/azureml-examples

Official community-driven Azure Machine Learning Examples, tested with GitHub Actions

Primary LanguagePythonMIT LicenseMIT

Azure Machine Learning Examples

smoke cleanup code style: black license: MIT

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.

Setup

Clone this repository and install required packages:

git clone https://github.com/Azure/azureml-examples --depth 1
cd azureml-examples
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.

Contents

A lightweight template repository for automating the ML lifecycle can be found here.

directory description
.cloud cloud templates
.github GitHub specific files like Actions workflow yaml definitions and issue templates
notebooks interactive jupyter notebooks for iterative ML development
tutorials self-contained directories of end-to-end tutorials
workflows self-contained directories of job to be run, organized by scenario then tool then project

Examples

Tutorials

path status notebooks description
an-introduction an-introduction 1.hello-world.ipynb
2.pytorch-model.ipynb
3.pytorch-model-cloud-data.ipynb		 learn the basics of Azure Machine Learning
automl-with-pycaret automl-with-pycaret 1.classification.ipynb learn how to automate ML with PyCaret
deploy-edge deploy-edge ase-gpu.ipynb learn how to use Edge device for model deployment and scoring
deploy-triton deploy-triton 1.densenet-local.ipynb
2.bidaf-aks-v100.ipynb		 learn how to efficiently deploy to GPUs using triton inference server
using-dask using-dask 1.intro-to-dask.ipynb
2.dask-cloudprovider.ipynb		 learn how to read from cloud data and scale PyData tools (Numpy, Pandas, Scikit-Learn, etc.) with Dask
using-pytorch-lightning using-pytorch-lightning 1.train-single-node.ipynb
2.log-with-tensorboard.ipynb
3.log-with-mlflow.ipynb
4.train-multi-node-ddp.ipynb		 learn how to train and log metrics with PyTorch Lightning
using-rapids 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
using-xgboost using-xgboost 1.local-eda.ipynb
2.distributed-cpu.ipynb		 learn how to use XGBoost on Azure

Notebooks

path status description
notebooks/train-lightgbm-local.ipynb train-lightgbm-local use mlflow for tracking local notebook experimentation in the cloud

Train

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

Deploy

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

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