Azure Machine Learning SDK (v2) (preview) examples

Public Preview

We are excited to introduce the public preview of Azure Machine Learning Python SDK v2.

Please note that this Public Preview release is subject to the Supplemental Terms of Use for Microsoft Azure Previews.

How can I provide feedback?

If you are facing any issues while using the new feature, please reach out to Azure ML SDK feedback. For general feedback, please submit an GitHub issue.

Prerequisites

1. An Azure subscription. If you don't have an Azure subscription, create a free account before you begin.

Getting started

1. Install the SDK v2

pip uninstall azure-ai-ml

pip install --pre azure-ai-ml

Clone examples repository

git clone https://github.com/Azure/azureml-examples
cd azureml-examples/sdk/python

Examples available

Test Status is for branch - main

Area	Sub-Area	Notebook	Description	Status
assets	component	component	Create a component asset
assets	data	data	Read, write and register a data asset
assets	data	working_with_mltable	Read, write and register a data asset
assets	environment	environment	Create custom environments from docker and/or conda YAML
assets	model	model	Create model from local files, cloud files, Runs
endpoints	batch	mnist-nonmlflow	Create and test batch endpoint and deployement
endpoints	online	online-endpoints-custom-container	Deploy a custom container as an online endpoint. Use web servers other than the default Python Flask server used by Azure ML without losing the benefits of Azure ML's built-in monitoring, scaling, alerting, and authentication.
endpoints	online	online-endpoints-triton-cc	Deploy a custom container as an online endpoint. Use web servers other than the default Python Flask server used by Azure ML without losing the benefits of Azure ML's built-in monitoring, scaling, alerting, and authentication.
endpoints	online	kubernetes-online-endpoints-safe-rollout	Safely rollout a new version of a web service to production by rolling out the change to a small subset of users/requests before rolling it out completely
endpoints	online	kubernetes-online-endpoints-simple-deployment	Use an online endpoint to deploy your model, so you don't have to create and manage the underlying infrastructure
endpoints	online	debug-online-endpoints-locally-in-visual-studio-code	no description
endpoints	online	online-endpoints-managed-identity-sai	no description - This sample is excluded from automated tests
endpoints	online	online-endpoints-managed-identity-uai	no description - This sample is excluded from automated tests
endpoints	online	online-endpoints-safe-rollout	Safely rollout a new version of a web service to production by rolling out the change to a small subset of users/requests before rolling it out completely
endpoints	online	online-endpoints-simple-deployment	Use an online endpoint to deploy your model, so you don't have to create and manage the underlying infrastructure
endpoints	online	online-endpoints-deploy-mlflow-model	Deploy an mlflow model to an online endpoint. This will be a no-code-deployment. It doesn't require scoring script and environment.
endpoints	online	online-endpoints-triton	Deploy a custom container as an online endpoint. Use web servers other than the default Python Flask server used by Azure ML without losing the benefits of Azure ML's built-in monitoring, scaling, alerting, and authentication.
jobs	automl-standalone-jobs	automl-classification-task-bankmarketing-mlflow	no description
jobs	automl-standalone-jobs	automl-classification-task-bankmarketing	no description
jobs	automl-standalone-jobs	mlflow-model-local-inference-test	no description - This sample is excluded from automated tests
jobs	automl-standalone-jobs	automl-forecasting-task-energy-demand-advanced-mlflow	no description
jobs	automl-standalone-jobs	automl-forecasting-task-energy-demand-advanced	no description
jobs	automl-standalone-jobs	mlflow-model-local-inference-test	no description - This sample is excluded from automated tests
jobs	automl-standalone-jobs	automl-image-classification-multiclass-task-fridge-items	no description
jobs	automl-standalone-jobs	mlflow-model-local-inference-test	no description - This sample is excluded from automated tests
jobs	automl-standalone-jobs	automl-image-classification-multilabel-task-fridge-items	no description
jobs	automl-standalone-jobs	mlflow-model-local-inference-test	no description - This sample is excluded from automated tests
jobs	automl-standalone-jobs	automl-image-instance-segmentation-task-fridge-items	no description
jobs	automl-standalone-jobs	mlflow-model-local-inference-test	no description - This sample is excluded from automated tests
jobs	automl-standalone-jobs	image-object-detection-batch-scoring-non-mlflow-model	no description
jobs	automl-standalone-jobs	automl-image-object-detection-task-fridge-items	no description
jobs	automl-standalone-jobs	mlflow-model-local-inference-test	no description - This sample is excluded from automated tests
jobs	automl-standalone-jobs	automl-nlp-text-classification-multiclass-task-sentiment-mlflow	no description
jobs	automl-standalone-jobs	automl-nlp-text-classification-multiclass-task-sentiment	no description
