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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-data-using-registry | no description |  |
| 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 | mlflow-for-batch-tabular | no description |  |
| endpoints | batch | text-summarization-batch | no description |  |
| endpoints | batch | imagenet-classifier-batch | no description |  |
| endpoints | batch | imagenet-classifier-mlflow | no description |  |
| endpoints | batch | mnist-batch | Create and test batch endpoint and deployement |  |
| endpoints | batch | sdk-deploy-and-test | no description |  |
| endpoints | batch | sdk-deploy-and-test | no description |  |
| endpoints | batch | sdk-deploy-and-test | 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 | 1_langchain_basic_deploy | Use an online endpoint to deploy a Langchain application, combining the power of LLMs with Azure ML's inferencing management. |  |
| endpoints | online | 1_semantic_http_server | Use an online endpoint to deploy a Semantic Kernel application, combining the power of LLMs with Azure ML's inferencing management. |  |
| 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-with-script | 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-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. |  |
| featurestore_sample | notebooks | [1. Develop a feature set and register with managed feature store](featurestore_sample/notebooks/sdk_and_cli/1. Develop a feature set and register with managed feature store.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_and_cli-1. Develop a feature set and register with managed feature store.yml) |
| featurestore_sample | notebooks | [2. Enable materialization and backfill feature data](featurestore_sample/notebooks/sdk_and_cli/2. Enable materialization and backfill feature data.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_and_cli-2. Enable materialization and backfill feature data.yml) |
| featurestore_sample | notebooks | [3. Experiment and train models using features](featurestore_sample/notebooks/sdk_and_cli/3. Experiment and train models using features.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_and_cli-3. Experiment and train models using features.yml) |
| featurestore_sample | notebooks | [4. Enable recurrent materialization and run batch inference](featurestore_sample/notebooks/sdk_and_cli/4. Enable recurrent materialization and run batch inference.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_and_cli-4. Enable recurrent materialization and run batch inference.yml) |
| featurestore_sample | notebooks | [Network Isolation for Feature store](featurestore_sample/notebooks/sdk_and_cli/network_isolation/Network Isolation for Feature store.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_and_cli-network_isolation-Network Isolation for Feature store.yml) |
| featurestore_sample | notebooks | [1. Develop a feature set and register with managed feature store](featurestore_sample/notebooks/sdk_only/1. Develop a feature set and register with managed feature store.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_only-1. Develop a feature set and register with managed feature store.yml) |
| featurestore_sample | notebooks | [2. Enable materialization and backfill feature data](featurestore_sample/notebooks/sdk_only/2. Enable materialization and backfill feature data.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_only-2. Enable materialization and backfill feature data.yml) |
| featurestore_sample | notebooks | [3. Experiment and train models using features](featurestore_sample/notebooks/sdk_only/3. Experiment and train models using features.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_only-3. Experiment and train models using features.yml) |
| featurestore_sample | notebooks | [4. Enable recurrent materialization and run batch inference](featurestore_sample/notebooks/sdk_only/4. Enable recurrent materialization and run batch inference.ipynb) | no description | [](https://github.com/Azure/azureml-examples/actions/workflows/sdk-featurestore_sample-notebooks-sdk_only-4. Enable recurrent materialization and run batch inference.yml) |
| foundation-models | azure_openai | openai_completions_finetune | no description |  |
| foundation-models | azure_openai | openai_chat_finetune_pipeline | no description |  |
| foundation-models | huggingface | question-answering-online-endpoint | no description |  |
| foundation-models | huggingface | text-generation-streaming-online-endpoint | no description |  |
| foundation-models | huggingface | token-classification-online-endpoint | no description |  |
| foundation-models | huggingface | translation-online-endpoint | no description |  |
| foundation-models | huggingface | zero-shot-classification-online-endpoint | no description |  |
| foundation-models | system | fill-mask | no description |  |
| foundation-models | system | question-answering | no description |  |
| foundation-models | system | abstractive-and-extractive-summarization | no description |  |
| foundation-models | system | entailment-contradiction | no description |  |
| foundation-models | system | sentiment-analysis | no description |  |
| foundation-models | system | text-generation | no description |  |
| foundation-models | system | news-articles-entity-recognition | no description |  |
| foundation-models | system | translation-romanian-to-english | no description |  |
| foundation-models | system | extractive-qa | no description |  |
| foundation-models | system | news-summary | no description |  |
| foundation-models | system | emotion-detection | no description |  |
| foundation-models | system | token-classification | no description |  |
| foundation-models | system | translation | no description |  |
| foundation-models | system | import_model_into_registry | no description |  |
| foundation-models | system | asr-batch-endpoint | no description |  |
| foundation-models | system | asr-online-endpoint | no description |  |
| foundation-models | system | fill-mask-batch-endpoint | no description |  |
| foundation-models | system | fill-mask-online-endpoint | no description |  |
| foundation-models | system | question-answering-batch-endpoint | no description |  |
| foundation-models | system | question-answering-online-endpoint | no description |  |
| foundation-models | system | summarization-batch-endpoint | no description |  |
| foundation-models | system | summarization-online-endpoint | no description |  |
| foundation-models | system | entailment-contradiction-batch | no description |  |
| foundation-models | system | text-classification-online-endpoint | no description |  |
| foundation-models | system | llama-prepare-uai | no description |  |
| foundation-models | system | llama-safe-batch-deployment | no description |  |
