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sample |
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Top-level directory for official Azure Machine Learning Python SDK v2 sample code. |
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.
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.
- An Azure subscription. If you don't have an Azure subscription, create a free account before you begin.
- Install the SDK v2
pip uninstall azure-ai-ml
pip install --pre 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 | 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 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.