README.md

page_type	languages	products	description
sample	python azurecli	azure-machine-learning	Top-level directory for official Azure Machine Learning Python SDK sample code and notebooks.

Azure Machine Learning Python SDK examples

Welcome to the Azure Machine Learning examples repository!

Prerequisites

1. An Azure subscription. If you don't have an Azure subscription, create a free account before you begin.
2. A terminal and Python >=3.6,<3.9.

Set up

Clone this repository and install required packages:

git clone https://github.com/Azure/azureml-examples --depth 1
cd azureml-examples/python-sdk
pip install --upgrade -r requirements.txt

To create or setup a workspace with the assets used in these examples, run the setup script.

If you do not have an Azure ML workspace, run python setup-workspace.py --subscription-id $ID, where $ID is your Azure subscription id. A resource group, Azure ML workspace, and other necessary resources will be created in the subscription.

If you have an Azure ML Workspace, install the Azure ML CLI and run az ml folder attach -w $WS -g $RG, where $WS and $RG are the workspace and resource group names.

Run python setup-workspace.py -h to see other arguments.

Getting started

To get started, see the introductory tutorial which uses Azure ML to:

• run a "hello world" job on cloud compute, demonstrating the basics
• run a series of PyTorch training jobs on cloud compute, demonstrating mlflow tracking & using cloud data

These concepts are sufficient to understand all examples in this repository, which are listed below.

Examples

Tutorials (tutorials)

path	status	notebooks	description
automl-with-azureml		automl-nlp-text-classification-multiclass.ipynb automl-nlp-text-classification-multilabel.ipynb automl-nlp-ner.ipynb auto-ml-classification-bank-marketing-all-features.ipynb auto-ml-classification-credit-card-fraud.ipynb auto-ml-classification-text-dnn.ipynb auto-ml-continuous-retraining.ipynb auto-ml-forecasting-backtest-many-models.ipynb auto-ml-forecasting-backtest-single-model.ipynb auto-ml-forecasting-bike-share.ipynb auto-ml-forecasting-data-preparation.ipynb auto-ml-forecasting-demand-forecasting-many-models.ipynb auto-ml-forecasting-demand-tcn.ipynb auto-ml-forecasting-energy-demand.ipynb auto-ml-forecasting-function.ipynb auto-ml-forecasting-github-dau.ipynb auto-ml-forecasting-hierarchical-timeseries.ipynb auto-ml-forecasting-many-models.ipynb auto-ml-forecasting-orange-juice-sales.ipynb auto-ml-forecasting-pipelines.ipynb auto-ml-forecasting-univariate-recipe-experiment-settings.ipynb auto-ml-forecasting-univariate-recipe-run-experiment.ipynb auto-ml-image-classification-multiclass-batch-scoring.ipynb auto-ml-image-classification-multiclass.ipynb auto-ml-image-classification-multilabel.ipynb auto-ml-image-instance-segmentation.ipynb auto-ml-image-object-detection.ipynb auto-ml-classification-credit-card-fraud-local.ipynb binary-classification-metric-and-confidence-interval.ipynb auto-ml-regression-explanation-featurization.ipynb auto-ml-regression.ipynb	Tutorials showing how to build high quality machine learning models using Azure Automated Machine Learning.
automl-with-databricks		automl-databricks-local-01.ipynb automl-databricks-local-with-deployment.ipynb	no description
dataset-uploads		upload_dataframe_register_as_dataset.ipynb upload_directory_create_file_dataset.ipynb	no description
deploy-local		1.deploy-local.ipynb 2.deploy-local-cli.ipynb	no description
train-on-low-priority-aml-compute		train-on-low-priority-aml-compute.ipynb	Learn how to train models using low-priority AML compute. Low-priority compute costs significantly less than dedicated compute, but jobs on low priority compute can be preempted.
using-pipelines		dataset-and-pipelineparameter.ipynb image-classification.ipynb publish-and-run-using-rest-endpoint.ipynb style-transfer-parallel-run.ipynb	no description
using-rapids		1.train-and-hpo.ipynb 2.train-multi-gpu.ipynb	Learn how to accelerate PyData tools (Numpy, Pandas, Scikit-Learn, etc.) on NVIDIA GPUs with RAPIDS and Azure ML.

Notebooks (notebooks)

path	status	description

Train (workflows/train)

path	status	description
deepspeed/cifar/job.py		train CIFAR-10 using DeepSpeed and PyTorch
deepspeed/transformers/job.py		train Huggingface transformer using DeepSpeed
fastai/mnist-mlproject/job.py		train fastai resnet18 model on mnist data via mlflow mlproject
fastai/mnist/job.py		train fastai resnet18 model on mnist data
fastai/pets/job.py		train fastai resnet34 model on pets data
lightgbm/iris/job.py		train a lightgbm model on iris data
pytorch/cifar-distributed/job.py		train CNN model on CIFAR-10 dataset with distributed PyTorch
pytorch/mnist-mlproject/job.py		train a pytorch CNN model on mnist data via mlflow mlproject
pytorch/mnist/job.py		train a pytorch CNN model on mnist data
scikit-learn/diabetes-mlproject/job.py		train sklearn ridge model on diabetes data via mlflow mlproject
scikit-learn/diabetes/job.py		train sklearn ridge model on diabetes data
tensorflow/mnist-distributed-horovod/job.py		train tensorflow CNN model on mnist data distributed via horovod
tensorflow/mnist-distributed/job.py		train tensorflow CNN model on mnist data distributed via tensorflow
tensorflow/mnist/job.py		train tensorflow NN model on mnist data
transformers/glue/1-aml-finetune-job.py		Submit GLUE finetuning with Huggingface transformers library on Azure ML
transformers/glue/2-aml-comparison-of-sku-job.py		Experiment comparing training performance of GLUE finetuning task with differing hardware.
transformers/glue/3-aml-hyperdrive-job.py		Automatic hyperparameter optimization with Azure ML HyperDrive library.
xgboost/iris/job.py		train xgboost model on iris data

Deploy (workflows/deploy)

path	status	description
pytorch/mnist/job.py		deploy pytorch cnn model trained on mnist data to aks
scikit-learn/diabetes/job.py		deploy sklearn ridge model trained on diabetes data to AKS

Experimental tutorials (experimental)

path	status	notebooks	description	why experimental?
automl-model-testing		classification-TSI.ipynb configuration.ipynb forecasting-TSI.ipynb regression-TSI.ipynb	no description	unknown
deploy-triton		1.bidaf-ncd-local.ipynb	Learn how to efficiently deploy to GPUs with the Triton inference server and Azure ML.	in preview
using-pytorch-lightning		1.train-single-node.ipynb 2.log-with-tensorboard.ipynb 3.log-with-mlflow.ipynb	Learn how to train and log metrics with PyTorch Lightning and Azure ML.	issues with multinode pytorch lightning

Contents

A lightweight template repository for automating the ML lifecycle can be found here. The contents of this repository are described below.

Note: It is not recommended to fork this repository and use it as a template directly. This repository is structured to host a large number of examples and CI for automation and testing.

directory	description
experimental	self-contained directories of experimental tutorials
tutorials	self-contained directories of tutorials
workflows	self-contained directories of job to be run, organized by scenario then tool then project

Contributing

We welcome contributions and suggestions! Please see the contributing guidelines for details.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. Please see the code of conduct for details.

Reference

• Documentation