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Top-level directory for official Azure Machine Learning Python SDK v2 sample code. |
- An Azure subscription. If you don't have an Azure subscription, create a free account before you begin.
- Install the SDK v2
pip install azure-ai-ml
git clone https://github.com/Azure/azureml-examples
cd azureml-examples/sdk/python
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-multimodel | no description | |
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 | |
endpoints | online | online-endpoints-managed-identity-uai | no description | |
endpoints | online | online-endpoints-multimodel | no description | |
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 | auto-ml-forecasting-bike-share | no description | |
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-nlp-text-ner-task-distributed-sweeping | Train an NLP model using AutoML NLP on named-entity-recognition data, sweeping over models and parameters to get the best performance. | |
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 | [![distributed-cifar10](https://github.com/Azure/azureml-examples/actions/workflows/sdk-jobs-single-step-pytorch-distributed training-distributed-cifar10.yml/badge.svg?branch=main)](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 job schedule |
We welcome contributions and suggestions! Please see the contributing guidelines for details.
This project has adopted the Microsoft Open Source Code of Conduct. Please see the code of conduct for details.