jobs	automl-standalone-jobs	mlflow-model-local-inference-test	no description - This sample is excluded from automated tests
jobs	automl-standalone-jobs	automl-nlp-text-classification-multilabel-task-paper-cat	no description
jobs	automl-standalone-jobs	automl-nlp-text-ner-task	no description
jobs	automl-standalone-jobs	automl-regression-task-hardware-performance	no description
jobs	configuration.ipynb	configuration	Setting up your Azure Machine Learning services workspace and configuring needed resources
jobs	multicloud-configuration.ipynb	multicloud-configuration	Setting up your Azure Machine Learning services workspace and configuring needed resources - This sample is excluded from automated tests
jobs	pipelines	pipeline_with_components_from_yaml	Create pipeline with CommandComponents from local YAML file
jobs	pipelines	pipeline_with_python_function_components	Create pipeline with command_component decorator
jobs	pipelines	pipeline_with_hyperparameter_sweep	Use sweep (hyperdrive) in pipeline to train mnist model using tensorflow
jobs	pipelines	pipeline_with_non_python_components	Create a pipeline with command function
jobs	pipelines	pipeline_with_registered_components	Register component and then use these components to build pipeline
jobs	pipelines	pipeline_with_parallel_nodes	Create pipeline with parallel node to do batch inference
jobs	pipelines	automl-classification-bankmarketing-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-forecasting-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-image-classification-multiclass-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-image-classification-multilabel-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-image-instance-segmentation-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-image-object-detection-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-regression-house-pricing-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-text-classification-multilabel-paper-categorization-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-text-classification-sentiment-in-pipeline	Create pipeline with automl node
jobs	pipelines	automl-text-ner-named-entity-recognition-in-pipeline	Create pipeline with automl node
jobs	pipelines	pipeline_with_spark_nodes	Create pipeline with spark node
jobs	pipelines	train_mnist_with_tensorflow	Create pipeline using components to run a distributed job with tensorflow
jobs	pipelines	train_cifar_10_with_pytorch	Get data, train and evaluate a model in pipeline with Components
jobs	pipelines	nyc_taxi_data_regression	Build pipeline with components for 5 jobs - prep data, transform data, train model, predict results and evaluate model performance
jobs	pipelines	image_classification_with_densenet	Create pipeline to train cnn image classification model
jobs	pipelines	image_classification_keras_minist_convnet	Create pipeline to train cnn image classification model with keras
jobs	pipelines	rai_pipeline_sample	Create sample RAI pipeline
jobs	single-step	lightgbm-iris-sweep	Run hyperparameter sweep on a Command or CommandComponent
jobs	single-step	[distributed-cifar10](jobs/single-step/pytorch/distributed training/distributed-cifar10.ipynb)	no description	[](https://github.com/Azure/azureml-examples/actions/workflows/sdk-jobs-single-step-pytorch-distributed training-distributed-cifar10.yml)
jobs	single-step	pytorch-iris	Run Command to train a neural network with PyTorch on Iris dataset
jobs	single-step	train-hyperparameter-tune-deploy-with-pytorch	Run Command to train a neural network with PyTorch on Iris dataset
jobs	single-step	accident-prediction	Run R in a Command to train a prediction model
jobs	single-step	sklearn-diabetes	Run Command to train a scikit-learn LinearRegression model on the Diabetes dataset
jobs	single-step	iris-scikit-learn	Run Command to train a scikit-learn SVM on the Iris dataset
jobs	single-step	sklearn-mnist	Run a Command to train a scikit-learn SVM on the mnist dataset.
jobs	single-step	train-hyperparameter-tune-with-sklearn	Run a Command to train a scikit-learn SVM on the mnist dataset.
jobs	single-step	tensorflow-mnist-distributed-horovod	Run a Distributed Command to train a basic neural network with distributed MPI on the MNIST dataset using Horovod
jobs	single-step	tensorflow-mnist-distributed	Run a Distributed Command to train a basic neural network with TensorFlow on the MNIST dataset
jobs	single-step	tensorflow-mnist	Run a Command to train a basic neural network with TensorFlow on the MNIST dataset
jobs	single-step	train-hyperparameter-tune-deploy-with-keras	Run a Command to train a basic neural network with TensorFlow on the MNIST dataset
jobs	single-step	train-hyperparameter-tune-deploy-with-tensorflow	Run a Command to train a basic neural network with TensorFlow on the MNIST dataset
resources	compute	compute	Create compute in Azure ML workspace
resources	datastores	datastore	Create datastores and use in a Command - This sample is excluded from automated tests
resources	workspace	workspace	Create Azure ML workspace
schedules	job-schedule.ipynb	job-schedule	Create a component asset

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