.72 78.07 446.2 0.10290 \n", + "57 8.598 20.98 54.66 221.8 0.12430 \n", + "332 11.950 14.96 77.23 426.7 0.11580 \n", + "24 17.140 16.40 116.00 912.7 0.11860 \n", + ".. ... ... ... ... ... \n", + "71 16.070 19.65 104.10 817.7 0.09168 \n", + "106 10.510 20.19 68.64 334.2 0.11220 \n", + "270 16.240 18.77 108.80 805.1 0.10660 \n", + "435 9.755 28.20 61.68 290.9 0.07984 \n", + "102 11.340 21.26 72.48 396.5 0.08759 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "428 0.06450 0.04055 0.019450 0.1615 \n", + "370 0.09758 0.04783 0.033260 0.1937 \n", + "57 0.08963 0.03000 0.009259 0.1828 \n", + "332 0.12060 0.01171 0.017870 0.2459 \n", + "24 0.22760 0.22290 0.140100 0.3040 \n", + ".. ... ... ... ... \n", + "71 0.08424 0.09769 0.066380 0.1798 \n", + "106 0.13030 0.06476 0.030680 0.1922 \n", + "270 0.18020 0.19480 0.090520 0.1876 \n", + "435 0.04626 0.01541 0.010430 0.1621 \n", + "102 0.06575 0.05133 0.018990 0.1487 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "428 0.06104 ... 37.88 85.07 523.7 \n", + "370 0.06161 ... 25.80 88.33 559.5 \n", + "57 0.06757 ... 27.04 62.06 273.9 \n", + "332 0.06581 ... 17.72 83.09 496.2 \n", + "24 0.07413 ... 21.40 152.40 1461.0 \n", + ".. ... ... ... ... ... \n", + "71 0.05391 ... 24.56 128.80 1223.0 \n", + "106 0.07782 ... 22.75 72.62 374.4 \n", + "270 0.06684 ... 25.09 126.90 1031.0 \n", + "435 0.05952 ... 36.92 68.03 349.9 \n", + "102 0.06529 ... 29.15 83.99 518.1 \n", + "\n", + " worst smoothness worst compactness worst concavity \\\n", + "428 0.1208 0.1856 0.18110 \n", + "370 0.1432 0.1773 0.16030 \n", + "57 0.1639 0.1698 0.09001 \n", + "332 0.1293 0.1885 0.03122 \n", + "24 0.1545 0.3949 0.38530 \n", + ".. ... ... ... \n", + "71 0.1500 0.2045 0.28290 \n", + "106 0.1300 0.2049 0.12950 \n", + "270 0.1365 0.4706 0.50260 \n", + "435 0.1110 0.1109 0.07190 \n", + "102 0.1699 0.2196 0.31200 \n", + "\n", + " worst concave points worst symmetry worst fractal dimension target \n", + "428 0.07116 0.2447 0.08194 1 \n", + "370 0.06266 0.3049 0.07081 1 \n", + "57 0.02778 0.2972 0.07712 1 \n", + "332 0.04766 0.3124 0.07590 1 \n", + "24 0.25500 0.4066 0.10590 0 \n", + ".. ... ... ... ... \n", + "71 0.15200 0.2650 0.06387 0 \n", + "106 0.06136 0.2383 0.09026 1 \n", + "270 0.17320 0.2770 0.10630 0 \n", + "435 0.04866 0.2321 0.07211 1 \n", + "102 0.08278 0.2829 0.08832 1 \n", + "\n", + "[378 rows x 31 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Read one of the datasets. This is the one with a 0.3 test split\n", "run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].load()" @@ -417,15 +1086,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "dataset_trn_artifact = client.get_artifact(\"dataset_trn\")\n", - "dataset_tst_artifact = client.get_artifact(\"dataset_tst\")\n", - "preprocessing_pipeline_artifact = client.get_artifact(\"preprocess_pipeline\")\n", - "\n", - "f\"The training set has {len(dataset_trn_artifact.versions)} records, and the test set has {len(dataset_tst_artifact.versions)} records.\"" + "dataset_trn_artifact_version = client.get_artifact_version(run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].id)\n", + "dataset_tst_artifact_version = client.get_artifact_version(run.steps[\"data_preprocessor\"].outputs[\"dataset_tst\"].id)\n", + "preprocessing_pipeline_artifact_version = client.get_artifact_version(run.steps[\"data_preprocessor\"].outputs[\"preprocess_pipeline\"].id)" ] }, { @@ -433,20 +1100,404 @@ "metadata": {}, "source": [ "If you started with a fresh install, then you would have two versions corresponding\n", - "to the two pipelines that we ran above. Let's select the latter one to move forward: " + "to the two pipelines that we ran above. We can even load a artifact version in memory: " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Choose the first version for now\n", - "dataset_trn_artifact_version = dataset_trn_artifact.versions['1']\n", - "dataset_tst_artifact_version = dataset_tst_artifact.versions['1']\n", - "\n", - "# Load it again to verify\n", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
42812.00028.2376.77442.50.084370.064500.040550.0194500.16150.06104...37.8885.07523.70.12080.18560.181100.071160.24470.081941
37012.10017.7278.07446.20.102900.097580.047830.0332600.19370.06161...25.8088.33559.50.14320.17730.160300.062660.30490.070811
578.59820.9854.66221.80.124300.089630.030000.0092590.18280.06757...27.0462.06273.90.16390.16980.090010.027780.29720.077121
33211.95014.9677.23426.70.115800.120600.011710.0178700.24590.06581...17.7283.09496.20.12930.18850.031220.047660.31240.075901
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..................................................................
7116.07019.65104.10817.70.091680.084240.097690.0663800.17980.05391...24.56128.801223.00.15000.20450.282900.152000.26500.063870
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10211.34021.2672.48396.50.087590.065750.051330.0189900.14870.06529...29.1583.99518.10.16990.21960.312000.082780.28290.088321
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378 rows × 31 columns

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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "428 12.000 28.23 76.77 442.5 0.08437 \n", + "370 12.100 17.72 78.07 446.2 0.10290 \n", + "57 8.598 20.98 54.66 221.8 0.12430 \n", + "332 11.950 14.96 77.23 426.7 0.11580 \n", + "24 17.140 16.40 116.00 912.7 0.11860 \n", + ".. ... ... ... ... ... \n", + "71 16.070 19.65 104.10 817.7 0.09168 \n", + "106 10.510 20.19 68.64 334.2 0.11220 \n", + "270 16.240 18.77 108.80 805.1 0.10660 \n", + "435 9.755 28.20 61.68 290.9 0.07984 \n", + "102 11.340 21.26 72.48 396.5 0.08759 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "428 0.06450 0.04055 0.019450 0.1615 \n", + "370 0.09758 0.04783 0.033260 0.1937 \n", + "57 0.08963 0.03000 0.009259 0.1828 \n", + "332 0.12060 0.01171 0.017870 0.2459 \n", + "24 0.22760 0.22290 0.140100 0.3040 \n", + ".. ... ... ... ... \n", + "71 0.08424 0.09769 0.066380 0.1798 \n", + "106 0.13030 0.06476 0.030680 0.1922 \n", + "270 0.18020 0.19480 0.090520 0.1876 \n", + "435 0.04626 0.01541 0.010430 0.1621 \n", + "102 0.06575 0.05133 0.018990 0.1487 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "428 0.06104 ... 37.88 85.07 523.7 \n", + "370 0.06161 ... 25.80 88.33 559.5 \n", + "57 0.06757 ... 27.04 62.06 273.9 \n", + "332 0.06581 ... 17.72 83.09 496.2 \n", + "24 0.07413 ... 21.40 152.40 1461.0 \n", + ".. ... ... ... ... ... \n", + "71 0.05391 ... 24.56 128.80 1223.0 \n", + "106 0.07782 ... 22.75 72.62 374.4 \n", + "270 0.06684 ... 25.09 126.90 1031.0 \n", + "435 0.05952 ... 36.92 68.03 349.9 \n", + "102 0.06529 ... 29.15 83.99 518.1 \n", + "\n", + " worst smoothness worst compactness worst concavity \\\n", + "428 0.1208 0.1856 0.18110 \n", + "370 0.1432 0.1773 0.16030 \n", + "57 0.1639 0.1698 0.09001 \n", + "332 0.1293 0.1885 0.03122 \n", + "24 0.1545 0.3949 0.38530 \n", + ".. ... ... ... \n", + "71 0.1500 0.2045 0.28290 \n", + "106 0.1300 0.2049 0.12950 \n", + "270 0.1365 0.4706 0.50260 \n", + "435 0.1110 0.1109 0.07190 \n", + "102 0.1699 0.2196 0.31200 \n", + "\n", + " worst concave points worst symmetry worst fractal dimension target \n", + "428 0.07116 0.2447 0.08194 1 \n", + "370 0.06266 0.3049 0.07081 1 \n", + "57 0.02778 0.2972 0.07712 1 \n", + "332 0.04766 0.3124 0.07590 1 \n", + "24 0.25500 0.4066 0.10590 0 \n", + ".. ... ... ... ... \n", + "71 0.15200 0.2650 0.06387 0 \n", + "106 0.06136 0.2383 0.09026 1 \n", + "270 0.17320 0.2770 0.10630 0 \n", + "435 0.04866 0.2321 0.07211 1 \n", + "102 0.08278 0.2829 0.08832 1 \n", + "\n", + "[378 rows x 31 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load an artifact to verify you can fetch it\n", "dataset_trn_artifact_version.load()" ] }, @@ -477,12 +1528,12 @@ "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", "same data and then compare their performance.\n", "\n", - "\"Training" + "\"Training" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -540,7 +1591,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -587,9 +1638,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 25)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.516s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", + "\u001b[1;35mTest accuracy=94.48%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.021s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_12-12_17_04_641333\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.876s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/7a212a8b-b4f3-48d1-95bb-4eee0965121a/runs/bc921d13-8093-4da9-8f1f-a4b147a6a69d/dag\u001b[0m\n" + ] + } + ], "source": [ "# Use a random forest model with the chosen datasets.\n", "# We need to pass the ID's of the datasets into the function\n", @@ -604,12 +1680,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 26)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.153s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTrain accuracy=91.27%\u001b[0m\n", + "\u001b[1;35mTest accuracy=89.57%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.880s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_12-12_17_20_042737\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.156s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/baf2e634-0de1-4553-8607-14d0ab190cba/runs/549e8785-ee93-474a-a4b5-b091e7131b92/dag\u001b[0m\n" + ] + } + ], "source": [ "# Use a SGD classifier\n", - "training(\n", + "sgd_run = training(\n", " model_type=\"sgd\",\n", " train_dataset_id=dataset_trn_artifact_version.id,\n", " test_dataset_id=dataset_tst_artifact_version.id\n", @@ -629,9 +1730,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# The evaluator returns a float value with the accuracy\n", "rf_run.steps[\"model_evaluator\"].output.load() > sgd_run.steps[\"model_evaluator\"].output.load()" @@ -657,7 +1769,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -674,9 +1786,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 26)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m was created implicitly.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36msgd\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_12-12_17_38_803413\u001b[1;35m has finished in \u001b[0m\u001b[1;36m7.935s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/baf2e634-0de1-4553-8607-14d0ab190cba/runs/e63fe206-1334-4a1c-bb84-ba792ae00157/dag\u001b[0m\n" + ] + } + ], "source": [ "# Let's train the SGD model and set the version name to \"sgd\"\n", "pipeline_settings[\"model_version\"].version = \"sgd\"\n", @@ -695,9 +1830,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 25)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36mrf\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_12-12_17_52_085483\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.346s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/7a212a8b-b4f3-48d1-95bb-4eee0965121a/runs/4a40a479-958d-43f5-bb2b-e7dc567d34ff/dag\u001b[0m\n" + ] + } + ], "source": [ "# Let's train the RF model and set the version name to \"rf\"\n", "pipeline_settings[\"model_version\"].version = \"rf\"\n", @@ -724,9 +1881,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id=UUID('92130b44-e3ce-4c73-ae8d-473e93f3dbf5') permission_denied=False body=ModelResponseBody(created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 12, 14, 17, 15), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), tags=[TagResponseModel(id=UUID('469b1284-df0a-4fee-b247-d80915501fb8'), created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), missing_permissions=False, name='breast_cancer', color=, tagged_count=1), TagResponseModel(id=UUID('dad60bad-aeda-42fc-8108-a49e0b451b34'), created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), missing_permissions=False, name='classifier', color=, tagged_count=1)], latest_version='rf', workspace={'id': 'f3a544f2-afb5-4672-934a-7a465c66201c', 'permission_denied': False, 'body': {'created': '2023-10-23T15:34:47', 'updated': '2023-10-23T15:34:47'}, 'metadata': None, 'name': 'default'}) metadata=ModelResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None) name='breast_cancer_classifier'\n", + "Model breast_cancer_classifier has 2 versions\n" + ] + }, + { + "data": { + "text/plain": [ + "('sgd', 'rf')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", "print(zenml_model)\n", @@ -748,9 +1924,23 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier()" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let's load the RF version\n", "rf_zenml_model_version = client.get_model_version(\"breast_cancer_classifier\", \"rf\")\n", @@ -767,7 +1957,7 @@ "source": [ "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", "\n", - "\"Model" + "\"Model" ] }, { @@ -786,7 +1976,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -808,7 +1998,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\"Model" + "\"Model" ] }, { @@ -834,12 +2024,12 @@ "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", "and generate predictions:\n", "\n", - "\"Inference\n" + "\"Inference" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -869,15 +2059,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "@pipeline\n", "def inference(preprocess_pipeline_id: UUID):\n", " \"\"\"Model batch inference pipeline\"\"\"\n", - " random_state = client.get_artifact_version(id=preprocess_pipeline_id).metadata[\"random_state\"].value\n", - " target = client.get_artifact_version(id=preprocess_pipeline_id).run_metadata['target'].value\n", + " # random_state = client.get_artifact_version(id=preprocess_pipeline_id).metadata[\"random_state\"].value\n", + " # target = client.get_artifact_version(id=preprocess_pipeline_id).run_metadata['target'].value\n", + " random_state = 42\n", + " target = \"target\"\n", + "\n", " df_inference = data_loader(\n", " random_state=random_state, is_inference=True\n", " )\n", @@ -902,9 +2095,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" + ] + } + ], "source": [ "pipeline_settings = {}\n", "\n", @@ -920,9 +2121,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.060s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", + "\u001b[33mYou specified both an ID as well as a version of the artifact_versions. Ignoring the version and fetching the artifact_versions by ID.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m14.047s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36minference-2023_12_12-12_22_12_777704\u001b[1;35m has finished in \u001b[0m\u001b[1;36m30.755s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/16e56d43-ffde-4f8a-b77d-cc3bd4e2186e/runs/a7b980c7-0313-43c2-817f-e30baff74084/dag\u001b[0m\n" + ] + } + ], "source": [ "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", @@ -932,7 +2156,7 @@ "# We need to pass in the ID of the preprocessing done in the feature engineering pipeline\n", "# in order to avoid training-serving skew\n", "inference_configured(\n", - " preprocess_pipeline_id=preprocessing_pipeline_artifact.id\n", + " preprocess_pipeline_id=preprocessing_pipeline_artifact_version.id\n", ")" ] }, @@ -946,9 +2170,48 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 0\n", + "2 0\n", + "3 1\n", + "4 1\n", + "5 0\n", + "6 0\n", + "7 0\n", + "8 0\n", + "9 1\n", + "10 1\n", + "11 0\n", + "12 1\n", + "13 0\n", + "14 1\n", + "15 0\n", + "16 1\n", + "17 1\n", + "18 1\n", + "19 0\n", + "20 1\n", + "21 1\n", + "22 0\n", + "23 1\n", + "24 1\n", + "25 1\n", + "26 1\n", + "27 1\n", + "Name: series, dtype: int64" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch production model\n", "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", @@ -963,7 +2226,7 @@ "source": [ "You can also see all predictions ever created as a complete history in the dashboard:\n", "\n", - "\"Model" + "\"Model" ] }, { From 548293d4bc3b148e050d9942700e70199c310f30 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Wed, 13 Dec 2023 17:38:45 +0100 Subject: [PATCH 15/48] Cleaned up and finalized for alexej --- template/pipelines/inference.py | 15 +- template/run.ipynb | 1421 ++----------------------------- template/run.py | 22 +- 3 files changed, 107 insertions(+), 1351 deletions(-) diff --git a/template/pipelines/inference.py b/template/pipelines/inference.py index da937f6..eafcbe8 100644 --- a/template/pipelines/inference.py +++ b/template/pipelines/inference.py @@ -16,7 +16,7 @@ @pipeline -def inference(): +def inference(random_state: str, target: str): """ Model inference pipeline. @@ -24,19 +24,12 @@ def inference(): the same preprocessing pipeline used in training, and runs inference with the trained model. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - client = Client() - + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Get the production model artifact - model: ArtifactVersionResponse = get_pipeline_context().model_version.get_artifact("model") + model = get_pipeline_context().model_version.get_artifact("model") # Get the preprocess pipeline artifact associated with this version - preprocess_pipeline: ArtifactVersionResponse = get_pipeline_context().model_version.get_artifact("preprocess_pipeline") - - # Use the metadata of feature engineering pipeline artifact - # to get the random state and target column - random_state = client.get_artifact_version(preprocess_pipeline.id).run_metadata["random_state"].value - target = client.get_artifact_version(preprocess_pipeline.id).run_metadata['target'].value + preprocess_pipeline = get_pipeline_context().model_version.get_artifact("preprocess_pipeline") # Link all the steps together by calling them and passing the output # of one step as the input of the next step. diff --git a/template/run.ipynb b/template/run.ipynb index 7227876..2de8236 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -229,230 +229,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" - ] - }, - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...17.33184.602019.00.16220.66560.71190.26540.46010.118900
120.5717.77132.901326.00.084740.078640.08690.070170.18120.05667...23.41158.801956.00.12380.18660.24160.18600.27500.089020
219.6921.25130.001203.00.109600.159900.19740.127900.20690.05999...25.53152.501709.00.14440.42450.45040.24300.36130.087580
311.4220.3877.58386.10.142500.283900.24140.105200.25970.09744...26.5098.87567.70.20980.86630.68690.25750.66380.173000
420.2914.34135.101297.00.100300.132800.19800.104300.18090.05883...16.67152.201575.00.13740.20500.40000.16250.23640.076780
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" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "0 17.99 10.38 122.80 1001.0 0.11840 \n", - "1 20.57 17.77 132.90 1326.0 0.08474 \n", - "2 19.69 21.25 130.00 1203.0 0.10960 \n", - "3 11.42 20.38 77.58 386.1 0.14250 \n", - "4 20.29 14.34 135.10 1297.0 0.10030 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "0 0.27760 0.3001 0.14710 0.2419 \n", - "1 0.07864 0.0869 0.07017 0.1812 \n", - "2 0.15990 0.1974 0.12790 0.2069 \n", - "3 0.28390 0.2414 0.10520 0.2597 \n", - "4 0.13280 0.1980 0.10430 0.1809 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "0 0.07871 ... 17.33 184.60 2019.0 \n", - "1 0.05667 ... 23.41 158.80 1956.0 \n", - "2 0.05999 ... 25.53 152.50 1709.0 \n", - "3 0.09744 ... 26.50 98.87 567.7 \n", - "4 0.05883 ... 16.67 152.20 1575.0 \n", - "\n", - " worst smoothness worst compactness worst concavity worst concave points \\\n", - "0 0.1622 0.6656 0.7119 0.2654 \n", - "1 0.1238 0.1866 0.2416 0.1860 \n", - "2 0.1444 0.4245 0.4504 0.2430 \n", - "3 0.2098 0.8663 0.6869 0.2575 \n", - "4 0.1374 0.2050 0.4000 0.1625 \n", - "\n", - " worst symmetry worst fractal dimension target \n", - "0 0.4601 0.11890 0 \n", - "1 0.2750 0.08902 0 \n", - "2 0.3613 0.08758 0 \n", - "3 0.6638 0.17300 0 \n", - "4 0.2364 0.07678 0 \n", - "\n", - "[5 rows x 31 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df = data_loader_simplified(random_state=42)\n", "df.head()" @@ -472,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "b50a9537", "metadata": {}, "outputs": [], @@ -515,32 +294,9 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 5)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.904s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.611s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m13.879s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_12-12_12_46_332864\u001b[1;35m has finished in \u001b[0m\u001b[1;36m23.070s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/088b8ebf-5f03-471d-975f-129e56e3f193/runs/05cb3ef9-007d-4ecf-8bdb-e68d63aafbc6/dag\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "feature_engineering()" ] @@ -554,32 +310,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 6)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.851s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.672s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.927s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_12-12_13_19_797666\u001b[1;35m has finished in \u001b[0m\u001b[1;36m14.861s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/985306fd-51ce-4204-96ee-079e3f57ddb5/runs/07999a33-49ec-4997-87cd-0bece20398cd/dag\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "feature_engineering(test_size=0.3)" ] @@ -631,17 +364,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "feature_engineering-2023_12_12-12_13_19_797666\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "client = Client()\n", "run = client.get_pipeline(\"feature_engineering\").last_run\n", @@ -657,419 +382,18 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'preprocess_pipeline': ArtifactVersionResponse(id=UUID('7a085583-a612-4195-9835-176048dabc16'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 12, 15, 4, 26), updated=datetime.datetime(2023, 12, 12, 15, 4, 26), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 12, 14, 17, 15), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), artifact=ArtifactResponse(id=UUID('428da721-89f8-4d1d-93b7-0ad1108803c7'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 12, 10, 29, 43), updated=datetime.datetime(2023, 12, 12, 10, 29, 43)), metadata=None, name='preprocess_pipeline'), version='108', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/preprocess_pipeline/f5740f18-a486-41d1-8bca-f59f4fcd9e12', type=, materializer=Source(module='zenml.integrations.sklearn.materializers.sklearn_materializer', attribute='SklearnMaterializer', type=), data_type=Source(module='sklearn.pipeline', attribute='Pipeline', type=, version='1.2.2', package_name='scikit-learn')), metadata=None),\n", - 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" 'dataset_tst': ArtifactVersionResponse(id=UUID('ef032295-f2fb-4f8e-b99e-4cf726373136'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 12, 15, 4, 24), updated=datetime.datetime(2023, 12, 12, 15, 4, 24), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 12, 14, 17, 15), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), artifact=ArtifactResponse(id=UUID('b4b9cd40-169b-4f56-867d-c20c7beb4a7d'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 12, 10, 29, 43), updated=datetime.datetime(2023, 12, 12, 10, 29, 43)), metadata=None, name='dataset_tst'), version='108', uri='/home/htahir1/.config/zenml/local_stores/466b79ce-3df9-4549-a50b-67ed433461f3/data_preprocessor/dataset_tst/f5740f18-a486-41d1-8bca-f59f4fcd9e12', type=, materializer=Source(module='zenml.materializers.pandas_materializer', attribute='PandasMaterializer', type=), data_type=Source(module='pandas.core.frame', attribute='DataFrame', type=, version='2.0.3', package_name='pandas')), metadata=None)}" - 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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
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378 rows × 31 columns

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" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "428 12.000 28.23 76.77 442.5 0.08437 \n", - "370 12.100 17.72 78.07 446.2 0.10290 \n", - "57 8.598 20.98 54.66 221.8 0.12430 \n", - "332 11.950 14.96 77.23 426.7 0.11580 \n", - "24 17.140 16.40 116.00 912.7 0.11860 \n", - ".. ... ... ... ... ... \n", - "71 16.070 19.65 104.10 817.7 0.09168 \n", - "106 10.510 20.19 68.64 334.2 0.11220 \n", - "270 16.240 18.77 108.80 805.1 0.10660 \n", - "435 9.755 28.20 61.68 290.9 0.07984 \n", - "102 11.340 21.26 72.48 396.5 0.08759 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "428 0.06450 0.04055 0.019450 0.1615 \n", - "370 0.09758 0.04783 0.033260 0.1937 \n", - "57 0.08963 0.03000 0.009259 0.1828 \n", - "332 0.12060 0.01171 0.017870 0.2459 \n", - "24 0.22760 0.22290 0.140100 0.3040 \n", - ".. ... ... ... ... \n", - "71 0.08424 0.09769 0.066380 0.1798 \n", - "106 0.13030 0.06476 0.030680 0.1922 \n", - "270 0.18020 0.19480 0.090520 0.1876 \n", - "435 0.04626 0.01541 0.010430 0.1621 \n", - "102 0.06575 0.05133 0.018990 0.1487 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "428 0.06104 ... 37.88 85.07 523.7 \n", - "370 0.06161 ... 25.80 88.33 559.5 \n", - "57 0.06757 ... 27.04 62.06 273.9 \n", - "332 0.06581 ... 17.72 83.09 496.2 \n", - "24 0.07413 ... 21.40 152.40 1461.0 \n", - ".. ... ... ... ... ... \n", - "71 0.05391 ... 24.56 128.80 1223.0 \n", - "106 0.07782 ... 22.75 72.62 374.4 \n", - "270 0.06684 ... 25.09 126.90 1031.0 \n", - "435 0.05952 ... 36.92 68.03 349.9 \n", - "102 0.06529 ... 29.15 83.99 518.1 \n", - "\n", - " worst smoothness worst compactness worst concavity \\\n", - "428 0.1208 0.1856 0.18110 \n", - "370 0.1432 0.1773 0.16030 \n", - "57 0.1639 0.1698 0.09001 \n", - "332 0.1293 0.1885 0.03122 \n", - "24 0.1545 0.3949 0.38530 \n", - ".. ... ... ... \n", - "71 0.1500 0.2045 0.28290 \n", - "106 0.1300 0.2049 0.12950 \n", - "270 0.1365 0.4706 0.50260 \n", - "435 0.1110 0.1109 0.07190 \n", - "102 0.1699 0.2196 0.31200 \n", - "\n", - " worst concave points worst symmetry worst fractal dimension target \n", - "428 0.07116 0.2447 0.08194 1 \n", - "370 0.06266 0.3049 0.07081 1 \n", - "57 0.02778 0.2972 0.07712 1 \n", - "332 0.04766 0.3124 0.07590 1 \n", - "24 0.25500 0.4066 0.10590 0 \n", - ".. ... ... ... ... \n", - "71 0.15200 0.2650 0.06387 0 \n", - "106 0.06136 0.2383 0.09026 1 \n", - "270 0.17320 0.2770 0.10630 0 \n", - "435 0.04866 0.2321 0.07211 1 \n", - "102 0.08278 0.2829 0.08832 1 \n", - "\n", - "[378 rows x 31 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Read one of the datasets. This is the one with a 0.3 test split\n", "run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].load()" @@ -1086,13 +410,31 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, + "id": "c8f90647", + "metadata": {}, + "outputs": [], + "source": [ + "# Get artifact version from our run\n", + "dataset_trn_artifact_version_via_run = run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"] \n", + "\n", + "# Get latest version from client directly\n", + "dataset_trn_artifact_version = client.get_artifact_version(\"dataset_trn\")\n", + "\n", + "# This should be true if our run is the latest run and no artifact has been produced\n", + "# in the intervening time\n", + "dataset_trn_artifact_version_via_run.id == dataset_trn_artifact_version.id" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "dataset_trn_artifact_version = client.get_artifact_version(run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].id)\n", - "dataset_tst_artifact_version = client.get_artifact_version(run.steps[\"data_preprocessor\"].outputs[\"dataset_tst\"].id)\n", - "preprocessing_pipeline_artifact_version = client.get_artifact_version(run.steps[\"data_preprocessor\"].outputs[\"preprocess_pipeline\"].id)" + "# Fetch the rest of the artifacts\n", + "dataset_tst_artifact_version = client.get_artifact_version(\"dataset_tst\")\n", + "preprocessing_pipeline_artifact_version = client.get_artifact_version(\"preprocess_pipeline\")" ] }, { @@ -1105,397 +447,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
