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Add .ipynb table of contents to "empty" folders #3452

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Jun 14, 2022
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Add .ipynb table of contents to "empty" folders #3452

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92ed8c6
add label_data table of contents nb
atqy Jun 9, 2022
a32f83f
create tabler of contents notebooks
atqy Jun 10, 2022
881405f
normal format titles in algorithms.ipynb
atqy Jun 14, 2022
5dbaf6d
Merge branch 'main' into atqy/add-table-of-contents-nb
atqy Jun 14, 2022
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52 changes: 52 additions & 0 deletions inference/bring_your_own_container.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# BYOC\n",
"\n",
"Examples on how to use your own model serving containers or extend pre-built containers on SageMaker."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TensorFlow\n",
"---\n",
"\n",
"### Elastic inference\n",
"\n",
"* [Using Amazon Elastic Inference with a pre-trained TensorFlow Serving model on SageMaker](../sagemaker-python-sdk/tensorflow_serving_using_elastic_inference_with_your_own_model/tensorflow_serving_pretrained_model_elastic_inference.ipynb)\n",
"\n",
"### TensorFlow Serving container\n",
"\n",
"* [Using the SageMaker TensorFlow Serving Container](../sagemaker-python-sdk/tensorflow_serving_container/tensorflow_serving_container.ipynb)\n",
"\n"
]
}
],
"metadata": {
"instance_type": "ml.t3.medium",
"kernelspec": {
"display_name": "Python 3 (Data Science)",
"language": "python",
"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
79 changes: 79 additions & 0 deletions inference/data_types.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image\n",
"---\n",
"\n",
"* [MNIST Training using PyTorch](../sagemaker-python-sdk/pytorch_mnist/pytorch_mnist.ipynb)\n",
"* [Using the SageMaker TensorFlow Serving Container](../sagemaker-python-sdk/tensorflow_serving_container/tensorflow_serving_container.ipynb)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tabular\n",
"---\n",
"\n",
"* [Iris Training and Prediction with Sagemaker Scikit-learn](../sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform.ipynb)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Text\n",
"---\n",
"\n",
"* [Hosting and Deployment of Pre-Trained Text Models using SageMaker Endpoint and BlazingText](../introduction_to_amazon_algorithms/blazingtext_hosting_pretrained_fasttext/blazingtext_hosting_pretrained_fasttext.ipynb)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time series\n",
"---\n",
"\n",
"* [Time series forecasting with DeepAR - Synthetic data](../introduction_to_amazon_algorithms/deepar_synthetic/deepar_synthetic.ipynb)\n",
"\n"
]
}
],
"metadata": {
"instance_type": "ml.t3.medium",
"kernelspec": {
"display_name": "Python 3 (Data Science)",
"language": "python",
"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
59 changes: 59 additions & 0 deletions inference/endpoints.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get started with endpoints\n",
"\n",
"Examples on how to use your own model serving containers or extend pre-built containers on SageMaker.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A/B testing\n",
"---\n",
"\n",
"* [A/B Testing with Amazon SageMaker](../sagemaker_endpoints/a_b_testing/a_b_testing.ipynb)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi-model endpoints\n",
"---\n",
"\n",
"* [Amazon SageMaker Multi-Model Endpoints using Scikit Learn](../advanced_functionality/multi_model_sklearn_home_value/sklearn_multi_model_endpoint_home_value.ipynb)\n",
"* [Amazon SageMaker Multi-Model Endpoints using XGBoost](../advanced_functionality/multi_model_xgboost_home_value/xgboost_multi_model_endpoint_home_value.ipynb)\n",
"\n"
]
}
],
"metadata": {
"instance_type": "ml.t3.medium",
"kernelspec": {
"display_name": "Python 3 (Data Science)",
"language": "python",
"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
104 changes: 104 additions & 0 deletions inference/index.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy Models with SageMaker\n",
"\n",
"Examples on how to host models for predictions, inference, and transformations with SageMaker."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bring your own container\n",
"---\n",
"\n",
"* [Bring Your Own Container (BYOC)](bring_your_own_container.ipynb)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data types\n",
"---\n",
"\n",
"* [Data Types](data_types.ipynb)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model deployment\n",
"---\n",
"\n",
"* [Use Your Own Inference Code with Amazon SageMaker XGBoost Algorithm](../introduction_to_amazon_algorithms/xgboost_abalone/xgboost_inferenece_script_mode.ipynb)\n",