| foundation-models | system | llama-safe-online-deployment | no description |  |
| foundation-models | system | text-generation-batch-endpoint | no description |  |
| foundation-models | system | text-generation-online-endpoint-dolly | no description |  |
| foundation-models | system | text-generation-online-endpoint | no description |  |
| foundation-models | system | token-classification-batch-endpoint | no description |  |
| foundation-models | system | token-classification-online-endpoint | no description |  |
| foundation-models | system | translation-batch-endpoint | no description |  |
| foundation-models | system | translation-online-endpoint | no description |  |
| generative-ai | promptflow | create_faiss_index | no description | |
| generative-ai | rag | acs_mlindex_with_langchain | no description | |
| generative-ai | rag | s3_to_acs_mlindex_with_langchain | no description | |
| generative-ai | rag | db_copilot_with_rag | no description | |
| generative-ai | rag | faiss_mlindex_with_langchain | no description | |
| generative-ai | rag | |||
| generative-ai | rag | url_to_faiss_incremental_embeddings_with_tabular_data | no description | |
| generative-ai | rag | mlindex_with_testgen | no description | |
| generative-ai | rag | qa_data_generation | no description | |
| jobs | automl-standalone-jobs | automl-classification-task-bankmarketing-serverless | 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-distributed-tcn | no description |  |
| 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 | automl-forecasting-recipe-univariate-experiment-settings | no description |  |
| jobs | automl-standalone-jobs | automl-forecasting-recipe-univariate-run | no description |  |
| 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 | odFridgeObjects_jsonl2coco | no description |  |
| 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 | parallel | oj_sales_prediction | Create pipeline with parallel node to do batch inference |  |
| jobs | parallel | iris_batch_prediction | Create pipeline with parallel node to do batch inference |  |
| jobs | parallel | mnist_batch_prediction | Create pipeline with parallel node to do batch inference |  |
| jobs | pipelines | pipeline_with_components_from_yaml | Create pipeline with CommandComponents from local YAML file |  |
| jobs | pipelines | pipeline_with_components_from_yaml_serverless | 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 |  |
| jobs | pipelines | nyc_taxi_data_regression_with_pipeline_component | Create pipeline with CommandComponents from local YAML file |  |
| jobs | pipelines | pipeline_with_train_eval_pipeline_component | Create pipeline with CommandComponents from local YAML file |  |
| jobs | pipelines | automl-forecasting-demand-hierarchical-timeseries-in-pipeline | no description |  |
| jobs | pipelines | automl-forecasting-demand-many-models-in-pipeline | no description |  |
| jobs | pipelines | flow_in_pipeline | Create pipeline using components to run a distributed job with tensorflow |  |
| 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 | 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 | tutorial | 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 | run_interactive_session_notebook | no description |  |
| jobs | spark | submit_spark_pipeline_jobs | no description |  |
| jobs | spark | submit_spark_standalone_jobs | no description |  |
| jobs | spark | submit_spark_standalone_jobs_managed_vnet | no description |  |
| resources | compute | attach_manage_spark_pools | no description |  |
| 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 | tabular | responsibleaidashboard-diabetes-decision-making | no description |  |
| responsible-ai | tabular | responsibleaidashboard-diabetes-regression-model-debugging | no description |  |
| responsible-ai | tabular | responsibleaidashboard-housing-classification-model-debugging | no description |  |
| responsible-ai | tabular | responsibleaidashboard-housing-decision-making | no description |  |
| responsible-ai | tabular | responsibleaidashboard-programmer-regression-model-debugging | no description |  |
| responsible-ai | text | responsibleaidashboard-multilabel-text-classification-covid-events | no description |  |
| responsible-ai | text | responsibleaidashboard-text-classification-DBPedia | no description |  |
| responsible-ai | text | responsibleaidashboard-text-classification-blbooksgenre | no description |  |
| responsible-ai | text | responsibleaidashboard-text-question-answering-squad | no description |  |
| responsible-ai | vision | responsibleaidashboard-automl-image-classification-fridge | no description |  |
| responsible-ai | vision | responsibleaidashboard-automl-object-detection-fridge-private-data | no description |  |
| responsible-ai | vision | responsibleaidashboard-image-classification-fridge | no description |  |
| responsible-ai | vision | responsibleaidashboard-image-multilabel-classification-fridge | no description |  |
| responsible-ai | vision | responsibleaidashboard-object-detection-MSCOCO | no description |  |
| schedules | job-schedule.ipynb | job-schedule | Create a component asset |  |
| using-mlflow | deploy | mlflow_sdk_online_endpoints | no description - This sample is excluded from automated tests |  |
| using-mlflow | deploy | mlflow_sdk_online_endpoints_progresive | no description - This sample is excluded from automated tests |  |
| using-mlflow | deploy | mlflow_sdk_web_service | no description - This sample is excluded from automated tests |  |
| using-mlflow | deploy | scoring_to_mlmodel | no description - This sample is excluded from automated tests |  |
| using-mlflow | deploy | track_with_databricks_deploy_aml | 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 | runs-management | run_history | no description - This sample is excluded from automated tests |  |
| using-mlflow | train-and-log | keras_mnist_with_mlflow | no description - This sample is excluded from automated tests |  |
| using-mlflow | train-and-log | logging_and_customizing_models | no description - This sample is excluded from automated tests |  |
| using-mlflow | train-and-log | xgboost_classification_mlflow | no description - This sample is excluded from automated tests |  |
| using-mlflow | train-and-log | xgboost_nested_runs | no description - This sample is excluded from automated tests |  |
| using-mlflow | train-and-log | 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 |  |
| using-mltable | delimited-files-example | delimited-files-example | no description |  |
| using-mltable | delta-lake-example | delta-lake-example | no description |  |
| using-mltable | from-paths-example | from-paths-example | no description |  |
| using-mltable | local-to-cloud | mltable-local-to-cloud | no description |  |
| using-mltable | quickstart | mltable-quickstart | no description |  |
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.