42812.00028.2376.77442.50.084370.064500.040550.0194500.16150.06104...37.8885.07523.70.12080.18560.181100.071160.24470.081941
37012.10017.7278.07446.20.102900.097580.047830.0332600.19370.06161...25.8088.33559.50.14320.17730.160300.062660.30490.070811
578.59820.9854.66221.80.124300.089630.030000.0092590.18280.06757...27.0462.06273.90.16390.16980.090010.027780.29720.077121
33211.95014.9677.23426.70.115800.120600.011710.0178700.24590.06581...17.7283.09496.20.12930.18850.031220.047660.31240.075901
2417.14016.40116.00912.70.118600.227600.222900.1401000.30400.07413...21.40152.401461.00.15450.39490.385300.255000.40660.105900
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7116.07019.65104.10817.70.091680.084240.097690.0663800.17980.05391...24.56128.801223.00.15000.20450.282900.152000.26500.063870
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378 rows × 31 columns

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" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "428 12.000 28.23 76.77 442.5 0.08437 \n", - "370 12.100 17.72 78.07 446.2 0.10290 \n", - "57 8.598 20.98 54.66 221.8 0.12430 \n", - "332 11.950 14.96 77.23 426.7 0.11580 \n", - "24 17.140 16.40 116.00 912.7 0.11860 \n", - ".. ... ... ... ... ... \n", - "71 16.070 19.65 104.10 817.7 0.09168 \n", - "106 10.510 20.19 68.64 334.2 0.11220 \n", - "270 16.240 18.77 108.80 805.1 0.10660 \n", - "435 9.755 28.20 61.68 290.9 0.07984 \n", - "102 11.340 21.26 72.48 396.5 0.08759 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "428 0.06450 0.04055 0.019450 0.1615 \n", - "370 0.09758 0.04783 0.033260 0.1937 \n", - "57 0.08963 0.03000 0.009259 0.1828 \n", - "332 0.12060 0.01171 0.017870 0.2459 \n", - "24 0.22760 0.22290 0.140100 0.3040 \n", - ".. ... ... ... ... \n", - "71 0.08424 0.09769 0.066380 0.1798 \n", - "106 0.13030 0.06476 0.030680 0.1922 \n", - "270 0.18020 0.19480 0.090520 0.1876 \n", - "435 0.04626 0.01541 0.010430 0.1621 \n", - "102 0.06575 0.05133 0.018990 0.1487 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "428 0.06104 ... 37.88 85.07 523.7 \n", - "370 0.06161 ... 25.80 88.33 559.5 \n", - "57 0.06757 ... 27.04 62.06 273.9 \n", - "332 0.06581 ... 17.72 83.09 496.2 \n", - "24 0.07413 ... 21.40 152.40 1461.0 \n", - ".. ... ... ... ... ... \n", - "71 0.05391 ... 24.56 128.80 1223.0 \n", - "106 0.07782 ... 22.75 72.62 374.4 \n", - "270 0.06684 ... 25.09 126.90 1031.0 \n", - "435 0.05952 ... 36.92 68.03 349.9 \n", - "102 0.06529 ... 29.15 83.99 518.1 \n", - "\n", - " worst smoothness worst compactness worst concavity \\\n", - "428 0.1208 0.1856 0.18110 \n", - "370 0.1432 0.1773 0.16030 \n", - "57 0.1639 0.1698 0.09001 \n", - "332 0.1293 0.1885 0.03122 \n", - "24 0.1545 0.3949 0.38530 \n", - ".. ... ... ... \n", - "71 0.1500 0.2045 0.28290 \n", - "106 0.1300 0.2049 0.12950 \n", - "270 0.1365 0.4706 0.50260 \n", - "435 0.1110 0.1109 0.07190 \n", - "102 0.1699 0.2196 0.31200 \n", - "\n", - " worst concave points worst symmetry worst fractal dimension target \n", - "428 0.07116 0.2447 0.08194 1 \n", - "370 0.06266 0.3049 0.07081 1 \n", - "57 0.02778 0.2972 0.07712 1 \n", - "332 0.04766 0.3124 0.07590 1 \n", - "24 0.25500 0.4066 0.10590 0 \n", - ".. ... ... ... ... \n", - "71 0.15200 0.2650 0.06387 0 \n", - "106 0.06136 0.2383 0.09026 1 \n", - "270 0.17320 0.2770 0.10630 0 \n", - "435 0.04866 0.2321 0.07211 1 \n", - "102 0.08278 0.2829 0.08832 1 \n", - "\n", - "[378 rows x 31 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Load an artifact to verify you can fetch it\n", "dataset_trn_artifact_version.load()" @@ -1533,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1591,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1638,34 +592,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 25)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.516s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", - "\u001b[1;35mTest accuracy=94.48%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m4.021s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_12-12_17_04_641333\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.876s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/7a212a8b-b4f3-48d1-95bb-4eee0965121a/runs/bc921d13-8093-4da9-8f1f-a4b147a6a69d/dag\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Use a random forest model with the chosen datasets.\n", "# We need to pass the ID's of the datasets into the function\n", @@ -1680,34 +609,9 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 26)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.153s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTrain accuracy=91.27%\u001b[0m\n", - "\u001b[1;35mTest accuracy=89.57%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m3.880s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_12-12_17_20_042737\u001b[1;35m has finished in \u001b[0m\u001b[1;36m10.156s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/baf2e634-0de1-4553-8607-14d0ab190cba/runs/549e8785-ee93-474a-a4b5-b091e7131b92/dag\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Use a SGD classifier\n", "sgd_run = training(\n", @@ -1730,20 +634,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# The evaluator returns a float value with the accuracy\n", "rf_run.steps[\"model_evaluator\"].output.load() > sgd_run.steps[\"model_evaluator\"].output.load()" @@ -1769,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1786,32 +679,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 26)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m was created implicitly.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36msgd\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_12-12_17_38_803413\u001b[1;35m has finished in \u001b[0m\u001b[1;36m7.935s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/baf2e634-0de1-4553-8607-14d0ab190cba/runs/e63fe206-1334-4a1c-bb84-ba792ae00157/dag\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Let's train the SGD model and set the version name to \"sgd\"\n", "pipeline_settings[\"model_version\"].version = \"sgd\"\n", @@ -1830,31 +700,9 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 25)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36mrf\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_12-12_17_52_085483\u001b[1;35m has finished in \u001b[0m\u001b[1;36m8.346s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/7a212a8b-b4f3-48d1-95bb-4eee0965121a/runs/4a40a479-958d-43f5-bb2b-e7dc567d34ff/dag\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Let's train the RF model and set the version name to \"rf\"\n", "pipeline_settings[\"model_version\"].version = \"rf\"\n", @@ -1881,28 +729,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id=UUID('92130b44-e3ce-4c73-ae8d-473e93f3dbf5') permission_denied=False body=ModelResponseBody(created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), user=UserResponse(id=UUID('c6fcdcc8-69e1-4ff5-9eb2-6a53aa81a08b'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 10, 24, 7, 36, 26), updated=datetime.datetime(2023, 12, 12, 14, 17, 15), active=True, activation_token=None, full_name='Hamza Tahir', email_opted_in=True, is_service_account=False), metadata=None, name='hamza@zenml.io'), tags=[TagResponseModel(id=UUID('469b1284-df0a-4fee-b247-d80915501fb8'), created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), missing_permissions=False, name='breast_cancer', color=, tagged_count=1), TagResponseModel(id=UUID('dad60bad-aeda-42fc-8108-a49e0b451b34'), created=datetime.datetime(2023, 12, 12, 15, 8, 29), updated=datetime.datetime(2023, 12, 12, 15, 8, 29), missing_permissions=False, name='classifier', color=, tagged_count=1)], latest_version='rf', workspace={'id': 'f3a544f2-afb5-4672-934a-7a465c66201c', 'permission_denied': False, 'body': {'created': '2023-10-23T15:34:47', 'updated': '2023-10-23T15:34:47'}, 'metadata': None, 'name': 'default'}) metadata=ModelResponseMetadata(workspace=WorkspaceResponse(id=UUID('f3a544f2-afb5-4672-934a-7a465c66201c'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 10, 23, 15, 34, 47), updated=datetime.datetime(2023, 10, 23, 15, 34, 47)), metadata=None, name='default'), license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None) name='breast_cancer_classifier'\n", - "Model breast_cancer_classifier has 2 versions\n" - ] - }, - { - "data": { - "text/plain": [ - "('sgd', 'rf')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", "print(zenml_model)\n", @@ -1924,23 +753,9 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
RandomForestClassifier()
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" - ], - "text/plain": [ - "RandomForestClassifier()" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Let's load the RF version\n", "rf_zenml_model_version = client.get_model_version(\"breast_cancer_classifier\", \"rf\")\n", @@ -1976,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2029,7 +844,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2059,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2095,19 +910,11 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "pipeline_settings = {}\n", + "pipeline_settings = {\"enable_cache\": False}\n", "\n", "# Lets add some metadata to the model to make it identifiable\n", "pipeline_settings[\"model_version\"] = ModelVersion(\n", @@ -2121,32 +928,9 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mhamza@zenml.io\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m11.060s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", - "\u001b[33mYou specified both an ID as well as a version of the artifact_versions. Ignoring the version and fetching the artifact_versions by ID.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m14.047s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36minference-2023_12_12-12_22_12_777704\u001b[1;35m has finished in \u001b[0m\u001b[1;36m30.755s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mDashboard URL: https://1cf18d95-zenml.cloudinfra.zenml.io/workspaces/default/pipelines/16e56d43-ffde-4f8a-b77d-cc3bd4e2186e/runs/a7b980c7-0313-43c2-817f-e30baff74084/dag\u001b[0m\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", @@ -2170,48 +954,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 0\n", - "2 0\n", - "3 1\n", - "4 1\n", - "5 0\n", - "6 0\n", - "7 0\n", - "8 0\n", - "9 1\n", - "10 1\n", - "11 0\n", - "12 1\n", - "13 0\n", - "14 1\n", - "15 0\n", - "16 1\n", - "17 1\n", - "18 1\n", - "19 0\n", - "20 1\n", - "21 1\n", - "22 0\n", - "23 1\n", - "24 1\n", - "25 1\n", - "26 1\n", - "27 1\n", - "Name: series, dtype: int64" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Fetch production model\n", "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", diff --git a/template/run.py b/template/run.py index ea2452e..e01a106 100644 --- a/template/run.py +++ b/template/run.py @@ -103,6 +103,8 @@ def main( of which comes from the YAML config files) * launching the pipeline """ + client = Client() + config_folder = os.path.join( os.path.dirname(os.path.realpath(__file__)), "configs", @@ -132,7 +134,6 @@ def main( train_dataset_version_name is not None and test_dataset_version_name is not None ) - client = Client() train_dataset_artifact_version = client.get_artifact_version( train_dataset_name, train_dataset_version_name ) @@ -148,10 +149,27 @@ def main( logger.info("Training pipeline finished successfully!") if inference_pipeline: + run_args_train = {} pipeline_args = {} pipeline_args["config_path"] = os.path.join(config_folder, "inference.yaml") run_args_inference = {} - inference.with_options(**pipeline_args)(**run_args_inference) + + # Configure the pipeline + inference_configured = inference.with_options(**pipeline_args) + + # Fetch the production model + zenml_model = client.get_model_version("breast_cancer_classifier", "production") + preprocess_pipeline_artifact = zenml_model.get_artifact("preprocess_pipeline") + + # Use the metadata of feature engineering pipeline artifact + # to get the random state and target column + random_state = preprocess_pipeline_artifact.run_metadata["random_state"].value + target = preprocess_pipeline_artifact.run_metadata['target'].value + run_args_inference["random_state"] = random_state + run_args_inference["target"] = target + + # Run the pipeline + inference_configured(**run_args_inference) logger.info("Inference pipeline finished successfully!") From 8557a3d3710791e43fd009f6b6d484d814fba572 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 13 Dec 2023 23:29:08 +0100 Subject: [PATCH 16/48] Initial changes --- .gitignore | 3 + requirements.txt | 2 +- template/configs/feature_engineering.yaml | 4 + template/configs/inference.yaml | 5 +- template/configs/training.yaml | 10 ++- template/pipelines/inference.py | 2 - template/pipelines/training.py | 1 - template/requirements.txt | 3 +- template/run.ipynb | 100 +++++++++++++++++++--- template/run.py | 2 +- template/steps/model_evaluator.py | 2 +- template/steps/model_promoter.py | 10 +-- 12 files changed, 117 insertions(+), 27 deletions(-) diff --git a/.gitignore b/.gitignore index 882db32..bfdb54c 100644 --- a/.gitignore +++ b/.gitignore @@ -98,6 +98,9 @@ __pypackages__/ celerybeat-schedule celerybeat.pid +# PyCharm Stuff +.idea + # SageMath parsed files *.sage.py diff --git a/requirements.txt b/requirements.txt index a64987f..21e8aa5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ scikit-learn<1.3 copier jinja2-time -zenml[server]>=0.50.0 +zenml[server]>=0.52.0 notebook \ No newline at end of file diff --git a/template/configs/feature_engineering.yaml b/template/configs/feature_engineering.yaml index b0e559f..bf76f12 100644 --- a/template/configs/feature_engineering.yaml +++ b/template/configs/feature_engineering.yaml @@ -3,3 +3,7 @@ settings: docker: required_integrations: - sklearn + - pyarrow + +# pipeline configuration +test_size: 0.35 \ No newline at end of file diff --git a/template/configs/inference.yaml b/template/configs/inference.yaml index 661b328..b9fd29d 100644 --- a/template/configs/inference.yaml +++ b/template/configs/inference.yaml @@ -3,11 +3,12 @@ settings: docker: required_integrations: - sklearn + - pyarrow # configuration of the Model Control Plane model_version: name: breast_cancer_classifier version: production license: Apache 2.0 - description: Classification of Breast Cancer Dataset. - tags: ["classification", "sklearn"] + description: A breast cancer classifier + tags: ["breast_cancer", "classifier"] \ No newline at end of file diff --git a/template/configs/training.yaml b/template/configs/training.yaml index daa91a1..b1161dd 100644 --- a/template/configs/training.yaml +++ b/template/configs/training.yaml @@ -3,10 +3,16 @@ settings: docker: required_integrations: - sklearn + - pyarrow # configuration of the Model Control Plane model_version: name: breast_cancer_classifier license: Apache 2.0 - description: Classification of Breast Cancer Dataset. - tags: ["classification", "sklearn"] + description: A breast cancer classifier + tags: ["breast_cancer", "classifier"] + +# pipeline configuration +model_type: "sgd" # Choose between rf/sgd +# train_dataset_id: ... +# test_dataset_id: ... \ No newline at end of file diff --git a/template/pipelines/inference.py b/template/pipelines/inference.py index eafcbe8..ebba44d 100644 --- a/template/pipelines/inference.py +++ b/template/pipelines/inference.py @@ -24,7 +24,6 @@ def inference(random_state: str, target: str): the same preprocessing pipeline used in training, and runs inference with the trained model. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Get the production model artifact model = get_pipeline_context().model_version.get_artifact("model") @@ -43,4 +42,3 @@ def inference(random_state: str, target: str): model=model, dataset_inf=df_inference, ) - ### END CODE HERE ### diff --git a/template/pipelines/training.py b/template/pipelines/training.py index a82760d..2e8b367 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -58,4 +58,3 @@ def training( ) model_promoter(accuracy=acc) - ### END CODE HERE ### diff --git a/template/requirements.txt b/template/requirements.txt index f3a893a..7a25951 100644 --- a/template/requirements.txt +++ b/template/requirements.txt @@ -1,3 +1,4 @@ zenml[server]>=0.50.0 notebook -scikit-learn<1.3 \ No newline at end of file +scikit-learn<1.3 +pyarrow \ No newline at end of file diff --git a/template/run.ipynb b/template/run.ipynb index 2de8236..97e4ba8 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "63ab391a", "metadata": {}, "source": [ "# Intro to MLOps using ZenML\n", @@ -21,6 +22,7 @@ }, { "cell_type": "markdown", + "id": "8f466b16", "metadata": {}, "source": [ "## Run on Colab\n", @@ -34,6 +36,7 @@ }, { "cell_type": "markdown", + "id": "66b2977c", "metadata": {}, "source": [ "# 👶 Step 0. Install Requirements\n", @@ -45,6 +48,7 @@ { "cell_type": "code", "execution_count": null, + "id": "ce2f40eb", "metadata": {}, "outputs": [], "source": [ @@ -54,6 +58,7 @@ { "cell_type": "code", "execution_count": null, + "id": "5aad397e", "metadata": {}, "outputs": [], "source": [ @@ -67,6 +72,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f76f562e", "metadata": {}, "outputs": [], "source": [ @@ -78,6 +84,7 @@ }, { "cell_type": "markdown", + "id": "3b044374", "metadata": {}, "source": [ "Please wait for the installation to complete before running subsequent cells. At\n", @@ -86,6 +93,7 @@ }, { "cell_type": "markdown", + "id": "e3955ff1", "metadata": {}, "source": [ "Optional: If you are using ZenML Cloud, execute the following cell with your tenant URL. Otherwise ignore." @@ -94,6 +102,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e2587315", "metadata": {}, "outputs": [], "source": [ @@ -155,6 +164,7 @@ }, { "cell_type": "markdown", + "id": "35e48460", "metadata": {}, "source": [ "## 🥇 Step 1: Load your data and execute feature engineering\n", @@ -175,6 +185,7 @@ { "cell_type": "code", "execution_count": null, + "id": "3cd974d1", "metadata": {}, "outputs": [], "source": [ @@ -199,6 +210,7 @@ }, { "cell_type": "markdown", + "id": "1e8ba4c6", "metadata": {}, "source": [ "The whole function is decorated with the `@step` decorator, which\n", @@ -219,6 +231,7 @@ }, { "cell_type": "markdown", + "id": "b6286b67", "metadata": {}, "source": [ "ZenML is built in a way that allows you to experiment with your data and build\n", @@ -230,6 +243,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d838e2ea", "metadata": {}, "outputs": [], "source": [ @@ -239,6 +253,7 @@ }, { "cell_type": "markdown", + "id": "28c05291", "metadata": {}, "source": [ "Everything looks as we'd expect and the values are all in the right format 🥳.\n", @@ -258,7 +273,7 @@ "source": [ "@pipeline\n", "def feature_engineering(\n", - " test_size: float = 0.2,\n", + " test_size: float = 0.3,\n", " drop_na: Optional[bool] = None,\n", " normalize: Optional[bool] = None,\n", " drop_columns: Optional[List[str]] = None,\n", @@ -286,6 +301,7 @@ }, { "cell_type": "markdown", + "id": "7cd73c23", "metadata": {}, "source": [ "We're ready to run the pipeline now, which we can do just as with the step - by calling the\n", @@ -295,6 +311,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1e0aa9af", "metadata": {}, "outputs": [], "source": [ @@ -303,6 +320,7 @@ }, { "cell_type": "markdown", + "id": "1785c303", "metadata": {}, "source": [ "Let's run this again with a slightly different test size, to create another dataset:" @@ -311,14 +329,16 @@ { "cell_type": "code", "execution_count": null, + "id": "9faaf0c2", "metadata": {}, "outputs": [], "source": [ - "feature_engineering(test_size=0.3)" + "feature_engineering(test_size=0.35)" ] }, { "cell_type": "markdown", + "id": "64bb7206", "metadata": {}, "source": [ "Notice that the data loader step was **cached**, while the rest of the pipeline was rerun. \n", @@ -328,10 +348,11 @@ }, { "cell_type": "markdown", + "id": "6c42078a", "metadata": {}, "source": [ "At this point you might be interested to view your pipeline runs in the ZenML\n", - "Dashboard. You can spin this up by executing the next cell. This will start a\n", + "Dashboard. In case you are not using a hosted instance of ZenML, you can spin this up by executing the next cell. This will start a\n", "server which you can access by clicking on the link that appears in the output\n", "of the cell.\n", "\n", @@ -343,20 +364,26 @@ { "cell_type": "code", "execution_count": null, + "id": "8cd3cc8c", "metadata": {}, "outputs": [], "source": [ "from zenml.environment import Environment\n", + "from zenml.zen_stores.rest_zen_store import RestZenStore\n", "\n", - "if Environment.in_google_colab():\n", - " # run ZenML through a cloudflare tunnel to get a public endpoint\n", - " !zenml up --port 8237 & cloudflared tunnel --url http://localhost:8237\n", - "else:\n", - " !zenml up" + "\n", + "if not isinstance(client.zen_store, RestZenStore):\n", + " # Only spin up a local Dashboard in case you aren't already connected to a remote server\n", + " if Environment.in_google_colab():\n", + " # run ZenML through a cloudflare tunnel to get a public endpoint\n", + " !zenml up --port 8237 & cloudflared tunnel --url http://localhost:8237\n", + " else:\n", + " !zenml up" ] }, { "cell_type": "markdown", + "id": "e8471f93", "metadata": {}, "source": [ "We can also fetch the pipeline from the server and view the results directly in the notebook:" @@ -365,6 +392,7 @@ { "cell_type": "code", "execution_count": null, + "id": "f208b200", "metadata": {}, "outputs": [], "source": [ @@ -375,6 +403,7 @@ }, { "cell_type": "markdown", + "id": "a037f09d", "metadata": {}, "source": [ "We can also see the data artifacts that were produced by the last step of the pipeline:" @@ -383,6 +412,7 @@ { "cell_type": "code", "execution_count": null, + "id": "34283e89", "metadata": {}, "outputs": [], "source": [ @@ -392,6 +422,7 @@ { "cell_type": "code", "execution_count": null, + "id": "bceb0312", "metadata": {}, "outputs": [], "source": [ @@ -401,6 +432,7 @@ }, { "cell_type": "markdown", + "id": "26d26436", "metadata": {}, "source": [ "We can also get the artifacts directly. Each time you create a new pipeline run, a new `artifact version` is created.\n", @@ -429,6 +461,7 @@ { "cell_type": "code", "execution_count": null, + "id": "3f9d3dfd", "metadata": {}, "outputs": [], "source": [ @@ -439,6 +472,7 @@ }, { "cell_type": "markdown", + "id": "7a7d1b04", "metadata": {}, "source": [ "If you started with a fresh install, then you would have two versions corresponding\n", @@ -448,6 +482,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c82aca75", "metadata": {}, "outputs": [], "source": [ @@ -457,6 +492,7 @@ }, { "cell_type": "markdown", + "id": "5963509e", "metadata": {}, "source": [ "We'll use these artifacts from above in our next pipeline" @@ -464,6 +500,7 @@ }, { "cell_type": "markdown", + "id": "8c28b474", "metadata": {}, "source": [ "# ⌚ Step 2: Training pipeline" @@ -471,6 +508,7 @@ }, { "cell_type": "markdown", + "id": "87909827", "metadata": {}, "source": [ "Now that we have our data it makes sense to train some models to get a sense of\n", @@ -488,6 +526,7 @@ { "cell_type": "code", "execution_count": null, + "id": "fccf1bd9", "metadata": {}, "outputs": [], "source": [ @@ -527,6 +566,7 @@ }, { "cell_type": "markdown", + "id": "73a00008", "metadata": {}, "source": [ "Our two training steps both return different kinds of `sklearn` classifier\n", @@ -535,6 +575,7 @@ }, { "cell_type": "markdown", + "id": "a5f22174", "metadata": {}, "source": [ "ZenML allows you to load any version of any dataset that is tracked by the framework\n", @@ -546,6 +587,7 @@ { "cell_type": "code", "execution_count": null, + "id": "1aa98f2f", "metadata": {}, "outputs": [], "source": [ @@ -582,6 +624,7 @@ }, { "cell_type": "markdown", + "id": "88b70fd3", "metadata": {}, "source": [ "The end goal of this quick baseline evaluation is to understand which of the two\n", @@ -593,6 +636,7 @@ { "cell_type": "code", "execution_count": null, + "id": "c64885ac", "metadata": {}, "outputs": [], "source": [ @@ -610,6 +654,7 @@ { "cell_type": "code", "execution_count": null, + "id": "4300c82f", "metadata": {}, "outputs": [], "source": [ @@ -625,6 +670,7 @@ }, { "cell_type": "markdown", + "id": "43f1a68a", "metadata": {}, "source": [ "You can see from the logs already how our model training went: the\n", @@ -635,6 +681,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d95810b1", "metadata": {}, "outputs": [], "source": [ @@ -644,6 +691,7 @@ }, { "cell_type": "markdown", + "id": "e256d145", "metadata": {}, "source": [ "# 💯 Step 3: Associating a model with your pipeline" @@ -651,6 +699,7 @@ }, { "cell_type": "markdown", + "id": "927978f3", "metadata": {}, "source": [ "You can see it is relatively easy to train ML models using ZenML pipelines. But it can be somewhat clunky to track\n", @@ -663,6 +712,7 @@ { "cell_type": "code", "execution_count": null, + "id": "99ca00c0", "metadata": {}, "outputs": [], "source": [ @@ -680,6 +730,7 @@ { "cell_type": "code", "execution_count": null, + "id": "0e78a520", "metadata": {}, "outputs": [], "source": [ @@ -701,6 +752,7 @@ { "cell_type": "code", "execution_count": null, + "id": "9b8e0002", "metadata": {}, "outputs": [], "source": [ @@ -721,6 +773,7 @@ }, { "cell_type": "markdown", + "id": "09597223", "metadata": {}, "source": [ "This time, running both pipelines has created two associated **model versions**.\n", @@ -730,6 +783,7 @@ { "cell_type": "code", "execution_count": null, + "id": "fbb25913", "metadata": {}, "outputs": [], "source": [ @@ -743,6 +797,7 @@ }, { "cell_type": "markdown", + "id": "e82cfac2", "metadata": {}, "source": [ "The interesting part is that ZenML went ahead and linked all artifacts produced by the\n", @@ -754,6 +809,7 @@ { "cell_type": "code", "execution_count": null, + "id": "31211413", "metadata": {}, "outputs": [], "source": [ @@ -768,6 +824,7 @@ }, { "cell_type": "markdown", + "id": "53517a9a", "metadata": {}, "source": [ "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", @@ -777,6 +834,7 @@ }, { "cell_type": "markdown", + "id": "eb645dde", "metadata": {}, "source": [ "There is a lot more you can do with ZenML models, including the ability to\n", @@ -792,6 +850,7 @@ { "cell_type": "code", "execution_count": null, + "id": "26b718f8", "metadata": {}, "outputs": [], "source": [ @@ -801,6 +860,7 @@ }, { "cell_type": "markdown", + "id": "9fddf3d0", "metadata": {}, "source": [ "Of course, normally one would only promote the model by comparing to all other model\n", @@ -811,6 +871,7 @@ }, { "cell_type": "markdown", + "id": "2ecbc8cf", "metadata": {}, "source": [ "\"Model" @@ -818,6 +879,7 @@ }, { "cell_type": "markdown", + "id": "8f1146db", "metadata": {}, "source": [ "Once the model is promoted, we can now consume the right model version in our\n", @@ -826,6 +888,7 @@ }, { "cell_type": "markdown", + "id": "d6306f14", "metadata": {}, "source": [ "# 🫅 Step 4: Consuming the model in production" @@ -833,6 +896,7 @@ }, { "cell_type": "markdown", + "id": "b51f3108", "metadata": {}, "source": [ "The batch inference pipeline simply takes the model marked as `production` and runs inference on it\n", @@ -845,6 +909,7 @@ { "cell_type": "code", "execution_count": null, + "id": "92c4c7dc", "metadata": {}, "outputs": [], "source": [ @@ -859,13 +924,13 @@ " predictions = predictor.predict(dataset_inf)\n", "\n", " predictions = pd.Series(predictions, name=\"predicted\")\n", - " ### YOUR CODE ENDS HERE ###\n", "\n", " return predictions\n" ] }, { "cell_type": "markdown", + "id": "3aeb227b", "metadata": {}, "source": [ "Apart from the loading the model, we must also load the preprocessing pipeline that we ran in feature engineering,\n", @@ -875,6 +940,7 @@ { "cell_type": "code", "execution_count": null, + "id": "37c409bd", "metadata": {}, "outputs": [], "source": [ @@ -902,6 +968,7 @@ }, { "cell_type": "markdown", + "id": "c7afe7be", "metadata": {}, "source": [ "The way to load the right model is to pass in the `production` stage into the `ModelVersion` config this time.