"* [TensorFlow BYOM: Train locally and deploy on SageMaker.](../advanced_functionality/tensorflow_iris_byom/tensorflow_BYOM_iris.ipynb)\n",
"* [Bring Your Own Model (k-means)](../advanced_functionality/kmeans_bring_your_own_model/kmeans_bring_your_own_model.ipynb)\n",
"* [Amazon SageMaker XGBoost Bring Your Own Model](../advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb)\n",
"\n",
"### Elastic inference\n",
"\n",
"* [Using Amazon Elastic Inference with MXNet on Amazon SageMaker](../sagemaker-python-sdk/mxnet_mnist/mxnet_mnist_elastic_inference.ipynb)\n",
"* [Using Amazon Elastic Inference with MXNet on an Amazon SageMaker Notebook Instance](../sagemaker-python-sdk/mxnet_mnist/mxnet_mnist_elastic_inference_local.ipynb)\n",
"* [Hosting ONNX models with Amazon Elastic Inference](../sagemaker-python-sdk/mxnet_onnx_eia/mxnet_onnx_eia.ipynb)\n",
"\n",
"\n",
"### Endpoints\n",
"\n",
"* [Endpoints](endpoints.ipynb)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi-Model Deployment\n",
"---\n",
"\n",
"* [Amazon SageMaker Multi-Model Endpoints using Scikit Learn](../advanced_functionality/multi_model_sklearn_home_value/sklearn_multi_model_endpoint_home_value.ipynb)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nvidia Triton Inference\n",
"---\n",
"\n",
"* [Triton on SageMaker - Deploying a PyTorch Resnet50 model](../sagemaker-triton/resnet50/triton_resnet50.ipynb)\n",
"* [Triton on SageMaker - NLP Bert](../sagemaker-triton/nlp_bert/triton_nlp_bert.ipynb)\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (Data Science)",
"language": "python",
"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
94 changes: 94 additions & 0 deletions label_data/index.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ground Truth\n",
"\n",
"Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get started with Ground Truth\n",
"---\n",
"\n",
"[This video](https://www.youtube.com/embed/_FPI6KjDlCI) shows you how to setup and use Amazon SageMaker Ground Truth. (Length: 9:37)\n",
"\n",
"### End-to-end demo: from unlabeled data to a deployed ML model\n",
"\n",
"[From Unlabeled Data to a Deployed Machine Learning Model: A SageMaker Ground Truth Demonstration for Image Classification](../ground_truth_labeling_jobs/from_unlabeled_data_to_deployed_machine_learning_model_ground_truth_demo_image_classification/from_unlabeled_data_to_deployed_machine_learning_model_ground_truth_demo_image_classification.ipynb)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task types\n",
"---\n",
"\n",
"### 3D point clouds\n",
"* [Create a 3D Point Cloud Labeling Job with Amazon SageMaker Ground Truth](../ground_truth_labeling_jobs/3d_point_cloud_demo/create-3D-pointcloud-labeling-job.ipynb)\n",
"* [Create a 3D Point Cloud Labeling Job for Object Tracking with Amazon SageMaker Ground Truth](../ground_truth_labeling_jobs/3d_point_cloud_input_data_processing/3D-point-cloud-input-data-processing.ipynb) \n",
"\n",
"\n",
"### Annotation consolidation\n",
"* [Understanding Annotation Consolidation: A SageMaker Ground Truth Demonstration for Image Classification](../ground_truth_labeling_jobs/annotation_consolidation/ACSBlogPost.ipynb)\n",
"\n",
"\n",
"### Object detection\n",
"* [From Unlabeled Data to a Deployed Machine Learning Model: A SageMaker Ground Truth Demonstration for Object Detection](../ground_truth_labeling_jobs/ground_truth_object_detection_tutorial/object_detection_tutorial.ipynb)\n",
"\n",
"\n",
"### Pretrained model labeling\n",
"* [Using a Pre-Trained Model for Cost Effective Data Labeling](../ground_truth_labeling_jobs/pretrained_model/pretrained_model_labeling_tutorial.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bring your own model\n",
"---\n",
"\n",
"* [Create an Active Learning Workflow using Amazon SageMaker Ground Truth](../ground_truth_labeling_jobs/bring_your_own_model_for_sagemaker_labeling_workflows_with_active_learning/bring_your_own_model_for_sagemaker_labeling_workflows_with_active_learning.ipynb)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis tasks\n",
"---\n",
"\n",
"### Data analysis of image classification\n",
"* [Data Analysis Using a Ground Truth Image Classification Output Manifest](../ground_truth_labeling_jobs/data_analysis_of_ground_truth_image_classification_output/data_analysis_of_ground_truth_image_classification_output.ipynb)\n"
]
}
],
"metadata": {
"instance_type": "ml.t3.medium",
"kernelspec": {
"display_name": "Python 3 (Data Science)",
"language": "python",
"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/datascience-1.0"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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