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sample |
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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 | assets-in-registry | share-models-components-environments | no description |  |
| 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 |  |
| data-wrangling | interactive_data_wrangling.ipynb | interactive_data_wrangling | no description - This sample is excluded from automated tests |  |
| endpoints | batch | custom-output-batch | no description |  |
| endpoints | batch | imagenet-classifier-batch | no description |  |
| endpoints | batch | mlflow-for-batch-tabular | no description |  |
| endpoints | batch | mnist-batch | Create and test batch endpoint and deployement |  |
| endpoints | batch | text-summarization-batch | no description |  |
| 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 - This sample is excluded from automated tests |  |
| endpoints | online | online-endpoints-managed-identity-sai | no description |  |
| endpoints | online | online-endpoints-managed-identity-uai | no description |  |
| endpoints | online | online-endpoints-binary-payloads | no description |  |
| endpoints | online | online-endpoints-inference-schema | no description |  |
| endpoints | online | online-endpoints-keyvault | no description |  |
| endpoints | online | online-endpoints-multimodel | no description |  |
| endpoints | online | online-endpoints-openapi | 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 | 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-github-dau | no description |  |
| jobs | automl-standalone-jobs | automl-forecasting-orange-juice-sales-mlflow | no description |  |
| jobs | automl-standalone-jobs | auto-ml-forecasting-bike-share | 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-multiclass-sentiment-mlflow | no description |  |
| jobs | automl-standalone-jobs | automl-nlp-multiclass-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-multilabel-paper-cat | no description |  |
| jobs | automl-standalone-jobs | automl-nlp-text-ner-task-distributed-with-sweeping | 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 | no description |  |
| 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-in-pipeline | Create pipeline with automl node |  |
| jobs | pipelines | automl-text-classification-multilabel-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 - This sample is excluded from automated tests |  |
| 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 | debug-and-monitor | Run a Command to train a basic neural network with TensorFlow on the MNIST dataset |  |
| jobs | single-step | lightgbm-iris-sweep | Run hyperparameter sweep on a Command or CommandComponent |  |
| jobs | single-step | objectdetectionAzureML | no description |  |
| jobs | single-step | distributed-cifar10 | no description |  |
| 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 | Train, hyperparameter tune, and deploy a PyTorch model to classify chicken and turkey images to build a deep learning neural network (DNN) based on PyTorch's transfer learning tutorial. |  |
| 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 | Train and tune a machine learning model using scikit-learn training scripts to build a to classify iris flower images. - This sample is excluded from automated tests |  |
| 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 | Train, hyperparameter tune, and deploy a Keras model to classify handwritten digits using a deep neural network (DNN). - This sample is excluded from automated tests |  |
| jobs | single-step | train-hyperparameter-tune-deploy-with-tensorflow | Train, hyperparameter tune, and deploy a Tensorflow model to classify handwritten digits using a deep neural network (DNN). - This sample is excluded from automated tests |  |
| jobs | spark | submit_spark_pipeline_jobs | no description - This sample is excluded from automated tests |  |
| jobs | spark | submit_spark_standalone_jobs | no description - This sample is excluded from automated tests |  |
| resources | compute | attach_manage_spark_pools | no description - This sample is excluded from automated tests |  |
| resources | compute | compute | Create compute in Azure ML workspace |  |
| resources | connections | connections | no description |  |
| resources | datastores | datastore | Create datastores and use in a Command - This sample is excluded from automated tests |  |
| resources | registry | registry-create | no description |  |
| resources | workspace | workspace | Create Azure ML workspace |  |
| responsible-ai | responsibleaidashboard-diabetes-decision-making | responsibleaidashboard-diabetes-decision-making | no description |  |
| responsible-ai | responsibleaidashboard-diabetes-regression-model-debugging | responsibleaidashboard-diabetes-regression-model-debugging | no description |  |
| responsible-ai | responsibleaidashboard-housing-classification-model-debugging | responsibleaidashboard-housing-classification-model-debugging | no description |  |
| responsible-ai | responsibleaidashboard-housing-decision-making | responsibleaidashboard-housing-decision-making | no description |  |
| responsible-ai | responsibleaidashboard-programmer-regression-model-debugging | responsibleaidashboard-programmer-regression-model-debugging | no description |  |
| schedules | job-schedule.ipynb | job-schedule | Create a component asset |  |
| using-mlflow | logging-models | logging_model_with_mlflow | no description - This sample is excluded from automated tests |  |
| using-mlflow | migrating-scoring-to-mlflow | scoring_to_mlmodel | no description - This sample is excluded from automated tests |  |
| using-mlflow | model-management | model_management | no description - This sample is excluded from automated tests |  |
| using-mlflow | no-code-deployment | deploying_with_mlflow | no description - This sample is excluded from automated tests |  |
| using-mlflow | no-code-deployment | track_with_databricks_deploy_aml | no description - This sample is excluded from automated tests |  |
| using-mlflow | run-history | run_history | no description - This sample is excluded from automated tests |  |
| using-mlflow | train-with-mlflow | xgboost_classification_mlflow | no description - This sample is excluded from automated tests |  |
| using-mlflow | train-with-mlflow | xgboost_nested_runs | no description - This sample is excluded from automated tests |  |
| using-mlflow | train-with-mlflow | xgboost_service_principal | no description - This sample is excluded from automated tests |  |
| using-mlflow | using-rest-api | using_mlflow_rest_api | no description - This sample is excluded from automated tests |  |
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.