\n", @@ -911,6 +978,7 @@ { "cell_type": "code", "execution_count": null, + "id": "61bf5939", "metadata": {}, "outputs": [], "source": [ @@ -929,6 +997,7 @@ { "cell_type": "code", "execution_count": null, + "id": "ff3402f1", "metadata": {}, "outputs": [], "source": [ @@ -946,6 +1015,7 @@ }, { "cell_type": "markdown", + "id": "2935d1fa", "metadata": {}, "source": [ "ZenML automatically links all artifacts to the `production` model version as well, including the predictions\n", @@ -955,6 +1025,7 @@ { "cell_type": "code", "execution_count": null, + "id": "e191d019", "metadata": {}, "outputs": [], "source": [ @@ -967,6 +1038,7 @@ }, { "cell_type": "markdown", + "id": "b0a73cdf", "metadata": {}, "source": [ "You can also see all predictions ever created as a complete history in the dashboard:\n", @@ -1001,6 +1073,14 @@ "* If you have questions or feedback... join our [**Slack Community**](https://zenml.io/slack) and become part of the ZenML family!\n", "* If you want to quickly get started with ZenML, check out the [ZenML Cloud](https://zenml.io/cloud)." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7c7bc27", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1019,7 +1099,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.13" } }, "nbformat": 4, diff --git a/template/run.py b/template/run.py index e01a106..7eceb6f 100644 --- a/template/run.py +++ b/template/run.py @@ -17,7 +17,7 @@ @click.command( help=""" -ZenML Starter project CLI v0.0.1. +ZenML Starter project. Run the ZenML starter project with basic options. diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index dbb8bca..79892e8 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -65,11 +65,11 @@ def model_evaluator( dataset_trn.drop(columns=[target]), dataset_trn[target], ) - logger.info(f"Train accuracy={trn_acc*100:.2f}%") tst_acc = model.score( dataset_tst.drop(columns=[target]), dataset_tst[target], ) + logger.info(f"Train accuracy={trn_acc*100:.2f}%") logger.info(f"Test accuracy={tst_acc*100:.2f}%") messages = [] diff --git a/template/steps/model_promoter.py b/template/steps/model_promoter.py index 784e0cc..d8a048b 100644 --- a/template/steps/model_promoter.py +++ b/template/steps/model_promoter.py @@ -8,14 +8,14 @@ @step def model_promoter(accuracy: float, stage: str = "production") -> bool: - """Dataset reader step. + """Model promoter step. - This is an example of a dataset reader step that load Breast Cancer dataset. + This is an example of a step that conditionally promotes a model. This step is parameterized, which allows you to configure the step independently of the step code, before running it in a pipeline. - In this example, the step can be configured with number of rows and logic - to drop target column or not. See the documentation for more information: + In this example, the step can be configured with the accuracy of the model + and the target stage: https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines @@ -26,7 +26,6 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: Returns: Whether the model was promoted or not. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### if accuracy < 0.8: logger.info( f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model." @@ -38,5 +37,4 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: model_version = get_step_context().model_version model_version.set_stage(stage, force=True) - ### YOUR CODE ENDS HERE ### return is_promoted From 072d3ef922b07da2d0847e0a6ced0c807574cc8e Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Wed, 13 Dec 2023 23:39:49 +0100 Subject: [PATCH 17/48] Removed some copier options --- copier.yaml | 49 ------------------------------------------------- 1 file changed, 49 deletions(-) diff --git a/copier.yaml b/copier.yaml index 270dd34..a0b5d30 100644 --- a/copier.yaml +++ b/copier.yaml @@ -51,55 +51,6 @@ use_step_params: help: "Would you like to see step parameters being used in the generated \n ZenML steps?" default: true when: "{{ template == 'starter' }}" -use_custom_artifacts: - type: bool - help: "Would you like to see custom artifact data types and materializers \n being used in the generated ZenML steps and pipelines?" - default: true - when: "{{ template == 'starter' }}" -configurable_dataset: - type: bool - help: "Would you like to be able to select the dataset used in model training\n at runtime through the CLI and as a data loader step parameter?" - default: true - when: "{{ template == 'starter' and use_step_params }}" -sklearn_dataset_name: - type: str - help: "The generated ZenML project will be populated with some example code - \n featuring one of the UCI ML datasets from scikit-learn. \n Which - dataset would you like to see being used in the generated code?" - choices: - UCI Wine Data Set (classification): wine - UCI Iris Data Set (classification): iris - UCI Breast Cancer Wisconsin (Diagnostic) Data Set (classification): breast_cancer - default: wine - when: "{{ template == 'starter' }}" -configurable_model: - type: bool - help: "Would you like to be able to select the type of model used in model \n - training at runtime through the CLI and as a model trainer step \n - parameter?" - default: true - help: >- - Would you like to be able to select the type of model used in model - training at runtime through the CLI and as a model trainer step - parameter ? - default: true - when: "{{ template == 'starter' and use_step_params }}" -sklearn_model_name: - type: str - help: "The generated ZenML project will be populated with some example code - \n featuring one of the scikit-learn classifier models. \n Which model class would you like to see being used in the generated code?" - choices: - Logistic Regression: LogisticRegression - C-Support Vector Classification: SVC - Linear Support Vector Classification: LinearSVC - Random Forest Classifier: RandomForestClassifier - K-Nearest Neighbors Classifier: KNeighborsClassifier - Gaussian Naive Bayes: GaussianNB - Linear Perceptron Classifier: Perceptron - Stochastic Gradient Descent Linear Classifier: SGDClassifier - Decision Tree Classifier: DecisionTreeClassifier - default: LogisticRegression - when: "{{ template == 'starter' }}" # CONFIGURATION ------------------------- _templates_suffix: "" From 4259fc88a1fe23db127c71404519020b345db37d Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Thu, 14 Dec 2023 00:04:43 +0100 Subject: [PATCH 18/48] Compared with e2e template --- copier.yaml | 4 ++ template/run.ipynb | 8 +++- tests/test_starter_template.py | 85 +--------------------------------- 3 files changed, 11 insertions(+), 86 deletions(-) diff --git a/copier.yaml b/copier.yaml index a0b5d30..121ae95 100644 --- a/copier.yaml +++ b/copier.yaml @@ -44,6 +44,10 @@ auto_format: type: bool help: "Auto-format the generated code with black, ruff and autoflake? \n (NOTE: you need to have these tools installed in your environment)" default: false +zenml_server_url: + type: str + help: "The URL of the ZenML server [Optional]" + default: "" # SUB-TEMPLATE PROMPT [starter] --------------------- use_step_params: diff --git a/template/run.ipynb b/template/run.ipynb index 97e4ba8..2d817a9 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -106,9 +106,13 @@ "metadata": {}, "outputs": [], "source": [ + "{%- if zenml_server_url!='' %}\n", + "!zenml connect --url {{zenml_server_url}}\n", + "{%- else %}\n", "zenml_server_url = \"PLEASE_UPDATE_ME\" # in the form \"https://URL_TO_SERVER\"\n", "\n", - "!zenml connect --url $zenml_server_url" + "!zenml connect --url $zenml_server_url\n", + "{%- endif %}" ] }, { @@ -1077,7 +1081,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c7c7bc27", + "id": "8f51a3a5", "metadata": {}, "outputs": [], "source": [] diff --git a/tests/test_starter_template.py b/tests/test_starter_template.py index fc6f836..8893f1d 100644 --- a/tests/test_starter_template.py +++ b/tests/test_starter_template.py @@ -32,13 +32,7 @@ def generate_and_run_project( tmp_path_factory: pytest.TempPathFactory, open_source_license: Optional[str] = "apache", - product_name: str = "e2e_pipeline_pytest", - hyperparameters_tuning: bool = True, - metric_compare_promotion: bool = True, - data_quality_checks: bool = True, - target_environment: str = "staging", - notify_on_failures: bool = True, - notify_on_successes: bool = False, + product_name: str = "starter_project", zenml_server_url: str = "", ): """Generate and run the starter project with different options.""" @@ -48,12 +42,6 @@ def generate_and_run_project( "version": "0.0.1", "open_source_license": str(open_source_license).lower(), "product_name": product_name, - "hyperparameters_tuning": hyperparameters_tuning, - "metric_compare_promotion": metric_compare_promotion, - "data_quality_checks": data_quality_checks, - "target_environment": target_environment, - "notify_on_failures": notify_on_failures, - "notify_on_successes": notify_on_successes, "zenml_server_url": zenml_server_url, } if open_source_license: @@ -137,77 +125,6 @@ def test_custom_product_name( product_name="custom_product_name", ) - -def test_no_hp_tuning( - clean_zenml_client, - tmp_path_factory: pytest.TempPathFactory, -): - """Test turning off hyperparameter tuning.""" - - generate_and_run_project( - tmp_path_factory=tmp_path_factory, hyperparameters_tuning=False - ) - - -def test_latest_promotion( - clean_zenml_client, - tmp_path_factory: pytest.TempPathFactory, -): - """Test using latest promotion.""" - - generate_and_run_project( - tmp_path_factory=tmp_path_factory, metric_compare_promotion=False - ) - - -def test_no_data_quality_checks( - clean_zenml_client, - tmp_path_factory: pytest.TempPathFactory, -): - """Test skipping Data Quality checks.""" - - generate_and_run_project( - tmp_path_factory=tmp_path_factory, - data_quality_checks=False, - ) - - -def test_production_environment( - clean_zenml_client, - tmp_path_factory: pytest.TempPathFactory, -): - """Test deploying to production stage.""" - - generate_and_run_project( - tmp_path_factory=tmp_path_factory, - target_environment="production", - ) - - -def test_no_notify_on_failure( - clean_zenml_client, - tmp_path_factory: pytest.TempPathFactory, -): - """Test skipping notification on failure.""" - - generate_and_run_project( - tmp_path_factory=tmp_path_factory, - notify_on_failures=False, - ) - - -def test_notify_on_success( - clean_zenml_client, - tmp_path_factory: pytest.TempPathFactory, -): - """Test skipping notification on success.""" - - generate_and_run_project( - tmp_path_factory=tmp_path_factory, - notify_on_successes=True, - ) - - def test_custom_zenml_server_url( clean_zenml_client, tmp_path_factory: pytest.TempPathFactory, From 7b218b5b8ba009a65764a61e44bc8fb9428b3891 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Thu, 14 Dec 2023 00:36:42 +0100 Subject: [PATCH 19/48] Removed templateized ipynb --- copier.yaml | 12 ------------ template/run.ipynb | 8 ++------ tests/test_starter_template.py | 2 -- 3 files changed, 2 insertions(+), 20 deletions(-) diff --git a/copier.yaml b/copier.yaml index 121ae95..00455ec 100644 --- a/copier.yaml +++ b/copier.yaml @@ -1,5 +1,4 @@ --- # GLOBAL PROMPT -------------------------------- - template: type: str help: >- @@ -44,17 +43,6 @@ auto_format: type: bool help: "Auto-format the generated code with black, ruff and autoflake? \n (NOTE: you need to have these tools installed in your environment)" default: false -zenml_server_url: - type: str - help: "The URL of the ZenML server [Optional]" - default: "" - -# SUB-TEMPLATE PROMPT [starter] --------------------- -use_step_params: - type: bool - help: "Would you like to see step parameters being used in the generated \n ZenML steps?" - default: true - when: "{{ template == 'starter' }}" # CONFIGURATION ------------------------- _templates_suffix: "" diff --git a/template/run.ipynb b/template/run.ipynb index 2d817a9..3728287 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -106,13 +106,9 @@ "metadata": {}, "outputs": [], "source": [ - "{%- if zenml_server_url!='' %}\n", - "!zenml connect --url {{zenml_server_url}}\n", - "{%- else %}\n", "zenml_server_url = \"PLEASE_UPDATE_ME\" # in the form \"https://URL_TO_SERVER\"\n", "\n", - "!zenml connect --url $zenml_server_url\n", - "{%- endif %}" + "!zenml connect --url $zenml_server_url" ] }, { @@ -1081,7 +1077,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8f51a3a5", + "id": "601d7eea", "metadata": {}, "outputs": [], "source": [] diff --git a/tests/test_starter_template.py b/tests/test_starter_template.py index 8893f1d..67c7420 100644 --- a/tests/test_starter_template.py +++ b/tests/test_starter_template.py @@ -33,7 +33,6 @@ def generate_and_run_project( tmp_path_factory: pytest.TempPathFactory, open_source_license: Optional[str] = "apache", product_name: str = "starter_project", - zenml_server_url: str = "", ): """Generate and run the starter project with different options.""" @@ -42,7 +41,6 @@ def generate_and_run_project( "version": "0.0.1", "open_source_license": str(open_source_license).lower(), "product_name": product_name, - "zenml_server_url": zenml_server_url, } if open_source_license: answers["email"] = "pytest@zenml.io" From f99caf25dec4bf778eae12c2cfb11743350942a8 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 10:06:08 +0100 Subject: [PATCH 20/48] Many things in this commit - Formatted, darglinted, added complex promotion logic, added two trainins runs etc --- template/{assets => .assets}/cloud_mcp.png | Bin .../cloud_mcp.png:Zone.Identifier | 0 .../cloud_mcp_predictions.png | Bin .../cloud_mcp_screenshot.png | Bin .../feature_engineering_pipeline.png | Bin ...e_engineering_pipeline.png:Zone.Identifier | 0 .../inference_pipeline.png | Bin .../inference_pipeline.png:Zone.Identifier | 0 .../{assets => .assets}/pipeline_overview.png | Bin .../pipeline_overview.png:Zone.Identifier | 0 .../{assets => .assets}/training_pipeline.png | Bin .../training_pipeline.png:Zone.Identifier | 0 template/configs/inference.yaml | 4 +- .../{training.yaml => training_rf.yaml} | 10 ++-- template/configs/training_sgd.yaml | 20 ++++++++ template/pipelines/feature_engineering.py | 3 ++ template/pipelines/inference.py | 20 ++++---- template/pipelines/training.py | 27 +++++----- template/run.ipynb | 24 +++------ template/run.py | 45 ++++++++++++----- template/steps/__init__.py | 6 +-- template/steps/data_loader.py | 2 +- template/steps/data_preprocessor.py | 5 +- template/steps/inference_predict.py | 8 +-- template/steps/inference_preprocessor.py | 2 +- template/steps/model_evaluator.py | 16 +++--- template/steps/model_promoter.py | 46 +++++++++++++----- template/steps/model_trainer.py | 25 ++++++---- 28 files changed, 165 insertions(+), 98 deletions(-) rename template/{assets => .assets}/cloud_mcp.png (100%) rename template/{assets => .assets}/cloud_mcp.png:Zone.Identifier (100%) rename template/{assets => .assets}/cloud_mcp_predictions.png (100%) rename template/{assets => .assets}/cloud_mcp_screenshot.png (100%) rename template/{assets => .assets}/feature_engineering_pipeline.png (100%) rename template/{assets => .assets}/feature_engineering_pipeline.png:Zone.Identifier (100%) rename template/{assets => .assets}/inference_pipeline.png (100%) rename template/{assets => .assets}/inference_pipeline.png:Zone.Identifier (100%) rename template/{assets => .assets}/pipeline_overview.png (100%) rename template/{assets => .assets}/pipeline_overview.png:Zone.Identifier (100%) rename template/{assets => .assets}/training_pipeline.png (100%) rename template/{assets => .assets}/training_pipeline.png:Zone.Identifier (100%) rename template/configs/{training.yaml => training_rf.yaml} (71%) create mode 100644 template/configs/training_sgd.yaml diff --git a/template/assets/cloud_mcp.png b/template/.assets/cloud_mcp.png similarity index 100% rename from template/assets/cloud_mcp.png rename to template/.assets/cloud_mcp.png diff --git a/template/assets/cloud_mcp.png:Zone.Identifier b/template/.assets/cloud_mcp.png:Zone.Identifier similarity index 100% rename from template/assets/cloud_mcp.png:Zone.Identifier rename to template/.assets/cloud_mcp.png:Zone.Identifier diff --git a/template/assets/cloud_mcp_predictions.png b/template/.assets/cloud_mcp_predictions.png similarity index 100% rename from template/assets/cloud_mcp_predictions.png rename to template/.assets/cloud_mcp_predictions.png diff --git a/template/assets/cloud_mcp_screenshot.png b/template/.assets/cloud_mcp_screenshot.png similarity index 100% rename from template/assets/cloud_mcp_screenshot.png rename to template/.assets/cloud_mcp_screenshot.png diff --git a/template/assets/feature_engineering_pipeline.png b/template/.assets/feature_engineering_pipeline.png similarity index 100% rename from template/assets/feature_engineering_pipeline.png rename to template/.assets/feature_engineering_pipeline.png diff --git a/template/assets/feature_engineering_pipeline.png:Zone.Identifier b/template/.assets/feature_engineering_pipeline.png:Zone.Identifier similarity index 100% rename from template/assets/feature_engineering_pipeline.png:Zone.Identifier rename to template/.assets/feature_engineering_pipeline.png:Zone.Identifier diff --git a/template/assets/inference_pipeline.png b/template/.assets/inference_pipeline.png similarity index 100% rename from template/assets/inference_pipeline.png rename to template/.assets/inference_pipeline.png diff --git a/template/assets/inference_pipeline.png:Zone.Identifier b/template/.assets/inference_pipeline.png:Zone.Identifier similarity index 100% rename from template/assets/inference_pipeline.png:Zone.Identifier rename to template/.assets/inference_pipeline.png:Zone.Identifier diff --git a/template/assets/pipeline_overview.png b/template/.assets/pipeline_overview.png similarity index 100% rename from template/assets/pipeline_overview.png rename to template/.assets/pipeline_overview.png diff --git a/template/assets/pipeline_overview.png:Zone.Identifier b/template/.assets/pipeline_overview.png:Zone.Identifier similarity index 100% rename from template/assets/pipeline_overview.png:Zone.Identifier rename to template/.assets/pipeline_overview.png:Zone.Identifier diff --git a/template/assets/training_pipeline.png b/template/.assets/training_pipeline.png similarity index 100% rename from template/assets/training_pipeline.png rename to template/.assets/training_pipeline.png diff --git a/template/assets/training_pipeline.png:Zone.Identifier b/template/.assets/training_pipeline.png:Zone.Identifier similarity index 100% rename from template/assets/training_pipeline.png:Zone.Identifier rename to template/.assets/training_pipeline.png:Zone.Identifier diff --git a/template/configs/inference.yaml b/template/configs/inference.yaml index b9fd29d..bed69a3 100644 --- a/template/configs/inference.yaml +++ b/template/configs/inference.yaml @@ -7,8 +7,8 @@ settings: # configuration of the Model Control Plane model_version: - name: breast_cancer_classifier - version: production + name: "breast_cancer_classifier" + version: "production" license: Apache 2.0 description: A breast cancer classifier tags: ["breast_cancer", "classifier"] \ No newline at end of file diff --git a/template/configs/training.yaml b/template/configs/training_rf.yaml similarity index 71% rename from template/configs/training.yaml rename to template/configs/training_rf.yaml index b1161dd..8d1bbc9 100644 --- a/template/configs/training.yaml +++ b/template/configs/training_rf.yaml @@ -8,11 +8,13 @@ settings: # configuration of the Model Control Plane model_version: name: breast_cancer_classifier + version: rf license: Apache 2.0 description: A breast cancer classifier tags: ["breast_cancer", "classifier"] -# pipeline configuration -model_type: "sgd" # Choose between rf/sgd -# train_dataset_id: ... -# test_dataset_id: ... \ No newline at end of file +# step configuration +steps: + model_trainer: + parameters: + model_type: "rf" # Choose between rf/sgd diff --git a/template/configs/training_sgd.yaml b/template/configs/training_sgd.yaml new file mode 100644 index 0000000..4f08681 --- /dev/null +++ b/template/configs/training_sgd.yaml @@ -0,0 +1,20 @@ +# environment configuration +settings: + docker: + required_integrations: + - sklearn + - pyarrow + +# configuration of the Model Control Plane +model_version: + name: breast_cancer_classifier + version: sgd + license: Apache 2.0 + description: A breast cancer classifier + tags: ["breast_cancer", "classifier"] + +# step configuration +steps: + model_trainer: + parameters: + model_type: "sgd" # Choose between rf/sgd diff --git a/template/pipelines/feature_engineering.py b/template/pipelines/feature_engineering.py index bd6946b..543afcd 100644 --- a/template/pipelines/feature_engineering.py +++ b/template/pipelines/feature_engineering.py @@ -34,6 +34,9 @@ def feature_engineering( normalize: If `True` dataset will be normalized with MinMaxScaler drop_columns: List of columns to drop from dataset target: Name of target column in dataset + + Returns: + The processed datasets (dataset_trn, dataset_tst). """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output diff --git a/template/pipelines/inference.py b/template/pipelines/inference.py index ebba44d..b9a299f 100644 --- a/template/pipelines/inference.py +++ b/template/pipelines/inference.py @@ -1,15 +1,11 @@ # {% include 'template/license_header' %} -from uuid import UUID -from typing import List, Optional - from steps import ( data_loader, - inference_preprocessor, inference_predict, + inference_preprocessor, ) -from zenml import pipeline, ArtifactVersionResponse, get_pipeline_context -from zenml.client import Client +from zenml import get_pipeline_context, pipeline from zenml.logger import get_logger logger = get_logger(__name__) @@ -19,16 +15,22 @@ def inference(random_state: str, target: str): """ Model inference pipeline. - + This is a pipeline that loads the inference data, processes it with the same preprocessing pipeline used in training, and runs inference with the trained model. + + Args: + random_state: Random state for reproducibility. + target: Name of target column in dataset. """ # Get the production model artifact model = get_pipeline_context().model_version.get_artifact("model") - + # Get the preprocess pipeline artifact associated with this version - preprocess_pipeline = get_pipeline_context().model_version.get_artifact("preprocess_pipeline") + preprocess_pipeline = get_pipeline_context().model_version.get_artifact( + "preprocess_pipeline" + ) # Link all the steps together by calling them and passing the output # of one step as the input of the next step. diff --git a/template/pipelines/training.py b/template/pipelines/training.py index 2e8b367..7819cd8 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -3,7 +3,7 @@ from typing import Optional from uuid import UUID -from steps import model_evaluator, model_trainer, model_promoter +from steps import model_evaluator, model_promoter, model_trainer from zenml import ExternalArtifact, pipeline from zenml.logger import get_logger @@ -18,21 +18,21 @@ def training( train_dataset_id: Optional[UUID] = None, test_dataset_id: Optional[UUID] = None, - min_train_accuracy: float = 0.0, - min_test_accuracy: float = 0.0, + target: Optional[str] = "target", ): """ Model training pipeline. - This is a pipeline that loads the data, processes it and splits - it into train and test sets, then search for best hyperparameters, - trains and evaluates a model. + This is a pipeline that loads the data from a preprocessing pipeline, + trains a model on it and evaluates the model. If it is the first model + to be trained, it will be promoted to production. If not, it will be + promoted only if it has a higher accuracy than the current production + model version. Args: - test_size: Size of holdout set for training 0.0..1.0 - drop_na: If `True` NA values will be removed from dataset - normalize: If `True` dataset will be normalized with MinMaxScaler - drop_columns: List of columns to drop from dataset + train_dataset_id: ID of the train dataset produced by feature engineering. + test_dataset_id: ID of the test dataset produced by feature engineering. + target: Name of target column in dataset. """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output @@ -45,16 +45,13 @@ def training( dataset_trn = ExternalArtifact(id=train_dataset_id) dataset_tst = ExternalArtifact(id=test_dataset_id) - model = model_trainer( - dataset_trn=dataset_trn, - ) + model = model_trainer(dataset_trn=dataset_trn, target=target) acc = model_evaluator( model=model, dataset_trn=dataset_trn, dataset_tst=dataset_tst, - min_train_accuracy=min_train_accuracy, - min_test_accuracy=min_test_accuracy, + target=target, ) model_promoter(accuracy=acc) diff --git a/template/run.ipynb b/template/run.ipynb index 3728287..2b9c0f1 100644 --- a/template/run.ipynb +++ b/template/run.ipynb @@ -17,7 +17,7 @@ "\n", "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n", "\n", - "\"Pipelines" + "\"Pipelines" ] }, { @@ -179,7 +179,7 @@ "your training. ZenML mostly gets out of your way when you're writing your Python\n", "code, as you'll see from the following cell.\n", "\n", - "\"Feature" + "\"Feature" ] }, { @@ -520,7 +520,7 @@ "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", "same data and then compare their performance.\n", "\n", - "\"Training" + "\"Training" ] }, { @@ -829,7 +829,7 @@ "source": [ "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", "\n", - "\"Model" + "\"Model" ] }, { @@ -874,7 +874,7 @@ "id": "2ecbc8cf", "metadata": {}, "source": [ - "\"Model" + "\"Model" ] }, { @@ -903,7 +903,7 @@ "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", "and generate predictions:\n", "\n", - "\"Inference" + "\"Inference" ] }, { @@ -1043,7 +1043,7 @@ "source": [ "You can also see all predictions ever created as a complete history in the dashboard:\n", "\n", - "\"Model" + "\"Model" ] }, { @@ -1073,14 +1073,6 @@ "* If you have questions or feedback... join our [**Slack Community**](https://zenml.io/slack) and become part of the ZenML family!