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Overview

PyMilo is an open source Python package that provides a simple, efficient, and safe way for users to export pre-trained machine learning models in a transparent way. By this, the exported model can be used in other environments, transferred across different platforms, and shared with others. PyMilo allows the users to export the models that are trained using popular Python libraries like scikit-learn, and then use them in deployment environments, or share them without exposing the underlying code or dependencies. The transparency of the exported models ensures reliability and safety for the end users, as it eliminates the risks of binary or pickle formats.

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Installation

PyPI

• Check Python Packaging User Guide
• Run pip install pymilo==1.1

Source code

• Download Version 1.1 or Latest Source
• Run pip install .

Conda

• Check Conda Managing Package
• Update Conda using conda update conda
• Run conda install -c openscilab pymilo

Usage

Import/Export

Imagine you want to train a LinearRegression model representing this equation: $y = x_0 + 2x_1 + 3$. You will create data points (X, y) and train your model as follows.

import numpy as np
from sklearn.linear_model import LinearRegression
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3
# y = 1 * x_0 + 2 * x_1 + 3
model = LinearRegression().fit(X, y)
pred = model.predict(np.array([[3, 5]]))
# pred = [16.] (=1 * 3 + 2 * 5 + 3)

Using PyMilo Export class you can easily serialize and export your trained model into a JSON file.

from pymilo import Export
Export(model).save("model.json")

You can check out your model as a JSON file now.

{
    "data": {
        "fit_intercept": true,
        "copy_X": true,
        "n_jobs": null,
        "positive": false,
        "n_features_in_": 2,
        "coef_": {
            "pymiloed-ndarray-list": [
                1.0000000000000002,
                1.9999999999999991
            ],
            "pymiloed-ndarray-dtype": "float64",
            "pymiloed-ndarray-shape": [
                2
            ],
            "pymiloed-data-structure": "numpy.ndarray"
        },
        "rank_": 2,
        "singular_": {
            "pymiloed-ndarray-list": [
                1.618033988749895,
                0.6180339887498948
            ],
            "pymiloed-ndarray-dtype": "float64",
            "pymiloed-ndarray-shape": [
                2
            ],
            "pymiloed-data-structure": "numpy.ndarray"
        },
        "intercept_": {
            "value": 3.0000000000000018,
            "np-type": "numpy.float64"
        }
    },
    "sklearn_version": "1.4.2",
    "pymilo_version": "0.8",
    "model_type": "LinearRegression"
}

You can see all the learned parameters of the model in this file and change them if you want. This JSON representation is a transparent version of your model.

Now let's load it back. You can do it easily by using PyMilo Import class.

from pymilo import Import
model = Import("model.json").to_model()
pred = model.predict(np.array([[3, 5]]))
# pred = [16.] (=1 * 3 + 2 * 5 + 3)

This loaded model is exactly the same as the original trained model.

ML streaming

You can easily serve your ML model from a remote server using ML streaming feature of PyMilo.

⚠️ ML streaming feature exists in versions >=1.0

⚠️ In order to use ML streaming feature, make sure you've installed the streaming mode of PyMilo

You can choose either REST or WebSocket as the communication medium protocol.

Server

Let's assume you are in the remote server and you want to import the exported JSON file and start serving your model through REST protocol!

from pymilo import Import
from pymilo.streaming import PymiloServer, CommunicationProtocol
my_model = Import("model.json").to_model()
communicator = PymiloServer(
    model=my_model,
    port=8000,
    communication_protocol=CommunicationProtocol["REST"],
    ).communicator
communicator.run()

Now PymiloServer runs on port 8000 and exposes REST API to upload, download and retrieve attributes either data attributes like model._coef or method attributes like model.predict(x_test).

Client

By using PymiloClient you can easily connect to the remote PymiloServer and execute any functionalities that the given ML model has, let's say you want to run predict function on your remote ML model and get the result:

from pymilo.streaming import PymiloClient, CommunicationProtocol
pymilo_client = PymiloClient(
    mode=PymiloClient.Mode.LOCAL,
    server_url="SERVER_URL",
    communication_protocol=CommunicationProtocol["REST"],
    )
pymilo_client.toggle_mode(PymiloClient.Mode.DELEGATE)
result = pymilo_client.predict(x_test)

ℹ️ If you've deployed PymiloServer locally (on port 8000 for instance), then SERVER_URL would be http://127.0.0.1:8000 or ws://127.0.0.1:8000 based on the selected protocol for the communication medium.

You can also download the remote ML model into your local and execute functions locally on your model.

Calling download function on PymiloClient will sync the local model that PymiloClient wraps upon with the remote ML model, and it doesn't save model directly to a file.

pymilo_client.download()

If you want to save the ML model to a file in your local, you can use Export class.

from pymilo import Export
Export(pymilo_client.model).save("model.json")

Now that you've synced the remote model with your local model, you can run functions.

pymilo_client.toggle_mode(mode=PymiloClient.Mode.LOCAL)
result = pymilo_client.predict(x_test)

PymiloClient wraps around the ML model, either to the local ML model or the remote ML model, and you can work with PymiloClient in the exact same way that you did with the ML model, you can run exact same functions with same signature.

ℹ️ Through the usage of toggle_mode function you can specify whether PymiloClient applies requests on the local ML model pymilo_client.toggle_mode(mode=Mode.LOCAL) or delegates it to the remote server pymilo_client.toggle_mode(mode=Mode.DELEGATE)

Supported ML models

scikit-learn	PyTorch
Linear Models ✅	-
Neural Networks ✅	-
Trees ✅	-
Clustering ✅	-
Naïve Bayes ✅	-
Support Vector Machines (SVMs) ✅	-
Nearest Neighbors ✅	-
Ensemble Models ✅	-
Pipeline Model ✅	-
Preprocessing Models ✅	-
Cross Decomposition Models ✅	-

Details are available in Supported Models.

Issues & bug reports

Just fill an issue and describe it. We'll check it ASAP! or send an email to [email protected].

• Please complete the issue template

You can also join our discord server

Acknowledgments

Python Software Foundation (PSF) grants PyMilo library partially for versions 1.0, 1.1. PSF is the organization behind Python. Their mission is to promote, protect, and advance the Python programming language and to support and facilitate the growth of a diverse and international community of Python programmers.

Trelis Research grants PyMilo library partially for version 1.0. Trelis Research provides tools and tutorials for businesses and developers looking to fine-tune and deploy large language models.

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