Azure Machine Learning (AML) Examples

Welcome to the AML examples!

Prerequisites

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

Installation

Clone this repository and install required packages:

git clone https://github.com/Azure/azureml-examples
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.py --subscription-id $ID, where $ID is your Azure subscription id. A resource group, AML 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.

By default, python setup.py will not provision all the compute targets needed to run every example in this repository - it will only create standard AML compute targets with auto scaledown and reasonable settings. Some examples will fail with a "compute target not found" error. To create the AKS and specialty AML compute targets, run python setup.py --create-aks True --create-V100 True.

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

Samples

Tutorials

path	status	notebooks	description
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		1.classification.ipynb	learn how to automate ML with pycaret
deploy-edge		ase-gpu.ipynb	learn how to use Edge device for model deployment and scoring
deploy-triton		1.densenet-local.ipynb 2.bidaf-aks-v100.ipynb	learn how to efficiently deploy to GPUs using triton inference server
music-with-ml		1.intro-to-magenta.ipynb	learn how to create music with ML using magenta
using-dask		1.intro-to-dask.ipynb 2.eds-at-scale.ipynb	learn how to read from cloud data and scale PyData tools (numpy, pandas, scikit-learn, etc.) with dask
using-mlflow		sklearn.ipynb	learn how to use AML as the backend for mlflow
using-optuna		1.intro-to-optuna.ipynb	learn how to optimize an objective function with optuna
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		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

Jupyter Notebooks

path	description
notebooks/train-lightgbm-local.ipynb	use AML and mlflow to track interactive experimentation in the cloud

Train

path	compute	environment	description
examples/train/deepspeed-cifar.py	AML - GPU	docker	train CIFAR-10 using DeepSpeed and PyTorch
examples/train/fastai-mnist-mlproject.py	AML - CPU	mlproject	train fastai resnet18 model on mnist data via mlflow mlproject
examples/train/fastai-mnist.py	AML - CPU	conda	train fastai resnet18 model on mnist data
examples/train/fastai-pets.py	AML - GPU	docker	train fastai resnet34 model on pets data
examples/train/lightgbm-iris.py	AML - CPU	pip	train a lightgbm model on iris data
examples/train/pytorch-mnist-mlproject.py	AML - GPU	mlproject	train a pytorch CNN model on mnist data via mlflow mlproject
examples/train/pytorch-mnist.py	AML - GPU	conda	train a pytorch CNN model on mnist data
examples/train/sklearn-diabetes-mlproject.py	AML - CPU	mlproject	train sklearn ridge model on diabetes data via mlflow mlproject
examples/train/sklearn-diabetes.py	AML - CPU	conda	train sklearn ridge model on diabetes data
examples/train/tensorflow-iris.py	AML - CPU	conda	train tensorflow NN model on iris data
examples/train/tensorflow-mnist-distributed-horovod.py	AML - GPU	conda	train tensorflow CNN model on mnist data distributed via horovod
examples/train/tensorflow-mnist-distributed.py	AML - GPU	conda	train tensorflow CNN model on mnist data distributed via tensorflow
examples/train/tensorflow-mnist.py	AML - GPU	conda	train tensorflow NN model on mnist data
examples/train/xgboost-iris.py	AML - CPU	pip	train xgboost model on iris data

Deploy

path	compute	description
examples/deploy/pytorch-mnist-aks-cpu.py	AKS - CPU	deploy pytorch CNN model trained on mnist data to AKS
examples/deploy/sklearn-diabetes-aks-cpu.py	AKS - CPU	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

• Azure Machine Learning Documentation
• Python SDK Documentation
• Azure Machine Learning Pipelines Notebooks