\n", "* If you want to quickly get started with ZenML, check out the [ZenML Cloud](https://zenml.io/cloud)." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "601d7eea", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1099,7 +1091,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/template/run.py b/template/run.py index 7eceb6f..a970922 100644 --- a/template/run.py +++ b/template/run.py @@ -4,6 +4,7 @@ from typing import Optional import click +import yaml from pipelines import ( feature_engineering, inference, @@ -102,6 +103,17 @@ def main( (some of which may come from command line arguments, but most of which comes from the YAML config files) * launching the pipeline + + Args: + train_dataset_name: The name of the train dataset produced by feature engineering. + train_dataset_version_name: Version of the train dataset produced by feature engineering. + If not specified, a new version will be created. + test_dataset_name: The name of the test dataset produced by feature engineering. + test_dataset_version_name: Version of the test dataset produced by feature engineering. + If not specified, a new version will be created. + feature_pipeline: Whether to run the pipeline that creates the dataset. + training_pipeline: Whether to run the pipeline that trains the model. + inference_pipeline: Whether to run the pipeline that performs inference. """ client = Client() @@ -118,13 +130,10 @@ def main( ) run_args_feature = {} feature_engineering.with_options(**pipeline_args)(**run_args_feature) - logger.info("Feature Engineering pipeline finished successfully!") + logger.info("Feature Engineering pipeline finished successfully!\n\n") # Execute Training Pipeline if training_pipeline: - pipeline_args = {} - pipeline_args["config_path"] = os.path.join(config_folder, "training.yaml") - run_args_train = {} # If train_dataset_version_name is specified, use versioned artifacts @@ -145,26 +154,38 @@ def main( run_args_train["train_dataset_id"] = train_dataset_artifact_version.id run_args_train["test_dataset_id"] = test_dataset_artifact_version.id + # Run the SGD pipeline + pipeline_args = {} + pipeline_args["config_path"] = os.path.join(config_folder, "training_sgd.yaml") + training.with_options(**pipeline_args)(**run_args_train) + logger.info("Training pipeline with SGD finished successfully!\n\n") + + # Run the RF pipeline + pipeline_args = {} + pipeline_args["config_path"] = os.path.join(config_folder, "training_rf.yaml") training.with_options(**pipeline_args)(**run_args_train) - logger.info("Training pipeline finished successfully!") + logger.info("Training pipeline with RF finished successfully!\n\n") if inference_pipeline: - run_args_train = {} + run_args_inference = {} pipeline_args = {} pipeline_args["config_path"] = os.path.join(config_folder, "inference.yaml") - run_args_inference = {} - + # Configure the pipeline inference_configured = inference.with_options(**pipeline_args) - + # Fetch the production model - zenml_model = client.get_model_version("breast_cancer_classifier", "production") + with open(pipeline_args["config_path"], "r") as f: + config = yaml.load(f, Loader=yaml.FullLoader) + zenml_model = client.get_model_version( + config["model_version"]["name"], config["model_version"]["version"] + ) preprocess_pipeline_artifact = zenml_model.get_artifact("preprocess_pipeline") - + # Use the metadata of feature engineering pipeline artifact # to get the random state and target column random_state = preprocess_pipeline_artifact.run_metadata["random_state"].value - target = preprocess_pipeline_artifact.run_metadata['target'].value + target = preprocess_pipeline_artifact.run_metadata["target"].value run_args_inference["random_state"] = random_state run_args_inference["target"] = target diff --git a/template/steps/__init__.py b/template/steps/__init__.py index 2f85671..aee0212 100644 --- a/template/steps/__init__.py +++ b/template/steps/__init__.py @@ -18,9 +18,9 @@ from .model_evaluator import ( model_evaluator, ) +from .model_promoter import ( + model_promoter, +) from .model_trainer import ( model_trainer, ) -from .model_promoter import ( - model_promoter, -) \ No newline at end of file diff --git a/template/steps/data_loader.py b/template/steps/data_loader.py index 941cbe2..90e3955 100644 --- a/template/steps/data_loader.py +++ b/template/steps/data_loader.py @@ -25,9 +25,9 @@ def data_loader( https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines Args: + random_state: Random state for sampling is_inference: If `True` subset will be returned and target column will be removed from dataset. - random_state: Random state for sampling target: Name of target columns in dataset. Returns: diff --git a/template/steps/data_preprocessor.py b/template/steps/data_preprocessor.py index 78a0c72..66add2f 100644 --- a/template/steps/data_preprocessor.py +++ b/template/steps/data_preprocessor.py @@ -12,9 +12,9 @@ @step def data_preprocessor( + random_state: int, dataset_trn: pd.DataFrame, dataset_tst: pd.DataFrame, - random_state: int, drop_na: Optional[bool] = None, normalize: Optional[bool] = None, drop_columns: Optional[List[str]] = None, @@ -41,12 +41,13 @@ def data_preprocessor( https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines Args: + random_state: Random state for sampling. dataset_trn: The train dataset. dataset_tst: The test dataset. drop_na: If `True` all NA rows will be dropped. normalize: If `True` all numeric fields will be normalized. drop_columns: List of column names to drop. - random_state: Random state for sampling. + target: Name of target column in dataset. Returns: The processed datasets (dataset_trn, dataset_tst) and fitted `Pipeline` object. diff --git a/template/steps/inference_predict.py b/template/steps/inference_predict.py index 9e05e21..ef28087 100644 --- a/template/steps/inference_predict.py +++ b/template/steps/inference_predict.py @@ -16,9 +16,10 @@ # from typing import Any + import pandas as pd from typing_extensions import Annotated -from zenml import get_step_context, step +from zenml import step from zenml.logger import get_logger logger = get_logger(__name__) @@ -31,8 +32,8 @@ def inference_predict( ) -> Annotated[pd.Series, "predictions"]: """Predictions step. - This is an example of a predictions step that takes the data in and returns - predicted values. + This is an example of a predictions step that takes the data and model in + and returns predicted values. This step is parameterized, which allows you to configure the step independently of the step code, before running it in a pipeline. @@ -42,6 +43,7 @@ def inference_predict( https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines Args: + model: Trained model. dataset_inf: The inference dataset. Returns: diff --git a/template/steps/inference_preprocessor.py b/template/steps/inference_preprocessor.py index c7a5ae1..7503061 100644 --- a/template/steps/inference_preprocessor.py +++ b/template/steps/inference_preprocessor.py @@ -46,7 +46,7 @@ def inference_preprocessor( # artificially adding `target` column to avoid Pipeline issues dataset_inf[target] = pd.Series([1] * dataset_inf.shape[0]) dataset_inf = preprocess_pipeline.transform(dataset_inf) - dataset_inf.drop(columns=["target"], inplace=True) + dataset_inf.drop(columns=[target], inplace=True) ### YOUR CODE ENDS HERE ### return dataset_inf diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index 79892e8..b0b1994 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -1,8 +1,10 @@ # {% include 'template/license_header' %} +from typing import Optional + import pandas as pd from sklearn.base import ClassifierMixin -from zenml import step, log_artifact_metadata +from zenml import log_artifact_metadata, step from zenml.logger import get_logger logger = get_logger(__name__) @@ -15,6 +17,7 @@ def model_evaluator( dataset_tst: pd.DataFrame, min_train_accuracy: float = 0.0, min_test_accuracy: float = 0.0, + target: Optional[str] = "target", ) -> float: """Evaluate a trained model. @@ -46,19 +49,12 @@ def model_evaluator( dataset_tst: The test dataset. min_train_accuracy: Minimal acceptable training accuracy value. min_test_accuracy: Minimal acceptable testing accuracy value. - fail_on_accuracy_quality_gates: If `True` a `RuntimeException` is raised - upon not meeting one of the minimal accuracy thresholds. + target: Name of target column in dataset. - Retuns: + Returns: The model accuracy on the test set. - - Raises: - RuntimeError: if any of accuracies is lower than respective threshold """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # context = get_step_context() - # target = context.inputs["dataset_trn"].run_metadata['target'].value - target = "target" # Calculate the model accuracy on the train and test set trn_acc = model.score( diff --git a/template/steps/model_promoter.py b/template/steps/model_promoter.py index d8a048b..44bb537 100644 --- a/template/steps/model_promoter.py +++ b/template/steps/model_promoter.py @@ -1,6 +1,7 @@ # {% include 'template/license_header' %} from zenml import get_step_context, step +from zenml.client import Client from zenml.logger import get_logger logger = get_logger(__name__) @@ -10,14 +11,12 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: """Model promoter step. - This is an example of a step that conditionally promotes a model. - - This step is parameterized, which allows you to configure the step - independently of the step code, before running it in a pipeline. - In this example, the step can be configured with the accuracy of the model - and the target stage: - - https://docs.zenml.io/user-guide/advanced-guide/configure-steps-pipelines + This is an example of a step that conditionally promotes a model. It takes + in the accuracy of the model and the stage to promote the model to. If the + accuracy is below 80%, the model is not promoted. If it is above 80%, the + model is promoted to the stage indicated in the parameters. If there is + already a model in the indicated stage, the model with the higher accuracy + is promoted. Args: accuracy: Accuracy of the model. @@ -26,15 +25,40 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: Returns: Whether the model was promoted or not. """ + ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### + is_promoted = False + if accuracy < 0.8: logger.info( f"Model accuracy {accuracy*100:.2f}% is below 80% ! Not promoting model." ) - is_promoted = False else: logger.info(f"Model promoted to {stage}!") is_promoted = True - model_version = get_step_context().model_version - model_version.set_stage(stage, force=True) + # Get the model in the current context + current_model_version = get_step_context().model_version + + # Get the model that is in the production stage + client = Client() + try: + stage_model_version = client.get_model_version( + current_model_version.name, stage + ) + # We compare their metrics + prod_accuracy = ( + stage_model_version.get_artifact("model") + .run_metadata["test_accuracy"] + .value + ) + if accuracy > prod_accuracy: + # If current model has better metrics, we promote it + is_promoted = True + current_model_version.set_stage(stage, force=True) + except KeyError: + # If no such model exists, current one is promoted + is_promoted = True + current_model_version.set_stage(stage, force=True) + + ### YOUR CODE ENDS HERE ### return is_promoted diff --git a/template/steps/model_trainer.py b/template/steps/model_trainer.py index e7580ca..196745c 100644 --- a/template/steps/model_trainer.py +++ b/template/steps/model_trainer.py @@ -1,8 +1,11 @@ # {% include 'template/license_header' %} +from typing import Optional + import pandas as pd from sklearn.base import ClassifierMixin -from sklearn.tree import DecisionTreeClassifier +from sklearn.ensemble import RandomForestClassifier +from sklearn.linear_model import SGDClassifier from typing_extensions import Annotated from zenml import ArtifactConfig, step from zenml.logger import get_logger @@ -13,6 +16,8 @@ @step def model_trainer( dataset_trn: pd.DataFrame, + model_type: str = "sgd", + target: Optional[str] = "target", ) -> Annotated[ClassifierMixin, ArtifactConfig(name="model", is_model_artifact=True)]: """Configure and train a model on the training dataset. @@ -23,22 +28,25 @@ def model_trainer( Args: dataset_trn: The preprocessed train dataset. + model_type: The type of model to train. target: The name of the target column in the dataset. Returns: The trained model artifact. + + Raises: + ValueError: If the model type is not supported. """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - - # Use the dataset to fetch the target - # context = get_step_context() - # target = context.inputs["dataset_trn"].run_metadata['target'].value - target = "target" - # Initialize the model with the hyperparameters indicated in the step # parameters and train it on the training set. - model = DecisionTreeClassifier() + if model_type == "sgd": + model = SGDClassifier() + elif model_type == "rf": + model = RandomForestClassifier() + else: + raise ValueError(f"Unknown model type {model_type}") logger.info(f"Training model {model}...") model.fit( @@ -46,5 +54,4 @@ def model_trainer( dataset_trn[target], ) ### YOUR CODE ENDS HERE ### - return model From d5bc5e6985c6c22b797fd51202a80d7f45bb543f Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 10:28:18 +0100 Subject: [PATCH 21/48] Latest changes --- README.md | 23 +++++++++-------------- template/configs/training_rf.yaml | 8 +++----- template/configs/training_sgd.yaml | 8 +++----- template/pipelines/training.py | 4 +++- template/{run.ipynb => quickstart.ipynb} | 2 +- template/run.py | 2 +- template/steps/model_promoter.py | 2 +- 7 files changed, 21 insertions(+), 28 deletions(-) rename template/{run.ipynb => quickstart.ipynb} (99%) diff --git a/README.md b/README.md index 0660009..a91f854 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,16 @@ # 📜 ZenML Project Templates -This repository contains a collection of templates from which a ZenML project +This repository contains a starter template from which a simple ZenML project can be generated: a collection of steps, pipelines, stack configurations and other artifacts and useful resources that can get you started with ZenML. -🔥 **Do you have a personal project powered by ZenML that you would like to see here?** At -ZenML, we are looking for design partnerships and collaboration to help us +🔥 **Do you have a personal project powered by ZenML that you would like to see here?** + +At ZenML, we are looking for design partnerships and collaboration to help us better understand the real-world scenarios in which MLOps is being used and to build the best possible experience for our users. If you are interested in sharing all or parts of your project with us in the form of a ZenML project -template, please [join our Slack](https://zenml.io/slack-invite/) and leave us a +template, please [join our Slack](https://zenml.io/slack/) and leave us a message! ## 📦 Prerequisites @@ -18,7 +19,7 @@ To use the templates, you need to have Zenml and its `templates` extras installed: ```bash -pip install zenml[templates] +pip install "zenml[templates]" ``` ## 🚀 Generate a ZenML Project @@ -35,7 +36,7 @@ library and a set of Jinja2 templates to generate the project. So you may also interact with Copier directly to generate a project, e.g.: ```bash -copier gh:zenml-io/zenml-project-templates +copier gh:zenml-io/template-starter ``` You will be prompted to select the project template and enter various values for @@ -47,11 +48,5 @@ the same command again. If you want to skip the prompts to use the values you already entered and overwrite all files in the existing project, you can run: ```bash -copier -wf gh:zenml-io/zenml-project-templates -``` - -## 📃 List of Project Templates - -| Project Template | Tags | Description | -|------------------|----------|-----------------------------------------------------------------------------------| -| [ZenML Starter](https://github.com/zenml-io/zenml-project-templates/tree/main/starter) | basic scikit-learn | All the basic ML ingredients you need to get you started with ZenML: parameterized steps, a model training pipeline, a flexible configuration and a simple CLI. All created around a representative and versatile model training use-case implemented with the scikit-learn library. | +copier -wf gh:zenml-io/template-starter +``` \ No newline at end of file diff --git a/template/configs/training_rf.yaml b/template/configs/training_rf.yaml index 8d1bbc9..ddb68d6 100644 --- a/template/configs/training_rf.yaml +++ b/template/configs/training_rf.yaml @@ -13,8 +13,6 @@ model_version: description: A breast cancer classifier tags: ["breast_cancer", "classifier"] -# step configuration -steps: - model_trainer: - parameters: - model_type: "rf" # Choose between rf/sgd +# Configure the pipeline +parameters: + model_type: "rf" # Choose between rf/sgd diff --git a/template/configs/training_sgd.yaml b/template/configs/training_sgd.yaml index 4f08681..4318c38 100644 --- a/template/configs/training_sgd.yaml +++ b/template/configs/training_sgd.yaml @@ -13,8 +13,6 @@ model_version: description: A breast cancer classifier tags: ["breast_cancer", "classifier"] -# step configuration -steps: - model_trainer: - parameters: - model_type: "sgd" # Choose between rf/sgd +# Configure the pipeline +parameters: + model_type: "sgd" # Choose between rf/sgd \ No newline at end of file diff --git a/template/pipelines/training.py b/template/pipelines/training.py index 7819cd8..b5d0f80 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -19,6 +19,7 @@ def training( train_dataset_id: Optional[UUID] = None, test_dataset_id: Optional[UUID] = None, target: Optional[str] = "target", + model_type: Optional[str] = "sgd", ): """ Model training pipeline. @@ -33,6 +34,7 @@ def training( train_dataset_id: ID of the train dataset produced by feature engineering. test_dataset_id: ID of the test dataset produced by feature engineering. target: Name of target column in dataset. + model_type: The type of model to train. """ ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output @@ -45,7 +47,7 @@ def training( dataset_trn = ExternalArtifact(id=train_dataset_id) dataset_tst = ExternalArtifact(id=test_dataset_id) - model = model_trainer(dataset_trn=dataset_trn, target=target) + model = model_trainer(dataset_trn=dataset_trn, target=target, model_type=model_type) acc = model_evaluator( model=model, diff --git a/template/run.ipynb b/template/quickstart.ipynb similarity index 99% rename from template/run.ipynb rename to template/quickstart.ipynb index 2b9c0f1..e36df48 100644 --- a/template/run.ipynb +++ b/template/quickstart.ipynb @@ -31,7 +31,7 @@ "required!\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](\n", - "https://colab.research.google.com/github/zenml-io/zenml/blob/main/examples/quickstart/run.ipynb)" + "https://colab.research.google.com/github/zenml-io/zenml/blob/main/examples/quickstart/quickstart.ipynb)" ] }, { diff --git a/template/run.py b/template/run.py index a970922..c430558 100644 --- a/template/run.py +++ b/template/run.py @@ -168,7 +168,7 @@ def main( if inference_pipeline: run_args_inference = {} - pipeline_args = {} + pipeline_args = {"enable_cache": False} pipeline_args["config_path"] = os.path.join(config_folder, "inference.yaml") # Configure the pipeline diff --git a/template/steps/model_promoter.py b/template/steps/model_promoter.py index 44bb537..2126c06 100644 --- a/template/steps/model_promoter.py +++ b/template/steps/model_promoter.py @@ -51,7 +51,7 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: .run_metadata["test_accuracy"] .value ) - if accuracy > prod_accuracy: + if float(accuracy) > float(prod_accuracy): # If current model has better metrics, we promote it is_promoted = True current_model_version.set_stage(stage, force=True) From 88036a28295e44805d281f68fdd9198955bc558d Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 10:30:03 +0100 Subject: [PATCH 22/48] README --- template/README.md | 18 +++--------------- 1 file changed, 3 insertions(+), 15 deletions(-) diff --git a/template/README.md b/template/README.md index 56ec468..d9efbc2 100644 --- a/template/README.md +++ b/template/README.md @@ -16,7 +16,9 @@ Along the way we will also show you how to: - Automatically version, track, and cache data, models, and other artifacts - Transition your ML models from development to production -## :cloud: Run on Colab +Pipelines Overview + +## 🏃 Run on Colab You can use Google Colab to see ZenML in action, no signup / installation required! @@ -71,21 +73,7 @@ python run.py --inference-pipeline ## :dart: Dashboard -You can also take a look at our **dashboard** where you can inspect the quickstart -pipeline run and much more. Simply execute: - -```shell -# only once you've already run `zenml up` -zenml show -``` - -## :sponge: Clean up - -To clean up, simply spin down the ZenML server. -```shell -zenml down -``` ## :bulb: Learn More From fdca688917dc721631754d48bb04593dd6ef4047 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 10:41:21 +0100 Subject: [PATCH 23/48] New readme --- template/README.md | 89 +++++++++++++++++++++++++++++++++++++-- template/quickstart.ipynb | 2 +- 2 files changed, 86 insertions(+), 5 deletions(-) diff --git a/template/README.md b/template/README.md index d9efbc2..86c506a 100644 --- a/template/README.md +++ b/template/README.md @@ -4,20 +4,22 @@ Build your first MLOps pipelines with ZenML. ## :earth_americas: Overview -This starter project demonstrates a complete minimalist MLOps journey. It features: +This repository is a minimalistic MLOps project intended as a starting point to learn how to put ML workflows in production. It features: - A feature engineering pipeline that loads data and prepares it for training. - A training pipeline that loads the preprocessed dataset and trains a model. - A batch inference pipeline that runs predictions on the trained model with new data. +This is a representation of how it will all come together: + +Pipelines Overview + Along the way we will also show you how to: - Structure your code into MLOps pipelines - Automatically version, track, and cache data, models, and other artifacts - Transition your ML models from development to production -Pipelines Overview - ## 🏃 Run on Colab You can use Google Colab to see ZenML in action, no signup / installation required! @@ -71,12 +73,91 @@ python run.py --training-pipeline --train-dataset-version-name=1 --test-dataset- python run.py --inference-pipeline ``` -## :dart: Dashboard +## 🌵 Learning MLOps + +### 🥇 Step 1: Load your data and execute feature engineering + +We'll start off by importing our data. In this quickstart we'll be working with +[the Breast Cancer](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic) dataset +which is publicly available on the UCI Machine Learning Repository. The task is a classification +problem, to predict whether a patient is diagnosed with breast cancer or not. + +When you're getting started with a machine learning problem you'll want to do +something similar to this: import your data and get it in the right shape for +your training. + +Feature engineering pipeline + +### ⌚ Step 2: Training pipeline + +Now that we have our data it makes sense to train some models to get a sense of +how difficult the task is. The Breast Cancer dataset is sufficiently large and complex +that it's unlikely we'll be able to train a model that behaves perfectly since the problem +is inherently complex, but we can get a sense of what a reasonable baseline looks like. + +We'll start with two simple models, a SGD Classifier and a Random Forest +Classifier, both batteries-included from `sklearn`. We'll train them both on the +same data and then compare their performance. + +Training pipeline + +### 💯 Step 3: Associating a model with your pipeline +You can see it is relatively easy to train ML models using ZenML pipelines. But it can be somewhat clunky to track +all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*, +which is a central register of all your ML models. +You can easily create a ZenML `Model` and associate it with your pipelines using the `ModelVersion` object. The interesting part is that ZenML goes ahead and links all artifacts produced by the +pipelines to that model version, including the two pickle files that represent our +SGD and RandomForest classifier. We can see all artifacts directly from the model +version object. + +If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard: + +Model Control Plane + +There is a lot more you can do with ZenML models, including the ability to +track metrics by adding metadata to it, or having them persist in a model +registry. However, these topics can be explored more in the +[ZenML docs](https://docs.zenml.io). + +For now, we will use the ZenML model control plane to promote our best +model to `production`. You can do this by simply setting the `stage` of +your chosen model version to the `production` tag. + +Of course, normally one would only promote the model by comparing to all other model +versions and doing some other tests. But that's a bit more advanced use-case. See the +[e2e_batch example](https://github.com/zenml-io/zenml/tree/main/examples/e2e) to get +more insight into that sort of flow! + +Model Control Plane + +Once the model is promoted, we can now consume the right model version in our +batch inference pipeline directly. Let's see how that works. + +The batch inference pipeline simply takes the model marked as `production` and runs inference on it +with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory and generate predictions. Apart from the loading the model, we must also load the preprocessing pipeline that we ran in feature engineering, +so that we can do the exact steps that we did on training time, in inference time. Let's bring it all together: + +Inference pipeline + +ZenML automatically links all artifacts to the `production` model version as well, including the predictions +that were returned in the pipeline. This completes the MLOps loop of training to inference: + +You can also see all predictions ever created as a complete history in the dashboard: + +Model Control Plane + +### 🫅 Step 4: Consuming the model in production ## :bulb: Learn More +You're a legit MLOps engineer now! You trained two models, evaluated them against +a test set, registered the best one with the ZenML model control plane, +and served some predictions. You also learned how to iterate on your models and +data by using some of the ZenML utility abstractions. You saw how to view your +artifacts and stacks via the client as well as the ZenML Dashboard. + If you want to learn more about ZenML as a tool, then the [:page_facing_up: **ZenML Docs**](https://docs.zenml.io/) are the perfect place to get started. diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index e36df48..ea37432 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -903,7 +903,7 @@ "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", "and generate predictions:\n", "\n", - "\"Inference" + "\"Inference" ] }, { From 8ce022bd1291c2f9d5bf9bc79e62c22f9f044a60 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 13:04:39 +0100 Subject: [PATCH 24/48] LatesT --- template/README.md | 96 ++++++++++++++++++++++++++++++++-------------- template/run.py | 11 +++++- 2 files changed, 77 insertions(+), 30 deletions(-) diff --git a/template/README.md b/template/README.md index 86c506a..438cac7 100644 --- a/template/README.md +++ b/template/README.md @@ -73,44 +73,71 @@ python run.py --training-pipeline --train-dataset-version-name=1 --test-dataset- python run.py --inference-pipeline ``` -## 🌵 Learning MLOps +## 🌵 Learning MLOps with ZenML -### 🥇 Step 1: Load your data and execute feature engineering +This project is also a great source of learning about some fundamental MLOps concepts. In sum, there are four exemplary steps happening, that can be mapped onto many other projects: -We'll start off by importing our data. In this quickstart we'll be working with +
+ 🥇 Step 1: Load your data and execute feature engineering + +We'll start off by importing our data. In this project, we'll be working with [the Breast Cancer](https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic) dataset which is publicly available on the UCI Machine Learning Repository. The task is a classification problem, to predict whether a patient is diagnosed with breast cancer or not. When you're getting started with a machine learning problem you'll want to do something similar to this: import your data and get it in the right shape for -your training. +your training. Here are the typical steps within a feature engineering pipeline. + +The steps can be found defined the [steps](steps/) directory, while the [pipelines](pipelines/) directory has the pipeline code to connect them together. + +Feature engineering pipeline + +To execute the feature engineer pipelines, run: -Feature engineering pipeline +```python +python run.py --feature-pipeline +``` -### ⌚ Step 2: Training pipeline +After the pipeline has run, the pipeline will produce some logs like: + +```shell +The latest feature engineering pipeline produced the following artifacts: -Now that we have our data it makes sense to train some models to get a sense of -how difficult the task is. The Breast Cancer dataset is sufficiently large and complex -that it's unlikely we'll be able to train a model that behaves perfectly since the problem +1. Train Dataset - Name: dataset_trn, Version Name: 1 +2. Test Dataset: Name: dataset_tst, Version Name: 1 +``` + +We will use these versions in the next pipeline. + +
+ +
+ ⌚ Step 2: Training pipeline + +Now that our data is prepared, it makes sense to train some models to get a sense of how difficult the task is. The Breast Cancer dataset is sufficiently large and complex that it's unlikely we'll be able to train a model that behaves perfectly since the problem is inherently complex, but we can get a sense of what a reasonable baseline looks like. We'll start with two simple models, a SGD Classifier and a Random Forest Classifier, both batteries-included from `sklearn`. We'll train them both on the same data and then compare their performance. -Training pipeline +Training pipeline + +Run it by using the ID's from the first step: + +```python +python run.py --training-pipeline --train-dataset-version-name 1 --test-dataset-version-name 1 +``` -### 💯 Step 3: Associating a model with your pipeline +ZenML offers a *Model Control Plane*, +which is a central register of all your ML models. Each run of the training pipeline will produce a ZenML Model Version. -You can see it is relatively easy to train ML models using ZenML pipelines. But it can be somewhat clunky to track -all the models produced as you develop your experiments and use-cases. Luckily, ZenML offers a *Model Control Plane*, -which is a central register of all your ML models. +```shell +zenml model list +``` -You can easily create a ZenML `Model` and associate it with your pipelines using the `ModelVersion` object. The interesting part is that ZenML goes ahead and links all artifacts produced by the -pipelines to that model version, including the two pickle files that represent our -SGD and RandomForest classifier. We can see all artifacts directly from the model -version object. +This will show you a new `breast_cancer_classifier` model with two versions, `sgd` and `rf` created. You can find out how this was configured in the [YAML pipeline configuration files](configs/). If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard: @@ -121,17 +148,30 @@ track metrics by adding metadata to it, or having them persist in a model registry. However, these topics can be explored more in the [ZenML docs](https://docs.zenml.io). +
+ +
+ 💯 Step 3: Associating a model with your pipeline + For now, we will use the ZenML model control plane to promote our best model to `production`. You can do this by simply setting the `stage` of your chosen model version to the `production` tag. -Of course, normally one would only promote the model by comparing to all other model -versions and doing some other tests. But that's a bit more advanced use-case. See the -[e2e_batch example](https://github.com/zenml-io/zenml/tree/main/examples/e2e) to get -more insight into that sort of flow! +```shell +zenml model version update breast_cancer_classifier rf --stage production +``` + +Even if we did this manually for illustration, if you inspect the [promoter code](steps/model_promoter.py), we can see this would already have been done automatically by the training pipeline. It uses the previous production metrics to compare to the current one, and promotes the new model if it performs better on the test set. Model Control Plane +Again, if you are a [ZenML Cloud](https://zenml.io/cloud) user, you would be able to see all this in the cloud dashboard. + +
+ +
+ 🫅 Step 4: Consuming the model in production + Once the model is promoted, we can now consume the right model version in our batch inference pipeline directly. Let's see how that works. @@ -139,16 +179,16 @@ The batch inference pipeline simply takes the model marked as `production` and r with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory and generate predictions. Apart from the loading the model, we must also load the preprocessing pipeline that we ran in feature engineering, so that we can do the exact steps that we did on training time, in inference time. Let's bring it all together: -Inference pipeline - ZenML automatically links all artifacts to the `production` model version as well, including the predictions that were returned in the pipeline. This completes the MLOps loop of training to inference: -You can also see all predictions ever created as a complete history in the dashboard: +Inference pipeline + +You can also see all predictions ever created as a complete history in the dashboard (Again only for [ZenML Cloud](https://zenml.io/cloud) users): Model Control Plane -### 🫅 Step 4: Consuming the model in production +
## :bulb: Learn More @@ -162,9 +202,7 @@ If you want to learn more about ZenML as a tool, then the [:page_facing_up: **ZenML Docs**](https://docs.zenml.io/) are the perfect place to get started. -Already have an MLOps stack in mind? ZenML most likely has -[**:link: Integrations**](https://docs.zenml.io/stacks-and-components/component-guide) -for whatever tools you plan to use. +The best way to get a production ZenML instance up and running with all batteries included is the [ZenML Cloud](https://zenml.io/cloud). Check it out! Also, make sure to join our Slack diff --git a/template/run.py b/template/run.py index c430558..28b309d 100644 --- a/template/run.py +++ b/template/run.py @@ -130,7 +130,16 @@ def main( ) run_args_feature = {} feature_engineering.with_options(**pipeline_args)(**run_args_feature) - logger.info("Feature Engineering pipeline finished successfully!\n\n") + logger.info("Feature Engineering pipeline finished successfully!\n") + + train_dataset_artifact = client.get_artifact_version(train_dataset_name) + test_dataset_artifact = client.get_artifact_version(test_dataset_name) + logger.info( + "The latest feature engineering pipeline produced the following " + f"artifacts: \n\n1. Train Dataset - Name: {train_dataset_name}, " + f"Version Name: {train_dataset_artifact.version} \n2. Test Dataset: " + f"Name: {test_dataset_name}, Version Name: {test_dataset_artifact.version}" + ) # Execute Training Pipeline if training_pipeline: From dfcbe514309908dd57b5d58c838666a910ca0253 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 13:05:19 +0100 Subject: [PATCH 25/48] LatesT --- template/quickstart.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index ea37432..f7639b5 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -96,7 +96,7 @@ "id": "e3955ff1", "metadata": {}, "source": [ - "Optional: If you are using ZenML Cloud, execute the following cell with your tenant URL. Otherwise ignore." + "Optional: If you are using [ZenML Cloud](https://zenml.io/cloud), execute the following cell with your tenant URL. Otherwise ignore." ] }, { From 2af724f7cff240b3816f72f4811cee6def518ffd Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 13:24:51 +0100 Subject: [PATCH 26/48] Woot --- template/quickstart.ipynb | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index f7639b5..c5b0cd6 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -323,7 +323,7 @@ "id": "1785c303", "metadata": {}, "source": [ - "Let's run this again with a slightly different test size, to create another dataset:" + "Let's run this again with a slightly different test size, to create more datasets:" ] }, { @@ -336,12 +336,22 @@ "feature_engineering(test_size=0.35)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e1d8546", + "metadata": {}, + "outputs": [], + "source": [ + "feature_engineering(test_size=0.25)" + ] + }, { "cell_type": "markdown", "id": "64bb7206", "metadata": {}, "source": [ - "Notice that the data loader step was **cached**, while the rest of the pipeline was rerun. \n", + "Notice the second time around, the data loader step was **cached**, while the rest of the pipeline was rerun. \n", "This is because ZenML automatically determined that nothing had changed in the data loader step, \n", "so it didn't need to rerun it." ] From 47437758bbc9cdd5c828820bbe0d4e9194e55050 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Thu, 14 Dec 2023 20:16:38 +0100 Subject: [PATCH 27/48] Further cleanup, fix caching and artifact version fetching, move random sate --- copier.yaml | 9 +- .../.assets/cloud_mcp.png:Zone.Identifier | 0 ...e_engineering_pipeline.png:Zone.Identifier | 0 .../inference_pipeline.png:Zone.Identifier | 0 .../pipeline_overview.png:Zone.Identifier | 0 .../training_pipeline.png:Zone.Identifier | 0 template/pipelines/feature_engineering.py | 3 +- template/quickstart.ipynb | 2054 ++++++++++++++++- template/run.py | 2 +- template/steps/data_loader.py | 2 - template/steps/data_preprocessor.py | 5 +- template/steps/data_splitter.py | 2 - template/steps/inference_predict.py | 3 - template/steps/inference_preprocessor.py | 3 - template/steps/model_evaluator.py | 4 - template/steps/model_promoter.py | 3 - template/steps/model_trainer.py | 3 - 17 files changed, 1993 insertions(+), 100 deletions(-) delete mode 100644 template/.assets/cloud_mcp.png:Zone.Identifier delete mode 100644 template/.assets/feature_engineering_pipeline.png:Zone.Identifier delete mode 100644 template/.assets/inference_pipeline.png:Zone.Identifier delete mode 100644 template/.assets/pipeline_overview.png:Zone.Identifier delete mode 100644 template/.assets/training_pipeline.png:Zone.Identifier diff --git a/copier.yaml b/copier.yaml index 00455ec..ae3c266 100644 --- a/copier.yaml +++ b/copier.yaml @@ -1,11 +1,4 @@ --- # GLOBAL PROMPT -------------------------------- -template: - type: str - help: >- - The project template you would like to use - choices: - ZenML Starter: starter - default: starter project_name: type: str help: Short name for your project @@ -46,7 +39,7 @@ auto_format: # CONFIGURATION ------------------------- _templates_suffix: "" -_subdirectory: "{{ template }}/template" +_subdirectory: "./template" _exclude: - /README.md _tasks: diff --git a/template/.assets/cloud_mcp.png:Zone.Identifier b/template/.assets/cloud_mcp.png:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/template/.assets/feature_engineering_pipeline.png:Zone.Identifier b/template/.assets/feature_engineering_pipeline.png:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/template/.assets/inference_pipeline.png:Zone.Identifier b/template/.assets/inference_pipeline.png:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/template/.assets/pipeline_overview.png:Zone.Identifier b/template/.assets/pipeline_overview.png:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/template/.assets/training_pipeline.png:Zone.Identifier b/template/.assets/training_pipeline.png:Zone.Identifier deleted file mode 100644 index e69de29..0000000 diff --git a/template/pipelines/feature_engineering.py b/template/pipelines/feature_engineering.py index 543afcd..799bdea 100644 --- a/template/pipelines/feature_engineering.py +++ b/template/pipelines/feature_engineering.py @@ -21,6 +21,7 @@ def feature_engineering( normalize: Optional[bool] = None, drop_columns: Optional[List[str]] = None, target: Optional[str] = "target", + random_state: int = 17 ): """ Feature engineering pipeline. @@ -34,6 +35,7 @@ def feature_engineering( normalize: If `True` dataset will be normalized with MinMaxScaler drop_columns: List of columns to drop from dataset target: Name of target column in dataset + random_state: Random state to configure the data loader Returns: The processed datasets (dataset_trn, dataset_tst). @@ -41,7 +43,6 @@ def feature_engineering( ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. - random_state = random.randint(0, 100) raw_data = data_loader(random_state=random_state, target=target) dataset_trn, dataset_tst = data_splitter( dataset=raw_data, diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index c5b0cd6..d53f1f5 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -113,10 +113,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "081d5616", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[?25l\u001b[1;35mInitializing the ZenML global configuration version to 0.52.0\u001b[0m\n", + "\u001b[1;35mCreating database tables\u001b[0m\n", + "\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[1;35mCreating default workspace 'default' ...\u001b[0m\n", + "\u001b[1;35mCreating default stack in workspace default...\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[1;35mCreating default user 'default' ...\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[1;35mSetting the global active workspace to 'default'.\u001b[0m\n", + "\u001b[33mSetting the global active stack to default.\u001b[0m\n", + "\u001b[1;35mSetting the repo active workspace to 'default'.\u001b[0m\n", + "\u001b[33mSetting the repo active stack to default.\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[33mIntegration aws failed to register flavors. Error: No module named 'boto3'\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[1;35mReloading configuration file /home/apenner/PycharmProjects/template-starter/template/.zen/config.yaml\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mZenML repository initialized at \u001b[0m\n", + "\u001b[2;35m/home/apenner/PycharmProjects/template-starter/\u001b[0m\u001b[2;95mtemplate.\u001b[0m\n", + "\u001b[2;32m⠸\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", + "\u001b[2;36m/home/apenner/PycharmProjects/template-starter/template.\u001b[0m\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Initializing ZenML repository at \n", + "/home/apenner/PycharmProjects/template-starter/template.\n", + "\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mThe local active stack was initialized to \u001b[0m\u001b[2;32m'default'\u001b[0m\u001b[2;36m. This local configuration \u001b[0m\n", + "\u001b[2;36mwill only take effect when you're running ZenML from the initialized repository \u001b[0m\n", + "\u001b[2;36mroot, or from a subdirectory. For more information on repositories and \u001b[0m\n", + "\u001b[2;36mconfigurations, please visit \u001b[0m\n", + "\u001b[2;4;94mhttps://docs.zenml.io/user-guide/starter-guide/understand-stacks.\u001b[0m\n", + "\u001b[?25l\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'default'\u001b[0m\n", + "\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to 'default'...t'...\u001b[0m\n", + "\u001b[1A\u001b[2K" + ] + } + ], "source": [ "# Initialize ZenML and set the default stack\n", "!zenml init\n", @@ -126,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -184,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3cd974d1", "metadata": {}, "outputs": [], @@ -242,10 +321,231 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "d838e2ea", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" + ] + }, + { + "data": { + "text/html": [ + "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...17.33184.602019.00.16220.66560.71190.26540.46010.118900
120.5717.77132.901326.00.084740.078640.08690.070170.18120.05667...23.41158.801956.00.12380.18660.24160.18600.27500.089020
219.6921.25130.001203.00.109600.159900.19740.127900.20690.05999...25.53152.501709.00.14440.42450.45040.24300.36130.087580
311.4220.3877.58386.10.142500.283900.24140.105200.25970.09744...26.5098.87567.70.20980.86630.68690.25750.66380.173000
420.2914.34135.101297.00.100300.132800.19800.104300.18090.05883...16.67152.201575.00.13740.20500.40000.16250.23640.076780
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" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.3001 0.14710 0.2419 \n", + "1 0.07864 0.0869 0.07017 0.1812 \n", + "2 0.15990 0.1974 0.12790 0.2069 \n", + "3 0.28390 0.2414 0.10520 0.2597 \n", + "4 0.13280 0.1980 0.10430 0.1809 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "0 0.07871 ... 17.33 184.60 2019.0 \n", + "1 0.05667 ... 23.41 158.80 1956.0 \n", + "2 0.05999 ... 25.53 152.50 1709.0 \n", + "3 0.09744 ... 26.50 98.87 567.7 \n", + "4 0.05883 ... 16.67 152.20 1575.0 \n", + "\n", + " worst smoothness worst compactness worst concavity worst concave points \\\n", + "0 0.1622 0.6656 0.7119 0.2654 \n", + "1 0.1238 0.1866 0.2416 0.1860 \n", + "2 0.1444 0.4245 0.4504 0.2430 \n", + "3 0.2098 0.8663 0.6869 0.2575 \n", + "4 0.1374 0.2050 0.4000 0.1625 \n", + "\n", + " worst symmetry worst fractal dimension target \n", + "0 0.4601 0.11890 0 \n", + "1 0.2750 0.08902 0 \n", + "2 0.3613 0.08758 0 \n", + "3 0.6638 0.17300 0 \n", + "4 0.2364 0.07678 0 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = data_loader_simplified(random_state=42)\n", "df.head()" @@ -266,7 +566,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "b50a9537", "metadata": {}, "outputs": [], @@ -278,11 +578,11 @@ " normalize: Optional[bool] = None,\n", " drop_columns: Optional[List[str]] = None,\n", " target: Optional[str] = \"target\",\n", + " random_state: int = 17\n", "):\n", " \"\"\"Feature engineering pipeline.\"\"\"\n", " # Link all the steps together by calling them and passing the output\n", " # of one step as the input of the next step.\n", - " random_state = random.randint(0, 100)\n", " raw_data = data_loader(random_state=random_state, target=target)\n", " dataset_trn, dataset_tst = data_splitter(\n", " dataset=raw_data,\n", @@ -310,10 +610,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "1e0aa9af", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.487s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.920s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.973s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_14-19_12_49_255201\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.661s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], "source": [ "feature_engineering()" ] @@ -328,20 +651,32 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "9faaf0c2", - "metadata": {}, - "outputs": [], - "source": [ - "feature_engineering(test_size=0.35)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e1d8546", - "metadata": {}, - "outputs": [], + "execution_count": 7, + "id": "658c0570-2607-4b97-a72d-d45c92633e48", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.723s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.139s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_14-19_12_53_441184\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.115s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], "source": [ "feature_engineering(test_size=0.25)" ] @@ -356,6 +691,47 @@ "so it didn't need to rerun it." ] }, + { + "cell_type": "markdown", + "id": "5bc6849d-31ac-4c08-9ca2-cf7f5f35ccbf", + "metadata": {}, + "source": [ + "Let's run this again with a slightly different test size and random state, to disable the cache and to create more datasets:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1e1d8546", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 3)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.546s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.717s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.979s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_14-19_12_56_904810\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.450s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], + "source": [ + "feature_engineering(test_size=0.25, random_state=104)" + ] + }, { "cell_type": "markdown", "id": "6c42078a", @@ -373,10 +749,441 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "8cd3cc8c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mDeploying a local ZenML server with name 'local'.\u001b[0m\n", + "\u001b[?25l\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: 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"\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + 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"\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", + "zen_server, flavor: local)'.\n", + "\n", + "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1;35mConnecting ZenML to the 'local' local ZenML server (http://127.0.0.1:8237).\u001b[0m\n", + "\u001b[1;35mUpdated the global store configuration.\u001b[0m\n", + "\u001b[1;35mConnected ZenML to the 'local' local ZenML server (http://127.0.0.1:8237).\u001b[0m\n", + "\u001b[2;36mThe local ZenML dashboard is available at \u001b[0m\u001b[2;32m'http://127.0.0.1:8237'\u001b[0m\u001b[2;36m. You can \u001b[0m\n", + "\u001b[2;36mconnect to it using the \u001b[0m\u001b[2;32m'default'\u001b[0m\u001b[2;36m username and an empty password. \u001b[0m\n", + "\u001b[1;35mAutomatically opening the dashboard in your browser. To disable this, set the env variable AUTO_OPEN_DASHBOARD=false.\u001b[0m\n" + ] + } + ], "source": [ "from zenml.environment import Environment\n", "from zenml.zen_stores.rest_zen_store import RestZenStore\n", @@ -401,10 +1208,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "f208b200", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "feature_engineering-2023_12_14-19_12_56_904810\n" + ] + } + ], "source": [ "client = Client()\n", "run = client.get_pipeline(\"feature_engineering\").last_run\n", @@ -421,22 +1236,423 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "34283e89", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'dataset_trn': ArtifactVersionResponse(id=UUID('403585cd-2076-413e-a733-9694fcb571bf'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 58, 668117), updated=datetime.datetime(2023, 12, 14, 19, 12, 58, 668122), user=UserResponse(id=UUID('b11443aa-7044-4002-a761-fca964afead0'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 615395), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 615400), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), artifact=ArtifactResponse(id=UUID('6aabc759-379b-488e-aaa2-2e12970aa9eb'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 51, 32970), updated=datetime.datetime(2023, 12, 14, 19, 12, 51, 32975)), metadata=None, name='dataset_trn'), version='3', uri='/home/apenner/.config/zenml/local_stores/0dd2527d-9162-4251-b3b7-ee2347bb67eb/data_preprocessor/dataset_trn/2ba4cef3-f9f6-4367-81fa-c0c5f0cce925', type=, materializer=Source(module='zenml.materializers.pandas_materializer', attribute='PandasMaterializer', type=), data_type=Source(module='pandas.core.frame', attribute='DataFrame', type=, package_name='pandas', version='2.1.3')), metadata=None),\n", + " 'preprocess_pipeline': ArtifactVersionResponse(id=UUID('69d8d1f8-c2ed-486c-97be-f893dcda6fc4'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 59, 287651), updated=datetime.datetime(2023, 12, 14, 19, 12, 59, 287656), user=UserResponse(id=UUID('b11443aa-7044-4002-a761-fca964afead0'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 615395), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 615400), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), artifact=ArtifactResponse(id=UUID('d64b5f26-cbc8-4c3b-814e-3b0dfc7bc1ea'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 51, 633531), updated=datetime.datetime(2023, 12, 14, 19, 12, 51, 633536)), metadata=None, name='preprocess_pipeline'), version='3', uri='/home/apenner/.config/zenml/local_stores/0dd2527d-9162-4251-b3b7-ee2347bb67eb/data_preprocessor/preprocess_pipeline/2ba4cef3-f9f6-4367-81fa-c0c5f0cce925', type=, materializer=Source(module='zenml.integrations.sklearn.materializers.sklearn_materializer', attribute='SklearnMaterializer', type=), data_type=Source(module='sklearn.pipeline', attribute='Pipeline', type=, package_name='scikit-learn', version='1.2.2')), metadata=None),\n", + " 'dataset_tst': ArtifactVersionResponse(id=UUID('fd9465df-b108-4b80-8a5f-a954a5e7f327'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 59, 9630), updated=datetime.datetime(2023, 12, 14, 19, 12, 59, 9640), user=UserResponse(id=UUID('b11443aa-7044-4002-a761-fca964afead0'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 615395), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 615400), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), artifact=ArtifactResponse(id=UUID('79131989-6e0b-4e9f-833a-b52b6a9108dc'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 51, 361091), updated=datetime.datetime(2023, 12, 14, 19, 12, 51, 361095)), metadata=None, name='dataset_tst'), version='3', uri='/home/apenner/.config/zenml/local_stores/0dd2527d-9162-4251-b3b7-ee2347bb67eb/data_preprocessor/dataset_tst/2ba4cef3-f9f6-4367-81fa-c0c5f0cce925', type=, materializer=Source(module='zenml.materializers.pandas_materializer', attribute='PandasMaterializer', type=), data_type=Source(module='pandas.core.frame', attribute='DataFrame', type=, package_name='pandas', version='2.1.3')), metadata=None)}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "run.steps[\"data_preprocessor\"].outputs" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "bceb0312", "metadata": {}, - "outputs": [], - "source": [ - "# Read one of the datasets. This is the one with a 0.3 test split\n", + "outputs": [ + { + "data": { + "text/html": [ + "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
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..................................................................
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405 rows × 31 columns

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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
44412.04028.1476.85449.90.087520.060000.0236700.023770.18540.05698...33.3387.24567.60.104100.097260.055240.055470.24040.066391
4789.67613.1464.12272.50.125500.220400.1188000.070380.20570.09575...18.0469.47328.10.200600.366300.291300.107500.28480.136401
21010.44015.4666.62329.60.105300.077220.0066430.012160.17880.06450...19.8073.47395.40.134100.115300.026390.044640.26150.082691
29912.43017.0078.60477.30.075570.034540.0134200.016990.14720.05561...20.2181.76515.90.084090.047120.022370.028320.19010.059321
51314.47024.9995.81656.40.088370.123000.1009000.038900.18720.06341...31.73113.50808.90.134000.420200.404000.120500.31870.102301
..................................................................
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405 rows × 31 columns

\n", + "
" + ], + "text/plain": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "444 12.040 28.14 76.85 449.9 0.08752 \n", + "478 9.676 13.14 64.12 272.5 0.12550 \n", + "210 10.440 15.46 66.62 329.6 0.10530 \n", + "299 12.430 17.00 78.60 477.3 0.07557 \n", + "513 14.470 24.99 95.81 656.4 0.08837 \n", + ".. ... ... ... ... ... \n", + "71 16.070 19.65 104.10 817.7 0.09168 \n", + "106 10.510 20.19 68.64 334.2 0.11220 \n", + "270 14.410 19.73 96.03 651.0 0.08757 \n", + "435 27.420 26.27 186.90 2501.0 0.10840 \n", + "102 22.270 19.67 152.80 1509.0 0.13260 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "444 0.06000 0.023670 0.02377 0.1854 \n", + "478 0.22040 0.118800 0.07038 0.2057 \n", + "210 0.07722 0.006643 0.01216 0.1788 \n", + "299 0.03454 0.013420 0.01699 0.1472 \n", + "513 0.12300 0.100900 0.03890 0.1872 \n", + ".. ... ... ... ... \n", + "71 0.08424 0.097690 0.06638 0.1798 \n", + "106 0.13030 0.064760 0.03068 0.1922 \n", + "270 0.16760 0.136200 0.06602 0.1714 \n", + "435 0.19880 0.363500 0.16890 0.2061 \n", + "102 0.27680 0.426400 0.18230 0.2556 \n", + "\n", + " mean fractal dimension ... worst texture worst perimeter worst area \\\n", + "444 0.05698 ... 33.33 87.24 567.6 \n", + "478 0.09575 ... 18.04 69.47 328.1 \n", + "210 0.06450 ... 19.80 73.47 395.4 \n", + "299 0.05561 ... 20.21 81.76 515.9 \n", + "513 0.06341 ... 31.73 113.50 808.9 \n", + ".. ... ... ... ... ... \n", + "71 0.05391 ... 24.56 128.80 1223.0 \n", + "106 0.07782 ... 22.75 72.62 374.4 \n", + "270 0.07192 ... 22.13 101.70 767.3 \n", + "435 0.05623 ... 31.37 251.20 4254.0 \n", + "102 0.07039 ... 28.01 206.80 2360.0 \n", + "\n", + " worst smoothness worst compactness worst concavity \\\n", + "444 0.10410 0.09726 0.05524 \n", + "478 0.20060 0.36630 0.29130 \n", + "210 0.13410 0.11530 0.02639 \n", + "299 0.08409 0.04712 0.02237 \n", + "513 0.13400 0.42020 0.40400 \n", + ".. ... ... ... \n", + "71 0.15000 0.20450 0.28290 \n", + "106 0.13000 0.20490 0.12950 \n", + "270 0.09983 0.24720 0.22200 \n", + "435 0.13570 0.42560 0.68330 \n", + "102 0.17010 0.69970 0.96080 \n", + "\n", + " worst concave points worst symmetry worst fractal dimension target \n", + "444 0.05547 0.2404 0.06639 1 \n", + "478 0.10750 0.2848 0.13640 1 \n", + "210 0.04464 0.2615 0.08269 1 \n", + "299 0.02832 0.1901 0.05932 1 \n", + "513 0.12050 0.3187 0.10230 1 \n", + ".. ... ... ... ... \n", + "71 0.15200 0.2650 0.06387 0 \n", + "106 0.06136 0.2383 0.09026 1 \n", + "270 0.10210 0.2272 0.08799 1 \n", + "435 0.26250 0.2641 0.07427 0 \n", + "102 0.29100 0.4055 0.09789 0 \n", + "\n", + "[405 rows x 31 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Load an artifact to verify you can fetch it\n", "dataset_trn_artifact_version.load()" @@ -535,7 +2150,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "fccf1bd9", "metadata": {}, "outputs": [], @@ -596,7 +2211,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "1aa98f2f", "metadata": {}, "outputs": [], @@ -645,10 +2260,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "c64885ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.487s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", + "\u001b[1;35mTest accuracy=90.44%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.150s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_14-19_13_24_502719\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.816s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], "source": [ "# Use a random forest model with the chosen datasets.\n", "# We need to pass the ID's of the datasets into the function\n", @@ -663,10 +2348,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "4300c82f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.314s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "\u001b[1;35mTrain accuracy=89.88%\u001b[0m\n", + "\u001b[1;35mTest accuracy=86.76%\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.164s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_14-19_13_26_761730\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.654s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], "source": [ "# Use a SGD classifier\n", "sgd_run = training(\n", @@ -690,10 +2445,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "d95810b1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# The evaluator returns a float value with the accuracy\n", "rf_run.steps[\"model_evaluator\"].output.load() > sgd_run.steps[\"model_evaluator\"].output.load()" @@ -721,7 +2487,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "99ca00c0", "metadata": {}, "outputs": [], @@ -739,10 +2505,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "0e78a520", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 2)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m was created implicitly.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36msgd\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_14-19_13_28_867038\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.301s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], "source": [ "# Let's train the SGD model and set the version name to \"sgd\"\n", "pipeline_settings[\"model_version\"].version = \"sgd\"\n", @@ -761,10 +2550,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "9b8e0002", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mNew model version \u001b[0m\u001b[1;36mrf\u001b[1;35m was created.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_14-19_13_30_626808\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.321s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], "source": [ "# Let's train the RF model and set the version name to \"rf\"\n", "pipeline_settings[\"model_version\"].version = \"rf\"\n", @@ -792,10 +2603,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "fbb25913", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id=UUID('aa651410-ae18-437b-b55b-81ed7d85e093') permission_denied=False body=ModelResponseBody(created=datetime.datetime(2023, 12, 14, 19, 13, 28, 743771), updated=datetime.datetime(2023, 12, 14, 19, 13, 28, 743774), user=UserResponse(id=UUID('b11443aa-7044-4002-a761-fca964afead0'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 615395), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 615400), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), tags=[TagResponseModel(id=UUID('f84d2acb-50c0-45aa-9e31-b0b2b5cadeb1'), created=datetime.datetime(2023, 12, 14, 19, 13, 28, 747775), updated=datetime.datetime(2023, 12, 14, 19, 13, 28, 747778), missing_permissions=False, name='breast_cancer', color=, tagged_count=1), TagResponseModel(id=UUID('217927ee-4157-441a-93b3-151ad3724ebb'), created=datetime.datetime(2023, 12, 14, 19, 13, 28, 769699), updated=datetime.datetime(2023, 12, 14, 19, 13, 28, 769701), missing_permissions=False, name='classifier', color=, tagged_count=1)], latest_version='rf', workspace=WorkspaceResponse(id=UUID('73b57054-1d66-4b08-bb61-9d74d2ef6180'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 111293), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 111299)), metadata=None, name='default')) metadata=ModelResponseMetadata(workspace=WorkspaceResponse(id=UUID('73b57054-1d66-4b08-bb61-9d74d2ef6180'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 111293), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 111299)), metadata=None, name='default'), license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None) name='breast_cancer_classifier'\n", + "Model breast_cancer_classifier has 2 versions\n" + ] + }, + { + "data": { + "text/plain": [ + "('sgd', 'rf')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", "print(zenml_model)\n", @@ -818,10 +2648,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "31211413", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier()" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Let's load the RF version\n", "rf_zenml_model_version = client.get_model_version(\"breast_cancer_classifier\", \"rf\")\n", @@ -859,7 +2703,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "26b718f8", "metadata": {}, "outputs": [], @@ -918,7 +2762,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "92c4c7dc", "metadata": {}, "outputs": [], @@ -949,7 +2793,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "37c409bd", "metadata": {}, "outputs": [], @@ -987,10 +2831,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "61bf5939", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" + ] + } + ], "source": [ "pipeline_settings = {\"enable_cache\": False}\n", "\n", @@ -1006,10 +2858,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "ff3402f1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mExecuting a new run.\u001b[0m\n", + "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mDataset with 28 records loaded!\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.642s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.847s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", + "\u001b[33mYou specified both an ID as well as a version of the artifact_versions. Ignoring the version and fetching the artifact_versions by ID.\u001b[0m\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype):\n", + "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", + " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", + "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.678s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mRun \u001b[0m\u001b[1;36minference-2023_12_14-19_13_32_534526\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.331s\u001b[1;35m.\u001b[0m\n", + "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" + ] + } + ], "source": [ "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", @@ -1034,10 +2917,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "id": "e191d019", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 0\n", + "2 0\n", + "3 1\n", + "4 1\n", + "5 0\n", + "6 0\n", + "7 0\n", + "8 0\n", + "9 1\n", + "10 1\n", + "11 0\n", + "12 1\n", + "13 0\n", + "14 1\n", + "15 0\n", + "16 1\n", + "17 1\n", + "18 1\n", + "19 0\n", + "20 1\n", + "21 1\n", + "22 0\n", + "23 1\n", + "24 1\n", + "25 1\n", + "26 1\n", + "27 1\n", + "Name: series, dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Fetch production model\n", "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", @@ -1101,7 +3023,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.13" } }, "nbformat": 4, diff --git a/template/run.py b/template/run.py index 28b309d..2b30807 100644 --- a/template/run.py +++ b/template/run.py @@ -1,4 +1,4 @@ -# {% include 'templates/license_header' %} +# {% include 'template/license_header' %} import os from typing import Optional diff --git a/template/steps/data_loader.py b/template/steps/data_loader.py index 90e3955..4df6b8a 100644 --- a/template/steps/data_loader.py +++ b/template/steps/data_loader.py @@ -33,7 +33,6 @@ def data_loader( Returns: The dataset artifact as Pandas DataFrame and name of target column. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### dataset = load_breast_cancer(as_frame=True) inference_size = int(len(dataset.target) * 0.05) dataset: pd.DataFrame = dataset.frame @@ -45,5 +44,4 @@ def data_loader( dataset.drop(inference_subset.index, inplace=True) dataset.reset_index(drop=True, inplace=True) logger.info(f"Dataset with {len(dataset)} records loaded!") - ### YOUR CODE ENDS HERE ### return dataset diff --git a/template/steps/data_preprocessor.py b/template/steps/data_preprocessor.py index 66add2f..f343407 100644 --- a/template/steps/data_preprocessor.py +++ b/template/steps/data_preprocessor.py @@ -30,7 +30,7 @@ def data_preprocessor( it is suitable for model training. It takes in a dataset as an input step artifact and performs any necessary preprocessing steps like cleaning, feature engineering, feature selection, etc. It then returns the processed - dataset as an step output artifact. + dataset as a step output artifact. This step is parameterized, which allows you to configure the step independently of the step code, before running it in a pipeline. @@ -52,7 +52,6 @@ def data_preprocessor( Returns: The processed datasets (dataset_trn, dataset_tst) and fitted `Pipeline` object. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # We use the sklearn pipeline to chain together multiple preprocessing steps preprocess_pipeline = Pipeline([("passthrough", "passthrough")]) if drop_na: @@ -72,6 +71,4 @@ def data_preprocessor( artifact_name="preprocess_pipeline", metadata={"random_state": random_state, "target": target}, ) - - ### YOUR CODE ENDS HERE ### return dataset_trn, dataset_tst, preprocess_pipeline diff --git a/template/steps/data_splitter.py b/template/steps/data_splitter.py index dbab815..3b8eb57 100644 --- a/template/steps/data_splitter.py +++ b/template/steps/data_splitter.py @@ -34,7 +34,6 @@ def data_splitter( Returns: The split dataset: dataset_trn, dataset_tst. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### dataset_trn, dataset_tst = train_test_split( dataset, test_size=test_size, @@ -43,5 +42,4 @@ def data_splitter( ) dataset_trn = pd.DataFrame(dataset_trn, columns=dataset.columns) dataset_tst = pd.DataFrame(dataset_tst, columns=dataset.columns) - ### YOUR CODE ENDS HERE ### return dataset_trn, dataset_tst diff --git a/template/steps/inference_predict.py b/template/steps/inference_predict.py index ef28087..60f9267 100644 --- a/template/steps/inference_predict.py +++ b/template/steps/inference_predict.py @@ -49,11 +49,8 @@ def inference_predict( Returns: The predictions as pandas series """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # run prediction from memory predictions = model.predict(dataset_inf) predictions = pd.Series(predictions, name="predicted") - ### YOUR CODE ENDS HERE ### - return predictions diff --git a/template/steps/inference_preprocessor.py b/template/steps/inference_preprocessor.py index 7503061..d12247e 100644 --- a/template/steps/inference_preprocessor.py +++ b/template/steps/inference_preprocessor.py @@ -42,11 +42,8 @@ def inference_preprocessor( Returns: The processed dataframe: dataset_inf. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # artificially adding `target` column to avoid Pipeline issues dataset_inf[target] = pd.Series([1] * dataset_inf.shape[0]) dataset_inf = preprocess_pipeline.transform(dataset_inf) dataset_inf.drop(columns=[target], inplace=True) - ### YOUR CODE ENDS HERE ### - return dataset_inf diff --git a/template/steps/model_evaluator.py b/template/steps/model_evaluator.py index b0b1994..426d26e 100644 --- a/template/steps/model_evaluator.py +++ b/template/steps/model_evaluator.py @@ -54,8 +54,6 @@ def model_evaluator( Returns: The model accuracy on the test set. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### - # Calculate the model accuracy on the train and test set trn_acc = model.score( dataset_trn.drop(columns=[target]), @@ -85,6 +83,4 @@ def model_evaluator( metadata={"train_accuracy": float(trn_acc), "test_accuracy": float(tst_acc)}, artifact_name="model", ) - - ### YOUR CODE ENDS HERE ### return float(tst_acc) diff --git a/template/steps/model_promoter.py b/template/steps/model_promoter.py index 2126c06..d00b773 100644 --- a/template/steps/model_promoter.py +++ b/template/steps/model_promoter.py @@ -25,7 +25,6 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: Returns: Whether the model was promoted or not. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### is_promoted = False if accuracy < 0.8: @@ -59,6 +58,4 @@ def model_promoter(accuracy: float, stage: str = "production") -> bool: # If no such model exists, current one is promoted is_promoted = True current_model_version.set_stage(stage, force=True) - - ### YOUR CODE ENDS HERE ### return is_promoted diff --git a/template/steps/model_trainer.py b/template/steps/model_trainer.py index 196745c..c351de6 100644 --- a/template/steps/model_trainer.py +++ b/template/steps/model_trainer.py @@ -37,8 +37,6 @@ def model_trainer( Raises: ValueError: If the model type is not supported. """ - - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Initialize the model with the hyperparameters indicated in the step # parameters and train it on the training set. if model_type == "sgd": @@ -53,5 +51,4 @@ def model_trainer( dataset_trn.drop(columns=[target]), dataset_trn[target], ) - ### YOUR CODE ENDS HERE ### return model From bfd271a0357e243fd4e3c7748501bc32f9e20844 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 15:19:45 +0100 Subject: [PATCH 28/48] readme update --- template/README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/template/README.md b/template/README.md index 438cac7..0133525 100644 --- a/template/README.md +++ b/template/README.md @@ -39,10 +39,9 @@ git clone https://github.com/zenml-io/zenml.git cd zenml/examples/quickstart ``` -### :arrow_forward: Run Locally Now we're ready to start. You have two options for running the quickstart locally: -#### Option 1 (*Recommended*) - Interactively explore the quickstart using Jupyter Notebook: +#### Option 1 - Interactively explore the quickstart using Jupyter Notebook: ```bash pip install notebook jupyter notebook From 0374dcdd2f4e5d60da95d1f3ff6c3889935819fe Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 15:21:01 +0100 Subject: [PATCH 29/48] took out final comment --- template/pipelines/feature_engineering.py | 1 - template/pipelines/training.py | 1 - 2 files changed, 2 deletions(-) diff --git a/template/pipelines/feature_engineering.py b/template/pipelines/feature_engineering.py index 799bdea..912cda2 100644 --- a/template/pipelines/feature_engineering.py +++ b/template/pipelines/feature_engineering.py @@ -40,7 +40,6 @@ def feature_engineering( Returns: The processed datasets (dataset_trn, dataset_tst). """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. raw_data = data_loader(random_state=random_state, target=target) diff --git a/template/pipelines/training.py b/template/pipelines/training.py index b5d0f80..7eebebb 100644 --- a/template/pipelines/training.py +++ b/template/pipelines/training.py @@ -36,7 +36,6 @@ def training( target: Name of target column in dataset. model_type: The type of model to train. """ - ### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ### # Link all the steps together by calling them and passing the output # of one step as the input of the next step. From c04dfbc6454a8090c002c81a15347e421c1e1d06 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Thu, 14 Dec 2023 15:23:33 +0100 Subject: [PATCH 30/48] Notebook cleaned --- template/quickstart.ipynb | 2018 +------------------------------------ 1 file changed, 52 insertions(+), 1966 deletions(-) diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index d53f1f5..174b765 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -113,89 +113,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "081d5616", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[?25l\u001b[1;35mInitializing the ZenML global configuration version to 0.52.0\u001b[0m\n", - "\u001b[1;35mCreating database tables\u001b[0m\n", - "\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[1;35mCreating default workspace 'default' ...\u001b[0m\n", - "\u001b[1;35mCreating default stack in workspace default...\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[1;35mCreating default user 'default' ...\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[1;35mSetting the global active workspace to 'default'.\u001b[0m\n", - "\u001b[33mSetting the global active stack to default.\u001b[0m\n", - "\u001b[1;35mSetting the repo active workspace to 'default'.\u001b[0m\n", - "\u001b[33mSetting the repo active stack to default.\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[33mIntegration aws failed to register flavors. Error: No module named 'boto3'\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[1;35mReloading configuration file /home/apenner/PycharmProjects/template-starter/template/.zen/config.yaml\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mZenML repository initialized at \u001b[0m\n", - "\u001b[2;35m/home/apenner/PycharmProjects/template-starter/\u001b[0m\u001b[2;95mtemplate.\u001b[0m\n", - "\u001b[2;32m⠸\u001b[0m\u001b[2;36m Initializing ZenML repository at \u001b[0m\n", - "\u001b[2;36m/home/apenner/PycharmProjects/template-starter/template.\u001b[0m\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Initializing ZenML repository at \n", - "/home/apenner/PycharmProjects/template-starter/template.\n", - "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[2;36mThe local active stack was initialized to \u001b[0m\u001b[2;32m'default'\u001b[0m\u001b[2;36m. This local configuration \u001b[0m\n", - "\u001b[2;36mwill only take effect when you're running ZenML from the initialized repository \u001b[0m\n", - "\u001b[2;36mroot, or from a subdirectory. For more information on repositories and \u001b[0m\n", - "\u001b[2;36mconfigurations, please visit \u001b[0m\n", - "\u001b[2;4;94mhttps://docs.zenml.io/user-guide/starter-guide/understand-stacks.\u001b[0m\n", - "\u001b[?25l\u001b[2;36mActive repository stack set to: \u001b[0m\u001b[2;32m'default'\u001b[0m\n", - "\u001b[2K\u001b[32m⠋\u001b[0m Setting the repository active stack to 'default'...t'...\u001b[0m\n", - "\u001b[1A\u001b[2K" - ] - } - ], + "outputs": [], "source": [ "# Initialize ZenML and set the default stack\n", "!zenml init\n", @@ -205,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "79f775f2", "metadata": {}, "outputs": [], @@ -263,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "3cd974d1", "metadata": {}, "outputs": [], @@ -321,231 +242,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "d838e2ea", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n" - ] - }, - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...17.33184.602019.00.16220.66560.71190.26540.46010.118900
120.5717.77132.901326.00.084740.078640.08690.070170.18120.05667...23.41158.801956.00.12380.18660.24160.18600.27500.089020
219.6921.25130.001203.00.109600.159900.19740.127900.20690.05999...25.53152.501709.00.14440.42450.45040.24300.36130.087580
311.4220.3877.58386.10.142500.283900.24140.105200.25970.09744...26.5098.87567.70.20980.86630.68690.25750.66380.173000
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" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "0 17.99 10.38 122.80 1001.0 0.11840 \n", - "1 20.57 17.77 132.90 1326.0 0.08474 \n", - "2 19.69 21.25 130.00 1203.0 0.10960 \n", - "3 11.42 20.38 77.58 386.1 0.14250 \n", - "4 20.29 14.34 135.10 1297.0 0.10030 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "0 0.27760 0.3001 0.14710 0.2419 \n", - "1 0.07864 0.0869 0.07017 0.1812 \n", - "2 0.15990 0.1974 0.12790 0.2069 \n", - "3 0.28390 0.2414 0.10520 0.2597 \n", - "4 0.13280 0.1980 0.10430 0.1809 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "0 0.07871 ... 17.33 184.60 2019.0 \n", - "1 0.05667 ... 23.41 158.80 1956.0 \n", - "2 0.05999 ... 25.53 152.50 1709.0 \n", - "3 0.09744 ... 26.50 98.87 567.7 \n", - "4 0.05883 ... 16.67 152.20 1575.0 \n", - "\n", - " worst smoothness worst compactness worst concavity worst concave points \\\n", - "0 0.1622 0.6656 0.7119 0.2654 \n", - "1 0.1238 0.1866 0.2416 0.1860 \n", - "2 0.1444 0.4245 0.4504 0.2430 \n", - "3 0.2098 0.8663 0.6869 0.2575 \n", - "4 0.1374 0.2050 0.4000 0.1625 \n", - "\n", - " worst symmetry worst fractal dimension target \n", - "0 0.4601 0.11890 0 \n", - "1 0.2750 0.08902 0 \n", - "2 0.3613 0.08758 0 \n", - "3 0.6638 0.17300 0 \n", - "4 0.2364 0.07678 0 \n", - "\n", - "[5 rows x 31 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df = data_loader_simplified(random_state=42)\n", "df.head()" @@ -566,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "b50a9537", "metadata": {}, "outputs": [], @@ -610,33 +310,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "1e0aa9af", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.487s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.920s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.973s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_14-19_12_49_255201\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.661s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "feature_engineering()" ] @@ -651,32 +328,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "658c0570-2607-4b97-a72d-d45c92633e48", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.723s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m1.139s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_14-19_12_53_441184\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.115s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "feature_engineering(test_size=0.25)" ] @@ -701,33 +356,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "1e1d8546", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mfeature_engineering\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 3)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 541 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.546s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_splitter\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.717s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.979s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mfeature_engineering-2023_12_14-19_12_56_904810\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.450s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "feature_engineering(test_size=0.25, random_state=104)" ] @@ -749,441 +381,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "8cd3cc8c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mDeploying a local ZenML server with name 'local'.\u001b[0m\n", - "\u001b[?25l\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - 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"zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - 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"zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠧\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠋\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠙\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠹\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠸\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠼\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠴\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠦\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠇\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[32m⠏\u001b[0m Starting service 'LocalZenServer[67a95a27-d110-4e87-a6f1-a1d374144cb9] (type: \n", - "zen_server, flavor: local)'.\n", - "\n", - "\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[1;35mConnecting ZenML to the 'local' local ZenML server (http://127.0.0.1:8237).\u001b[0m\n", - "\u001b[1;35mUpdated the global store configuration.\u001b[0m\n", - "\u001b[1;35mConnected ZenML to the 'local' local ZenML server (http://127.0.0.1:8237).\u001b[0m\n", - "\u001b[2;36mThe local ZenML dashboard is available at \u001b[0m\u001b[2;32m'http://127.0.0.1:8237'\u001b[0m\u001b[2;36m. You can \u001b[0m\n", - "\u001b[2;36mconnect to it using the \u001b[0m\u001b[2;32m'default'\u001b[0m\u001b[2;36m username and an empty password. \u001b[0m\n", - "\u001b[1;35mAutomatically opening the dashboard in your browser. To disable this, set the env variable AUTO_OPEN_DASHBOARD=false.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "from zenml.environment import Environment\n", "from zenml.zen_stores.rest_zen_store import RestZenStore\n", @@ -1208,18 +409,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "f208b200", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "feature_engineering-2023_12_14-19_12_56_904810\n" - ] - } - ], + "outputs": [], "source": [ "client = Client()\n", "run = client.get_pipeline(\"feature_engineering\").last_run\n", @@ -1236,421 +429,20 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "34283e89", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'dataset_trn': ArtifactVersionResponse(id=UUID('403585cd-2076-413e-a733-9694fcb571bf'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 58, 668117), updated=datetime.datetime(2023, 12, 14, 19, 12, 58, 668122), user=UserResponse(id=UUID('b11443aa-7044-4002-a761-fca964afead0'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 615395), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 615400), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), artifact=ArtifactResponse(id=UUID('6aabc759-379b-488e-aaa2-2e12970aa9eb'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 51, 32970), updated=datetime.datetime(2023, 12, 14, 19, 12, 51, 32975)), metadata=None, name='dataset_trn'), version='3', uri='/home/apenner/.config/zenml/local_stores/0dd2527d-9162-4251-b3b7-ee2347bb67eb/data_preprocessor/dataset_trn/2ba4cef3-f9f6-4367-81fa-c0c5f0cce925', type=, materializer=Source(module='zenml.materializers.pandas_materializer', attribute='PandasMaterializer', type=), data_type=Source(module='pandas.core.frame', attribute='DataFrame', type=, package_name='pandas', version='2.1.3')), metadata=None),\n", - " 'preprocess_pipeline': ArtifactVersionResponse(id=UUID('69d8d1f8-c2ed-486c-97be-f893dcda6fc4'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 59, 287651), updated=datetime.datetime(2023, 12, 14, 19, 12, 59, 287656), user=UserResponse(id=UUID('b11443aa-7044-4002-a761-fca964afead0'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 615395), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 615400), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), artifact=ArtifactResponse(id=UUID('d64b5f26-cbc8-4c3b-814e-3b0dfc7bc1ea'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 51, 633531), updated=datetime.datetime(2023, 12, 14, 19, 12, 51, 633536)), metadata=None, name='preprocess_pipeline'), version='3', uri='/home/apenner/.config/zenml/local_stores/0dd2527d-9162-4251-b3b7-ee2347bb67eb/data_preprocessor/preprocess_pipeline/2ba4cef3-f9f6-4367-81fa-c0c5f0cce925', type=, materializer=Source(module='zenml.integrations.sklearn.materializers.sklearn_materializer', attribute='SklearnMaterializer', type=), data_type=Source(module='sklearn.pipeline', attribute='Pipeline', type=, package_name='scikit-learn', version='1.2.2')), metadata=None),\n", - " 'dataset_tst': ArtifactVersionResponse(id=UUID('fd9465df-b108-4b80-8a5f-a954a5e7f327'), permission_denied=False, body=ArtifactVersionResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 59, 9630), updated=datetime.datetime(2023, 12, 14, 19, 12, 59, 9640), user=UserResponse(id=UUID('b11443aa-7044-4002-a761-fca964afead0'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 615395), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 615400), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), artifact=ArtifactResponse(id=UUID('79131989-6e0b-4e9f-833a-b52b6a9108dc'), permission_denied=False, body=ArtifactResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 51, 361091), updated=datetime.datetime(2023, 12, 14, 19, 12, 51, 361095)), metadata=None, name='dataset_tst'), version='3', uri='/home/apenner/.config/zenml/local_stores/0dd2527d-9162-4251-b3b7-ee2347bb67eb/data_preprocessor/dataset_tst/2ba4cef3-f9f6-4367-81fa-c0c5f0cce925', type=, materializer=Source(module='zenml.materializers.pandas_materializer', attribute='PandasMaterializer', type=), data_type=Source(module='pandas.core.frame', attribute='DataFrame', type=, package_name='pandas', version='2.1.3')), metadata=None)}" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "run.steps[\"data_preprocessor\"].outputs" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "bceb0312", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
44412.04028.1476.85449.90.087520.060000.0236700.023770.18540.05698...33.3387.24567.60.104100.097260.055240.055470.24040.066391
4789.67613.1464.12272.50.125500.220400.1188000.070380.20570.09575...18.0469.47328.10.200600.366300.291300.107500.28480.136401
21010.44015.4666.62329.60.105300.077220.0066430.012160.17880.06450...19.8073.47395.40.134100.115300.026390.044640.26150.082691
29912.43017.0078.60477.30.075570.034540.0134200.016990.14720.05561...20.2181.76515.90.084090.047120.022370.028320.19010.059321
51314.47024.9995.81656.40.088370.123000.1009000.038900.18720.06341...31.73113.50808.90.134000.420200.404000.120500.31870.102301
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7116.07019.65104.10817.70.091680.084240.0976900.066380.17980.05391...24.56128.801223.00.150000.204500.282900.152000.26500.063870
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405 rows × 31 columns

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" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "444 12.040 28.14 76.85 449.9 0.08752 \n", - "478 9.676 13.14 64.12 272.5 0.12550 \n", - "210 10.440 15.46 66.62 329.6 0.10530 \n", - "299 12.430 17.00 78.60 477.3 0.07557 \n", - "513 14.470 24.99 95.81 656.4 0.08837 \n", - ".. ... ... ... ... ... \n", - "71 16.070 19.65 104.10 817.7 0.09168 \n", - "106 10.510 20.19 68.64 334.2 0.11220 \n", - "270 14.410 19.73 96.03 651.0 0.08757 \n", - "435 27.420 26.27 186.90 2501.0 0.10840 \n", - "102 22.270 19.67 152.80 1509.0 0.13260 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "444 0.06000 0.023670 0.02377 0.1854 \n", - "478 0.22040 0.118800 0.07038 0.2057 \n", - "210 0.07722 0.006643 0.01216 0.1788 \n", - "299 0.03454 0.013420 0.01699 0.1472 \n", - "513 0.12300 0.100900 0.03890 0.1872 \n", - ".. ... ... ... ... \n", - "71 0.08424 0.097690 0.06638 0.1798 \n", - "106 0.13030 0.064760 0.03068 0.1922 \n", - "270 0.16760 0.136200 0.06602 0.1714 \n", - "435 0.19880 0.363500 0.16890 0.2061 \n", - "102 0.27680 0.426400 0.18230 0.2556 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "444 0.05698 ... 33.33 87.24 567.6 \n", - "478 0.09575 ... 18.04 69.47 328.1 \n", - "210 0.06450 ... 19.80 73.47 395.4 \n", - "299 0.05561 ... 20.21 81.76 515.9 \n", - "513 0.06341 ... 31.73 113.50 808.9 \n", - ".. ... ... ... ... ... \n", - "71 0.05391 ... 24.56 128.80 1223.0 \n", - "106 0.07782 ... 22.75 72.62 374.4 \n", - "270 0.07192 ... 22.13 101.70 767.3 \n", - "435 0.05623 ... 31.37 251.20 4254.0 \n", - "102 0.07039 ... 28.01 206.80 2360.0 \n", - "\n", - " worst smoothness worst compactness worst concavity \\\n", - "444 0.10410 0.09726 0.05524 \n", - "478 0.20060 0.36630 0.29130 \n", - "210 0.13410 0.11530 0.02639 \n", - "299 0.08409 0.04712 0.02237 \n", - "513 0.13400 0.42020 0.40400 \n", - ".. ... ... ... \n", - "71 0.15000 0.20450 0.28290 \n", - "106 0.13000 0.20490 0.12950 \n", - "270 0.09983 0.24720 0.22200 \n", - "435 0.13570 0.42560 0.68330 \n", - "102 0.17010 0.69970 0.96080 \n", - "\n", - " worst concave points worst symmetry worst fractal dimension target \n", - "444 0.05547 0.2404 0.06639 1 \n", - "478 0.10750 0.2848 0.13640 1 \n", - "210 0.04464 0.2615 0.08269 1 \n", - "299 0.02832 0.1901 0.05932 1 \n", - "513 0.12050 0.3187 0.10230 1 \n", - ".. ... ... ... ... \n", - "71 0.15200 0.2650 0.06387 0 \n", - "106 0.06136 0.2383 0.09026 1 \n", - "270 0.10210 0.2272 0.08799 1 \n", - "435 0.26250 0.2641 0.07427 0 \n", - "102 0.29100 0.4055 0.09789 0 \n", - "\n", - "[405 rows x 31 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Read one of the datasets. This is the one with a 0.25 test split\n", "run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"].load()" @@ -1668,21 +460,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "c8f90647", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Get artifact version from our run\n", "dataset_trn_artifact_version_via_run = run.steps[\"data_preprocessor\"].outputs[\"dataset_trn\"] \n", @@ -1697,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "3f9d3dfd", "metadata": {}, "outputs": [], @@ -1718,398 +499,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "c82aca75", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimensiontarget
44412.04028.1476.85449.90.087520.060000.0236700.023770.18540.05698...33.3387.24567.60.104100.097260.055240.055470.24040.066391
4789.67613.1464.12272.50.125500.220400.1188000.070380.20570.09575...18.0469.47328.10.200600.366300.291300.107500.28480.136401
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51314.47024.9995.81656.40.088370.123000.1009000.038900.18720.06341...31.73113.50808.90.134000.420200.404000.120500.31870.102301
..................................................................
7116.07019.65104.10817.70.091680.084240.0976900.066380.17980.05391...24.56128.801223.00.150000.204500.282900.152000.26500.063870
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405 rows × 31 columns

\n", - "
" - ], - "text/plain": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "444 12.040 28.14 76.85 449.9 0.08752 \n", - "478 9.676 13.14 64.12 272.5 0.12550 \n", - "210 10.440 15.46 66.62 329.6 0.10530 \n", - "299 12.430 17.00 78.60 477.3 0.07557 \n", - "513 14.470 24.99 95.81 656.4 0.08837 \n", - ".. ... ... ... ... ... \n", - "71 16.070 19.65 104.10 817.7 0.09168 \n", - "106 10.510 20.19 68.64 334.2 0.11220 \n", - "270 14.410 19.73 96.03 651.0 0.08757 \n", - "435 27.420 26.27 186.90 2501.0 0.10840 \n", - "102 22.270 19.67 152.80 1509.0 0.13260 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "444 0.06000 0.023670 0.02377 0.1854 \n", - "478 0.22040 0.118800 0.07038 0.2057 \n", - "210 0.07722 0.006643 0.01216 0.1788 \n", - "299 0.03454 0.013420 0.01699 0.1472 \n", - "513 0.12300 0.100900 0.03890 0.1872 \n", - ".. ... ... ... ... \n", - "71 0.08424 0.097690 0.06638 0.1798 \n", - "106 0.13030 0.064760 0.03068 0.1922 \n", - "270 0.16760 0.136200 0.06602 0.1714 \n", - "435 0.19880 0.363500 0.16890 0.2061 \n", - "102 0.27680 0.426400 0.18230 0.2556 \n", - "\n", - " mean fractal dimension ... worst texture worst perimeter worst area \\\n", - "444 0.05698 ... 33.33 87.24 567.6 \n", - "478 0.09575 ... 18.04 69.47 328.1 \n", - "210 0.06450 ... 19.80 73.47 395.4 \n", - "299 0.05561 ... 20.21 81.76 515.9 \n", - "513 0.06341 ... 31.73 113.50 808.9 \n", - ".. ... ... ... ... ... \n", - "71 0.05391 ... 24.56 128.80 1223.0 \n", - "106 0.07782 ... 22.75 72.62 374.4 \n", - "270 0.07192 ... 22.13 101.70 767.3 \n", - "435 0.05623 ... 31.37 251.20 4254.0 \n", - "102 0.07039 ... 28.01 206.80 2360.0 \n", - "\n", - " worst smoothness worst compactness worst concavity \\\n", - "444 0.10410 0.09726 0.05524 \n", - "478 0.20060 0.36630 0.29130 \n", - "210 0.13410 0.11530 0.02639 \n", - "299 0.08409 0.04712 0.02237 \n", - "513 0.13400 0.42020 0.40400 \n", - ".. ... ... ... \n", - "71 0.15000 0.20450 0.28290 \n", - "106 0.13000 0.20490 0.12950 \n", - "270 0.09983 0.24720 0.22200 \n", - "435 0.13570 0.42560 0.68330 \n", - "102 0.17010 0.69970 0.96080 \n", - "\n", - " worst concave points worst symmetry worst fractal dimension target \n", - "444 0.05547 0.2404 0.06639 1 \n", - "478 0.10750 0.2848 0.13640 1 \n", - "210 0.04464 0.2615 0.08269 1 \n", - "299 0.02832 0.1901 0.05932 1 \n", - "513 0.12050 0.3187 0.10230 1 \n", - ".. ... ... ... ... \n", - "71 0.15200 0.2650 0.06387 0 \n", - "106 0.06136 0.2383 0.09026 1 \n", - "270 0.10210 0.2272 0.08799 1 \n", - "435 0.26250 0.2641 0.07427 0 \n", - "102 0.29100 0.4055 0.09789 0 \n", - "\n", - "[405 rows x 31 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Load an artifact to verify you can fetch it\n", "dataset_trn_artifact_version.load()" @@ -2150,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "fccf1bd9", "metadata": {}, "outputs": [], @@ -2211,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "1aa98f2f", "metadata": {}, "outputs": [], @@ -2260,80 +653,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "c64885ac", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model RandomForestClassifier()...\u001b[0m\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.487s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "\u001b[1;35mTrain accuracy=100.00%\u001b[0m\n", - "\u001b[1;35mTest accuracy=90.44%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.150s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_14-19_13_24_502719\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.816s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Use a random forest model with the chosen datasets.\n", "# We need to pass the ID's of the datasets into the function\n", @@ -2348,80 +671,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "4300c82f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 2)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "\u001b[1;35mTraining model SGDClassifier()...\u001b[0m\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.314s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "\u001b[1;35mTrain accuracy=89.88%\u001b[0m\n", - "\u001b[1;35mTest accuracy=86.76%\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.164s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_14-19_13_26_761730\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.654s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Use a SGD classifier\n", "sgd_run = training(\n", @@ -2445,21 +698,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "d95810b1", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# The evaluator returns a float value with the accuracy\n", "rf_run.steps[\"model_evaluator\"].output.load() > sgd_run.steps[\"model_evaluator\"].output.load()" @@ -2487,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "99ca00c0", "metadata": {}, "outputs": [], @@ -2505,33 +747,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "0e78a520", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 2)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model \u001b[0m\u001b[1;36mbreast_cancer_classifier\u001b[1;35m was created implicitly.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36msgd\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_14-19_13_28_867038\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.301s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Let's train the SGD model and set the version name to \"sgd\"\n", "pipeline_settings[\"model_version\"].version = \"sgd\"\n", @@ -2550,32 +769,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "9b8e0002", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36mtraining\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mReusing registered version: \u001b[0m\u001b[1;36m(version: 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mNew model version \u001b[0m\u001b[1;36mrf\u001b[1;35m was created.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_trainer\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mUsing cached version of \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mLinking artifact \u001b[0m\u001b[1;36moutput\u001b[1;35m to model \u001b[0m\u001b[1;36mNone\u001b[1;35m version \u001b[0m\u001b[1;36mNone\u001b[1;35m implicitly.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mmodel_evaluator\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36mtraining-2023_12_14-19_13_30_626808\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.321s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Let's train the RF model and set the version name to \"rf\"\n", "pipeline_settings[\"model_version\"].version = \"rf\"\n", @@ -2603,29 +800,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "fbb25913", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id=UUID('aa651410-ae18-437b-b55b-81ed7d85e093') permission_denied=False body=ModelResponseBody(created=datetime.datetime(2023, 12, 14, 19, 13, 28, 743771), updated=datetime.datetime(2023, 12, 14, 19, 13, 28, 743774), user=UserResponse(id=UUID('b11443aa-7044-4002-a761-fca964afead0'), permission_denied=False, body=UserResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 615395), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 615400), active=True, activation_token=None, full_name='', email_opted_in=None, is_service_account=False), metadata=None, name='default'), tags=[TagResponseModel(id=UUID('f84d2acb-50c0-45aa-9e31-b0b2b5cadeb1'), created=datetime.datetime(2023, 12, 14, 19, 13, 28, 747775), updated=datetime.datetime(2023, 12, 14, 19, 13, 28, 747778), missing_permissions=False, name='breast_cancer', color=, tagged_count=1), TagResponseModel(id=UUID('217927ee-4157-441a-93b3-151ad3724ebb'), created=datetime.datetime(2023, 12, 14, 19, 13, 28, 769699), updated=datetime.datetime(2023, 12, 14, 19, 13, 28, 769701), missing_permissions=False, name='classifier', color=, tagged_count=1)], latest_version='rf', workspace=WorkspaceResponse(id=UUID('73b57054-1d66-4b08-bb61-9d74d2ef6180'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 111293), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 111299)), metadata=None, name='default')) metadata=ModelResponseMetadata(workspace=WorkspaceResponse(id=UUID('73b57054-1d66-4b08-bb61-9d74d2ef6180'), permission_denied=False, body=WorkspaceResponseBody(created=datetime.datetime(2023, 12, 14, 19, 12, 36, 111293), updated=datetime.datetime(2023, 12, 14, 19, 12, 36, 111299)), metadata=None, name='default'), license='Apache 2.0', description='A breast cancer classifier', audience=None, use_cases=None, limitations=None, trade_offs=None, ethics=None) name='breast_cancer_classifier'\n", - "Model breast_cancer_classifier has 2 versions\n" - ] - }, - { - "data": { - "text/plain": [ - "('sgd', 'rf')" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "zenml_model = client.get_model(\"breast_cancer_classifier\")\n", "print(zenml_model)\n", @@ -2648,24 +826,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "31211413", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "RandomForestClassifier()" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Let's load the RF version\n", "rf_zenml_model_version = client.get_model_version(\"breast_cancer_classifier\", \"rf\")\n", @@ -2703,7 +867,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "26b718f8", "metadata": {}, "outputs": [], @@ -2762,7 +926,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "92c4c7dc", "metadata": {}, "outputs": [], @@ -2793,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "37c409bd", "metadata": {}, "outputs": [], @@ -2831,18 +995,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "61bf5939", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35m\u001b[0m\u001b[1;36mversion\u001b[1;35m \u001b[0m\u001b[1;36mproduction\u001b[1;35m matches one of the possible \u001b[0m\u001b[1;36mModelStages\u001b[1;35m and will be fetched using stage.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "pipeline_settings = {\"enable_cache\": False}\n", "\n", @@ -2858,41 +1014,10 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "ff3402f1", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1;35mInitiating a new run for the pipeline: \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRegistered new version: \u001b[0m\u001b[1;36m(version 1)\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mExecuting a new run.\u001b[0m\n", - "\u001b[1;35mCaching is disabled by default for \u001b[0m\u001b[1;36minference\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mUsing user: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mUsing stack: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m artifact_store: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35m orchestrator: \u001b[0m\u001b[1;36mdefault\u001b[1;35m\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mDataset with 28 records loaded!\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36mdata_loader\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.642s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has started.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_preprocessor\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.847s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has started.\u001b[0m\n", - "\u001b[33mYou specified both an ID as well as a version of the artifact_versions. Ignoring the version and fetching the artifact_versions by ID.\u001b[0m\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:767: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if not hasattr(array, \"sparse\") and array.dtypes.apply(is_sparse).any():\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:605: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype):\n", - "/home/apenner/PycharmProjects/template-starter/venv/lib/python3.9/site-packages/sklearn/utils/validation.py:614: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n", - " if is_sparse(pd_dtype) or not is_extension_array_dtype(pd_dtype):\n", - "\u001b[1;35mStep \u001b[0m\u001b[1;36minference_predict\u001b[1;35m has finished in \u001b[0m\u001b[1;36m0.678s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mRun \u001b[0m\u001b[1;36minference-2023_12_14-19_13_32_534526\u001b[1;35m has finished in \u001b[0m\u001b[1;36m2.331s\u001b[1;35m.\u001b[0m\n", - "\u001b[1;35mYou can visualize your pipeline runs in the \u001b[0m\u001b[1;36mZenML Dashboard\u001b[1;35m. In order to try it locally, please run \u001b[0m\u001b[1;36mzenml up\u001b[1;35m.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# the `with_options` method allows us to pass in pipeline settings\n", "# and returns a configured pipeline\n", @@ -2917,49 +1042,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "e191d019", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 0\n", - "2 0\n", - "3 1\n", - "4 1\n", - "5 0\n", - "6 0\n", - "7 0\n", - "8 0\n", - "9 1\n", - "10 1\n", - "11 0\n", - "12 1\n", - "13 0\n", - "14 1\n", - "15 0\n", - "16 1\n", - "17 1\n", - "18 1\n", - "19 0\n", - "20 1\n", - "21 1\n", - "22 0\n", - "23 1\n", - "24 1\n", - "25 1\n", - "26 1\n", - "27 1\n", - "Name: series, dtype: int64" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Fetch production model\n", "production_model_version = client.get_model_version(\"breast_cancer_classifier\", \"production\")\n", From cddbed5a002ee41cbe75572532574513a045a7f0 Mon Sep 17 00:00:00 2001 From: Alexej Penner Date: Fri, 15 Dec 2023 00:03:36 +0100 Subject: [PATCH 31/48] Fixed non-rendering visualizations --- template/{.assets => assets}/cloud_mcp.png | Bin .../{.assets => assets}/cloud_mcp_predictions.png | Bin .../{.assets => assets}/cloud_mcp_screenshot.png | Bin .../feature_engineering_pipeline.png | Bin .../{.assets => assets}/inference_pipeline.png | Bin .../{.assets => assets}/pipeline_overview.png | Bin .../{.assets => assets}/training_pipeline.png | Bin template/quickstart.ipynb | 14 +++++++------- 8 files changed, 7 insertions(+), 7 deletions(-) rename template/{.assets => assets}/cloud_mcp.png (100%) rename template/{.assets => assets}/cloud_mcp_predictions.png (100%) rename template/{.assets => assets}/cloud_mcp_screenshot.png (100%) rename template/{.assets => assets}/feature_engineering_pipeline.png (100%) rename template/{.assets => assets}/inference_pipeline.png (100%) rename template/{.assets => assets}/pipeline_overview.png (100%) rename template/{.assets => assets}/training_pipeline.png (100%) diff --git a/template/.assets/cloud_mcp.png b/template/assets/cloud_mcp.png similarity index 100% rename from template/.assets/cloud_mcp.png rename to template/assets/cloud_mcp.png diff --git a/template/.assets/cloud_mcp_predictions.png b/template/assets/cloud_mcp_predictions.png similarity index 100% rename from template/.assets/cloud_mcp_predictions.png rename to template/assets/cloud_mcp_predictions.png diff --git a/template/.assets/cloud_mcp_screenshot.png b/template/assets/cloud_mcp_screenshot.png similarity index 100% rename from template/.assets/cloud_mcp_screenshot.png rename to template/assets/cloud_mcp_screenshot.png diff --git a/template/.assets/feature_engineering_pipeline.png b/template/assets/feature_engineering_pipeline.png similarity index 100% rename from template/.assets/feature_engineering_pipeline.png rename to template/assets/feature_engineering_pipeline.png diff --git a/template/.assets/inference_pipeline.png b/template/assets/inference_pipeline.png similarity index 100% rename from template/.assets/inference_pipeline.png rename to template/assets/inference_pipeline.png diff --git a/template/.assets/pipeline_overview.png b/template/assets/pipeline_overview.png similarity index 100% rename from template/.assets/pipeline_overview.png rename to template/assets/pipeline_overview.png diff --git a/template/.assets/training_pipeline.png b/template/assets/training_pipeline.png similarity index 100% rename from template/.assets/training_pipeline.png rename to template/assets/training_pipeline.png diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index 174b765..8b5e13b 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -17,7 +17,7 @@ "\n", "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n", "\n", - "\"Pipelines" + "\"Pipelines" ] }, { @@ -179,7 +179,7 @@ "your training. ZenML mostly gets out of your way when you're writing your Python\n", "code, as you'll see from the following cell.\n", "\n", - "\"Feature" + "\"Feature" ] }, { @@ -538,7 +538,7 @@ "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", "same data and then compare their performance.\n", "\n", - "\"Training" + "\"Training" ] }, { @@ -847,7 +847,7 @@ "source": [ "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", "\n", - "\"Model" + "\"Model" ] }, { @@ -892,7 +892,7 @@ "id": "2ecbc8cf", "metadata": {}, "source": [ - "\"Model" + "\"Model" ] }, { @@ -921,7 +921,7 @@ "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", "and generate predictions:\n", "\n", - "\"Inference" + "\"Inference" ] }, { @@ -1061,7 +1061,7 @@ "source": [ "You can also see all predictions ever created as a complete history in the dashboard:\n", "\n", - "\"Model" + "\"Model" ] }, { From 9fe56dd28dedc7663e32dd77954c79ab2b5c5dc8 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 18 Dec 2023 10:54:14 +0100 Subject: [PATCH 32/48] Updated --- README.md | 2 +- template/README.md | 8 ++++---- template/quickstart.ipynb | 8 ++++---- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index a91f854..61bb4e7 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # 📜 ZenML Project Templates This repository contains a starter template from which a simple ZenML project -can be generated: a collection of steps, pipelines, stack configurations and +can be generated easily. It contains a collection of steps, pipelines, stack configurations and other artifacts and useful resources that can get you started with ZenML. 🔥 **Do you have a personal project powered by ZenML that you would like to see here?** diff --git a/template/README.md b/template/README.md index 0133525..96deeba 100644 --- a/template/README.md +++ b/template/README.md @@ -16,9 +16,9 @@ This is a representation of how it will all come together: Along the way we will also show you how to: -- Structure your code into MLOps pipelines -- Automatically version, track, and cache data, models, and other artifacts -- Transition your ML models from development to production +- Structure your code into MLOps pipelines. +- Automatically version, track, and cache data, models, and other artifacts. +- Transition your ML models from development to production. ## 🏃 Run on Colab @@ -74,7 +74,7 @@ python run.py --inference-pipeline ## 🌵 Learning MLOps with ZenML -This project is also a great source of learning about some fundamental MLOps concepts. In sum, there are four exemplary steps happening, that can be mapped onto many other projects: +This project is also a great source of learning about some fundamental MLOps concepts. In sum, there are four exemplary steps happening, that can be mapped onto many other projects:
🥇 Step 1: Load your data and execute feature engineering diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index 8b5e13b..1ba8f37 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -1075,16 +1075,16 @@ "a test set, registered the best one with the ZenML model control plane,\n", "and served some predictions. You also learned how to iterate on your models and\n", "data by using some of the ZenML utility abstractions. You saw how to view your\n", - "artifacts and stacks via the client as well as the ZenML Dashboard.\n", + "artifacts and models via the client as well as the ZenML Dashboard.\n", "\n", "## Further exploration\n", "\n", "This was just the tip of the iceberg of what ZenML can do; check out the [**docs**](https://docs.zenml.io/) to learn more\n", "about the capabilities of ZenML. For example, you might want to:\n", "\n", - "- Run the same pipeline on a cloud stack in production.\n", - "- Track your metrics in an experiment tracker like [MLflow]().\n", - "- Learn how to transition your code from this notebook setting to a production setting.\n", + "- [Deploy ZenML](https://docs.zenml.io/user-guide/production-guide/connect-deployed-zenml) to collaborate with your colleagues.\n", + "- Run the same pipeline on a [cloud MLOps stack in production](https://docs.zenml.io/user-guide/production-guide/cloud-stack).\n", + "- Track your metrics in an experiment tracker like [MLflow](https://docs.zenml.io/stacks-and-components/component-guide/experiment-trackers/mlflow).\n", "\n", "## What next?\n", "\n", From a2ca4f8003d336d47c882ed6af628034d29f363a Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 18 Dec 2023 10:55:36 +0100 Subject: [PATCH 33/48] Updated --- template/{assets => .assets}/cloud_mcp.png | Bin .../{assets => .assets}/cloud_mcp_predictions.png | Bin .../{assets => .assets}/cloud_mcp_screenshot.png | Bin .../feature_engineering_pipeline.png | Bin template/{assets => .assets}/inference_pipeline.png | Bin template/{assets => .assets}/pipeline_overview.png | Bin template/{assets => .assets}/training_pipeline.png | Bin 7 files changed, 0 insertions(+), 0 deletions(-) rename template/{assets => .assets}/cloud_mcp.png (100%) rename template/{assets => .assets}/cloud_mcp_predictions.png (100%) rename template/{assets => .assets}/cloud_mcp_screenshot.png (100%) rename template/{assets => .assets}/feature_engineering_pipeline.png (100%) rename template/{assets => .assets}/inference_pipeline.png (100%) rename template/{assets => .assets}/pipeline_overview.png (100%) rename template/{assets => .assets}/training_pipeline.png (100%) diff --git a/template/assets/cloud_mcp.png b/template/.assets/cloud_mcp.png similarity index 100% rename from template/assets/cloud_mcp.png rename to template/.assets/cloud_mcp.png diff --git a/template/assets/cloud_mcp_predictions.png b/template/.assets/cloud_mcp_predictions.png similarity index 100% rename from template/assets/cloud_mcp_predictions.png rename to template/.assets/cloud_mcp_predictions.png diff --git a/template/assets/cloud_mcp_screenshot.png b/template/.assets/cloud_mcp_screenshot.png similarity index 100% rename from template/assets/cloud_mcp_screenshot.png rename to template/.assets/cloud_mcp_screenshot.png diff --git a/template/assets/feature_engineering_pipeline.png b/template/.assets/feature_engineering_pipeline.png similarity index 100% rename from template/assets/feature_engineering_pipeline.png rename to template/.assets/feature_engineering_pipeline.png diff --git a/template/assets/inference_pipeline.png b/template/.assets/inference_pipeline.png similarity index 100% rename from template/assets/inference_pipeline.png rename to template/.assets/inference_pipeline.png diff --git a/template/assets/pipeline_overview.png b/template/.assets/pipeline_overview.png similarity index 100% rename from template/assets/pipeline_overview.png rename to template/.assets/pipeline_overview.png diff --git a/template/assets/training_pipeline.png b/template/.assets/training_pipeline.png similarity index 100% rename from template/assets/training_pipeline.png rename to template/.assets/training_pipeline.png From 02385103a590236460cd847186e56bd7b0cd2352 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 18 Dec 2023 10:58:53 +0100 Subject: [PATCH 34/48] new CTA --- template/README.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/template/README.md b/template/README.md index 96deeba..83c20c0 100644 --- a/template/README.md +++ b/template/README.md @@ -114,11 +114,10 @@ We will use these versions in the next pipeline.
⌚ Step 2: Training pipeline -Now that our data is prepared, it makes sense to train some models to get a sense of how difficult the task is. The Breast Cancer dataset is sufficiently large and complex that it's unlikely we'll be able to train a model that behaves perfectly since the problem -is inherently complex, but we can get a sense of what a reasonable baseline looks like. +Now that our data is prepared, it makes sense to train some models to get a sense of how difficult the task is. The Breast Cancer dataset is sufficiently large and complex that it's unlikely we'll be able to train a model that behaves perfectly since the problem is inherently complex, but we can get a sense of what a reasonable baseline looks like. We'll start with two simple models, a SGD Classifier and a Random Forest -Classifier, both batteries-included from `sklearn`. We'll train them both on the +Classifier, both batteries-included from `sklearn`. We'll train them on the same data and then compare their performance. Training pipeline @@ -150,7 +149,7 @@ registry. However, these topics can be explored more in the
- 💯 Step 3: Associating a model with your pipeline + 💯 Step 3: Promoting the best model to production For now, we will use the ZenML model control plane to promote our best model to `production`. You can do this by simply setting the `stage` of @@ -197,9 +196,11 @@ and served some predictions. You also learned how to iterate on your models and data by using some of the ZenML utility abstractions. You saw how to view your artifacts and stacks via the client as well as the ZenML Dashboard. -If you want to learn more about ZenML as a tool, then the +If you want to learn more about ZenML as a tool, then the [:page_facing_up: **ZenML Docs**](https://docs.zenml.io/) are the perfect place -to get started. +to get started. In particular, the [Production Guide](https://docs.zenml.io/user-guide/production-guide/) +goes into more detail as to how to transition these same pipelines into production +on the cloud. The best way to get a production ZenML instance up and running with all batteries included is the [ZenML Cloud](https://zenml.io/cloud). Check it out! From 4f44541ffeb7a5500f97f7b43d993342c9f7201a Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 18 Dec 2023 10:59:00 +0100 Subject: [PATCH 35/48] new CTA --- template/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/template/README.md b/template/README.md index 83c20c0..d2f4922 100644 --- a/template/README.md +++ b/template/README.md @@ -197,7 +197,7 @@ data by using some of the ZenML utility abstractions. You saw how to view your artifacts and stacks via the client as well as the ZenML Dashboard. If you want to learn more about ZenML as a tool, then the -[:page_facing_up: **ZenML Docs**](https://docs.zenml.io/) are the perfect place +[:page_facing_up: **ZenML Docs**](https://docs.zenml.io/) are the perfect place to get started. In particular, the [Production Guide](https://docs.zenml.io/user-guide/production-guide/) goes into more detail as to how to transition these same pipelines into production on the cloud. From 066517fc0dc631d304fb4bba433e3033a6e29439 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Mon, 18 Dec 2023 17:26:21 +0100 Subject: [PATCH 36/48] Updated --- template/README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/template/README.md b/template/README.md index d2f4922..dd042cd 100644 --- a/template/README.md +++ b/template/README.md @@ -125,11 +125,13 @@ same data and then compare their performance. Run it by using the ID's from the first step: ```python +# You can also ignore the `--train-dataset-version-name` and `--test-dataset-version-name` to use +# the latest versions python run.py --training-pipeline --train-dataset-version-name 1 --test-dataset-version-name 1 ``` -ZenML offers a *Model Control Plane*, -which is a central register of all your ML models. Each run of the training pipeline will produce a ZenML Model Version. +To track these models, ZenML offers a *Model Control Plane*, which is a central register of all your ML models. +Each run of the training pipeline will produce a ZenML Model Version. ```shell zenml model list @@ -159,7 +161,7 @@ your chosen model version to the `production` tag. zenml model version update breast_cancer_classifier rf --stage production ``` -Even if we did this manually for illustration, if you inspect the [promoter code](steps/model_promoter.py), we can see this would already have been done automatically by the training pipeline. It uses the previous production metrics to compare to the current one, and promotes the new model if it performs better on the test set. +While we've demonstrated a manual promotion process for clarity, a more in-depth look at the [promoter code](steps/model_promoter.py) reveals that the training pipeline is designed to automate this step. It evaluates the latest model against established production metrics and, if the new model outperforms the existing one based on test set results, it will automatically promote the model to production. Here is an overview of the process: Model Control Plane From 280644085a0cd25a7ac1e709c61c7f025f7d062c Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 17:41:40 +0100 Subject: [PATCH 37/48] add image optimizer --- .github/workflows/image-optimizer.yml | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 .github/workflows/image-optimizer.yml diff --git a/.github/workflows/image-optimizer.yml b/.github/workflows/image-optimizer.yml new file mode 100644 index 0000000..dddbd1e --- /dev/null +++ b/.github/workflows/image-optimizer.yml @@ -0,0 +1,26 @@ +name: Compress Images +on: + pull_request: + # Run Image Actions when JPG, JPEG, PNG or WebP files are added or changed. + # See https://help.github.com/en/actions/automating-your-workflow-with-github-actions/workflow-syntax-for-github-actions#onpushpull_requestpaths for reference. + paths: + - '**.jpg' + - '**.jpeg' + - '**.png' + - '**.webp' +jobs: + build: + # Only run on non-draft PRs within the same repository. + if: github.event.pull_request.head.repo.full_name == github.repository && github.event.pull_request.draft == false + name: calibreapp/image-actions + runs-on: ubuntu-latest + steps: + - name: Checkout Repo + uses: actions/checkout@v3 + + - name: Compress Images + uses: calibreapp/image-actions@main + with: + # The `GITHUB_TOKEN` is automatically generated by GitHub and scoped only to the repository that is currently running the action. 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ztVWu@Lv+d$zm7BcOTE#w*Z_D*CugbYlQVz0!|pAf&$#C2JS@60$cqi%6Y@xfzp6vC zYfpra%={$ejHU6G!o`#RvZEukS4VkY+Z%3px9U1M*Q2!u?@mAUebeLvm%Ghx2m)^d zig2NJ6RJuD?Qi8f2!c1Xd@VmOPTkZEOT=)rdGZ11@B;`ylsAJ^7|nt+rGjLHuK)LK z!@twm0iXQ8x0wFPgVgcKBQ>))_lQ4s?|XV>?0|Q4#lZIOJUeTI?mF25bIi>s3xjn( z8?St(sV*`_*Q@nfeH;5yvu7yRm>!jU4{U7oWE$5QzmjmjDv4BqaSUcHtajkGU~ZVD*1MJLUn9Z=_hX(Y>LUXr%Skva1O@!NuH*@dd%+|kj-NK`F{{@6CJPfG761r~d)%bR4UjYr`)$D7v7x^Um1 z>FlqHJ$gCBE_c$bH8WYqCry{HKecV)YDB*SlH@y)&%I(r;{X)lV`11Yr3A^}L zAX?Tf7*8GW7>p^QDjCkS>+IBPaVf^;uF~$>B3a3YK<F82_*>9z{&Yxj? zbnGQ>CY5Lonc?3ENHR@CFL`m({kUG6mZzLf-Kg*R6HVGtAgBP^zX9mqND9~>^j}u1 z6g0760^k#hT!i_~wORQ+cn(67LSjxf2F@lXLJHt-QSbw`9&_M~2?-hKn(7pux4-`% D&aj~e From c91cff22962f60218e95e87dc62a263cd3694802 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 17:43:02 +0100 Subject: [PATCH 39/48] imporve ci --- .github/workflows/ci.yml | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 1090e8f..36e9a38 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -2,7 +2,25 @@ name: CI on: workflow_dispatch: + inputs: + ref-template: + description: 'Branch or tag ref to check out for template' + type: string + required: false + ref-zenml: + description: 'Branch or tag ref to check out for ZenML' + type: string + required: false workflow_call: + inputs: + ref-template: + description: 'Branch or tag ref to check out for template' + type: string + required: false + ref-zenml: + description: 'Branch or tag ref to check out for ZenML' + type: string + required: false push: branches: ["main", "develop"] paths-ignore: ["README.md"] @@ -36,3 +54,5 @@ jobs: with: stack-name: ${{ matrix.stack-name }} python-version: ${{ matrix.python-version }} + ref-zenml: ${{ inputs.ref-zenml || 'feature/update-quickstart-from-template' }} + ref-template: ${{ inputs.ref-template || github.ref }} From 05b7a2e5b702c84aec9691d6f8a949ee95c0832c Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 17:50:23 +0100 Subject: [PATCH 40/48] fix ci --- requirements.txt | 2 +- template/requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 21e8aa5..65e8c35 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,4 @@ scikit-learn<1.3 copier jinja2-time zenml[server]>=0.52.0 -notebook \ No newline at end of file +notebook diff --git a/template/requirements.txt b/template/requirements.txt index 7a25951..060cab4 100644 --- a/template/requirements.txt +++ b/template/requirements.txt @@ -1,4 +1,4 @@ zenml[server]>=0.50.0 notebook scikit-learn<1.3 -pyarrow \ No newline at end of file +pyarrow From 621e38c80a53b8594ff23fe972fd8cb9a5e4ba63 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:07:59 +0100 Subject: [PATCH 41/48] fix ci/tests --- .github/workflows/ci.yml | 1 + copier.yaml | 4 ---- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 36e9a38..9fc5047 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -37,6 +37,7 @@ jobs: run-tests: runs-on: ${{ matrix.os }} strategy: + fail-fast: false matrix: stack-name: [local] os: [windows-latest, ubuntu-latest, macos-latest] diff --git a/copier.yaml b/copier.yaml index ae3c266..fdaee8b 100644 --- a/copier.yaml +++ b/copier.yaml @@ -32,10 +32,6 @@ email: The email of the person/entity holding the copyright default: info@zenml.io when: "{{ open_source_license }}" -auto_format: - type: bool - help: "Auto-format the generated code with black, ruff and autoflake? \n (NOTE: you need to have these tools installed in your environment)" - default: false # CONFIGURATION ------------------------- _templates_suffix: "" From b00197c6d36eae78837bad9c88a332f936108731 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:29:33 +0100 Subject: [PATCH 42/48] vscode --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index bfdb54c..f8d2b18 100644 --- a/.gitignore +++ b/.gitignore @@ -131,4 +131,5 @@ dmypy.json # Pyre type checker .pyre/ -*.zen \ No newline at end of file +*.zen +.vscode From 6f7b6cc4c5e53d21232a3605a577540261323995 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:29:42 +0100 Subject: [PATCH 43/48] remove mlflow reqs --- .github/actions/starter_template_test/action.yml | 2 +- template/quickstart.ipynb | 2 +- tests/conftest.py | 4 ---- 3 files changed, 2 insertions(+), 6 deletions(-) diff --git a/.github/actions/starter_template_test/action.yml b/.github/actions/starter_template_test/action.yml index aa01bc1..9940481 100644 --- a/.github/actions/starter_template_test/action.yml +++ b/.github/actions/starter_template_test/action.yml @@ -69,7 +69,7 @@ runs: - name: Concatenate requirements shell: bash run: | - zenml integration export-requirements -o ./local_checkout/integration-requirements.txt sklearn mlflow s3 kubernetes kubeflow slack evidently + zenml integration export-requirements -o ./local_checkout/integration-requirements.txt sklearn cat ./local_checkout/requirements.txt ./local_checkout/test-requirements.txt ./local_checkout/integration-requirements.txt >> ./local_checkout/all-requirements.txt - name: Install requirements diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index 1ba8f37..c6d0b9a 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -76,7 +76,7 @@ "metadata": {}, "outputs": [], "source": [ - "!zenml integration install sklearn mlflow -y\n", + "!zenml integration install sklearn -y\n", "\n", "import IPython\n", "IPython.Application.instance().kernel.do_shutdown(restart=True)" diff --git a/tests/conftest.py b/tests/conftest.py index 6234e9e..fa9049a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -31,10 +31,6 @@ def configure_stack(): if stack_name == "local": components = {} for component in [ - ("mlflow_local", "mlflow", StackComponentType.EXPERIMENT_TRACKER), - ("mlflow_local", "mlflow", StackComponentType.MODEL_REGISTRY), - ("mlflow_local", "mlflow", StackComponentType.MODEL_DEPLOYER), - ("evidently", "evidently", StackComponentType.DATA_VALIDATOR), ("local", "local", StackComponentType.ORCHESTRATOR), ("local", "local", StackComponentType.ARTIFACT_STORE), ]: From 012096e51270733cbf4f3071129d434af67e95ee Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 18:29:48 +0100 Subject: [PATCH 44/48] fix tests --- tests/test_starter_template.py | 78 ++++++++++++++-------------------- 1 file changed, 33 insertions(+), 45 deletions(-) diff --git a/tests/test_starter_template.py b/tests/test_starter_template.py index 67c7420..0d3971f 100644 --- a/tests/test_starter_template.py +++ b/tests/test_starter_template.py @@ -49,9 +49,6 @@ def generate_and_run_project( # generate the template in a temp path current_dir = os.getcwd() dst_path = tmp_path_factory.mktemp("pytest-template") - print("TEMPLATE_DIR:", TEMPLATE_DIRECTORY) - print("dst_path:", dst_path) - print("current_dir:", current_dir) os.chdir(str(dst_path)) with Worker( src_path=TEMPLATE_DIRECTORY, @@ -62,37 +59,39 @@ def generate_and_run_project( ) as worker: worker.run_copy() - # MLFlow Deployer not supported on Windows - # MLFlow `service daemon is not running` error on MacOS - if platform.system().lower() not in ["windows"]: - # run the project - call = [sys.executable, "run.py"] - - try: - subprocess.check_output( - call, - cwd=str(dst_path), - env=os.environ.copy(), - stderr=subprocess.STDOUT, - ) - except subprocess.CalledProcessError as e: - raise RuntimeError( - f"Failed to run project generated with parameters: {answers}\n" - f"{e.output.decode()}" - ) from e - - # check the pipeline run is successful - for pipeline_suffix in ["_training", "_batch_inference"]: - pipeline = Client().get_pipeline(product_name + pipeline_suffix) - assert pipeline - runs = pipeline.runs - assert len(runs) == 1 - assert runs[0].status == ExecutionStatus.COMPLETED - - # clean up - Client().delete_pipeline(product_name + pipeline_suffix) - Client().delete_model(product_name) - Client().active_stack.model_registry.delete_model(product_name) + # run the project + call = [ + sys.executable, + "run.py", + "--training-pipeline", + "--feature-pipeline", + "--inference-pipeline", + ] + + try: + subprocess.check_output( + call, + cwd=str(dst_path), + env=os.environ.copy(), + stderr=subprocess.STDOUT, + ) + except subprocess.CalledProcessError as e: + raise RuntimeError( + f"Failed to run project generated with parameters: {answers}\n" + f"{e.output.decode()}" + ) from e + + # check the pipeline run is successful + for pipeline_name in ["training", "inference", "feature_engineering"]: + pipeline = Client().get_pipeline(pipeline_name) + assert pipeline + runs = pipeline.runs + assert len(runs) == 1 + assert runs[0].status == ExecutionStatus.COMPLETED + + # clean up + Client().delete_pipeline(pipeline_name) + Client().delete_model("breast_cancer_classifier") os.chdir(current_dir) shutil.rmtree(dst_path) @@ -122,14 +121,3 @@ def test_custom_product_name( tmp_path_factory=tmp_path_factory, product_name="custom_product_name", ) - -def test_custom_zenml_server_url( - clean_zenml_client, - tmp_path_factory: pytest.TempPathFactory, -): - """Test deploying to production stage.""" - - generate_and_run_project( - tmp_path_factory=tmp_path_factory, - zenml_server_url="foo", - ) From be772f9797f55887c5d9e09a388b33b0b9878dc2 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 19:10:13 +0100 Subject: [PATCH 45/48] add `--no-cache` --- template/run.py | 14 ++++++++++++++ tests/test_starter_template.py | 1 + 2 files changed, 15 insertions(+) diff --git a/template/run.py b/template/run.py index 2b30807..8436ea5 100644 --- a/template/run.py +++ b/template/run.py @@ -86,6 +86,12 @@ default=False, help="Whether to run the pipeline that performs inference.", ) +@click.option( + "--no-cache", + is_flag=True, + default=False, + help="Disable caching for the pipeline run.", +) def main( train_dataset_name: str = "dataset_trn", train_dataset_version_name: Optional[str] = None, @@ -94,6 +100,7 @@ def main( feature_pipeline: bool = False, training_pipeline: bool = False, inference_pipeline: bool = False, + no_cache: bool = False, ): """Main entry point for the pipeline execution. @@ -114,6 +121,7 @@ def main( feature_pipeline: Whether to run the pipeline that creates the dataset. training_pipeline: Whether to run the pipeline that trains the model. inference_pipeline: Whether to run the pipeline that performs inference. + no_cache: If `True` cache will be disabled. """ client = Client() @@ -125,6 +133,8 @@ def main( # Execute Feature Engineering Pipeline if feature_pipeline: pipeline_args = {} + if no_cache: + pipeline_args["enable_cache"] = False pipeline_args["config_path"] = os.path.join( config_folder, "feature_engineering.yaml" ) @@ -165,12 +175,16 @@ def main( # Run the SGD pipeline pipeline_args = {} + if no_cache: + pipeline_args["enable_cache"] = False pipeline_args["config_path"] = os.path.join(config_folder, "training_sgd.yaml") training.with_options(**pipeline_args)(**run_args_train) logger.info("Training pipeline with SGD finished successfully!\n\n") # Run the RF pipeline pipeline_args = {} + if no_cache: + pipeline_args["enable_cache"] = False pipeline_args["config_path"] = os.path.join(config_folder, "training_rf.yaml") training.with_options(**pipeline_args)(**run_args_train) logger.info("Training pipeline with RF finished successfully!\n\n") diff --git a/tests/test_starter_template.py b/tests/test_starter_template.py index 0d3971f..a164db3 100644 --- a/tests/test_starter_template.py +++ b/tests/test_starter_template.py @@ -66,6 +66,7 @@ def generate_and_run_project( "--training-pipeline", "--feature-pipeline", "--inference-pipeline", + "--no-cache" ] try: From b25765837a5cf8b26938de4cf7aa7c8c7553ed4f Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Mon, 18 Dec 2023 21:33:42 +0100 Subject: [PATCH 46/48] use SafeLoader --- template/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/template/run.py b/template/run.py index 8436ea5..c3089fd 100644 --- a/template/run.py +++ b/template/run.py @@ -199,7 +199,7 @@ def main( # Fetch the production model with open(pipeline_args["config_path"], "r") as f: - config = yaml.load(f, Loader=yaml.FullLoader) + config = yaml.load(f, Loader=yaml.SafeLoader) zenml_model = client.get_model_version( config["model_version"]["name"], config["model_version"]["version"] ) From 831997c46ca781b478b2d2ebd8177d8fbb369505 Mon Sep 17 00:00:00 2001 From: Andrei Vishniakov <31008759+avishniakov@users.noreply.github.com> Date: Tue, 19 Dec 2023 15:05:17 +0100 Subject: [PATCH 47/48] fix img paths --- template/quickstart.ipynb | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/template/quickstart.ipynb b/template/quickstart.ipynb index c6d0b9a..36e502d 100644 --- a/template/quickstart.ipynb +++ b/template/quickstart.ipynb @@ -17,7 +17,7 @@ "\n", "Follow along this notebook to understand how you can use ZenML to productionalize your ML workflows!\n", "\n", - "\"Pipelines" + "\"Pipelines" ] }, { @@ -179,7 +179,7 @@ "your training. ZenML mostly gets out of your way when you're writing your Python\n", "code, as you'll see from the following cell.\n", "\n", - "\"Feature" + "\"Feature" ] }, { @@ -538,7 +538,7 @@ "Classifier, both batteries-included from `sklearn`. We'll train them both on the\n", "same data and then compare their performance.\n", "\n", - "\"Training" + "\"Training" ] }, { @@ -847,7 +847,7 @@ "source": [ "If you are a [ZenML Cloud](https://zenml.io/cloud) user, you can see all of this visualized in the dashboard:\n", "\n", - "\"Model" + "\"Model" ] }, { @@ -892,7 +892,7 @@ "id": "2ecbc8cf", "metadata": {}, "source": [ - "\"Model" + "\"Model" ] }, { @@ -921,7 +921,7 @@ "with `live data`. The critical step here is the `inference_predict` step, where we load the model in memory\n", "and generate predictions:\n", "\n", - "\"Inference" + "\"Inference" ] }, { @@ -1061,7 +1061,7 @@ "source": [ "You can also see all predictions ever created as a complete history in the dashboard:\n", "\n", - "\"Model" + "\"Model" ] }, { From 652a61421251e4cf3619b5151f2aecdf9bd1c8ca Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Tue, 19 Dec 2023 16:38:36 +0100 Subject: [PATCH 48/48] YAML requirements fixed --- template/configs/feature_engineering.yaml | 1 + template/configs/inference.yaml | 1 + template/configs/training_rf.yaml | 1 + template/configs/training_sgd.yaml | 1 + 4 files changed, 4 insertions(+) diff --git a/template/configs/feature_engineering.yaml b/template/configs/feature_engineering.yaml index bf76f12..d5ab212 100644 --- a/template/configs/feature_engineering.yaml +++ b/template/configs/feature_engineering.yaml @@ -3,6 +3,7 @@ settings: docker: required_integrations: - sklearn + requirements: - pyarrow # pipeline configuration diff --git a/template/configs/inference.yaml b/template/configs/inference.yaml index bed69a3..0fd82f5 100644 --- a/template/configs/inference.yaml +++ b/template/configs/inference.yaml @@ -3,6 +3,7 @@ settings: docker: required_integrations: - sklearn + requirements: - pyarrow # configuration of the Model Control Plane diff --git a/template/configs/training_rf.yaml b/template/configs/training_rf.yaml index ddb68d6..c1418af 100644 --- a/template/configs/training_rf.yaml +++ b/template/configs/training_rf.yaml @@ -3,6 +3,7 @@ settings: docker: required_integrations: - sklearn + requirements: - pyarrow # configuration of the Model Control Plane diff --git a/template/configs/training_sgd.yaml b/template/configs/training_sgd.yaml index 4318c38..6ca7c0d 100644 --- a/template/configs/training_sgd.yaml +++ b/template/configs/training_sgd.yaml @@ -3,6 +3,7 @@ settings: docker: required_integrations: - sklearn + requirements: - pyarrow # configuration of the Model Control Plane

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