Azure Machine Learning Curated Environments

This article lists the curated environments with latest framework versions in Azure Machine Learning. Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They are backed by cached Docker images that use the latest version of the Azure Machine Learning SDK, reducing the run preparation cost and allowing for faster deployment time. Use these environments to quickly get started with various machine learning frameworks.


Use the Python SDK, CLI, or Azure Machine Learning studio to get the full list of environments and their dependencies. For more information, see the environments article.

Why should I use curated environments?

  • Reduces training and deployment latency.
  • Improves training and deployment success rate.
  • Avoid unnecessary image builds.
  • Only have required dependencies and access right in the image/container. 


To view more information about curated environment packages and versions, visit the Environments tab in the Azure Machine Learning studio.

Curated environments


Items marked (preview) in this article are currently in public preview. The preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Azure Container for PyTorch (ACPT) (preview)

Name: AzureML-ACPT-pytorch-1.12-py39-cuda11.6-gpu
Description: The Azure Curated Environment for PyTorch is our latest PyTorch curated environment. It is optimized for large, distributed deep learning workloads and comes pre-packaged with the best of Microsoft technologies for accelerated training, e.g., OnnxRuntime Training (ORT), DeepSpeed, MSCCL, etc.

The following configurations are supported:

Environment Name OS GPU Version Python Version PyTorch Version ORT-training Version DeepSpeed Version torch-ort Version
AzureML-ACPT-pytorch-1.12-py39-cuda11.6-gpu Ubuntu 20.04 cu116 3.9 1.12.1 1.13.1 0.7.3 1.13.1
AzureML-ACPT-pytorch-1.12-py38-cuda11.6-gpu Ubuntu 20.04 cu116 3.8 1.12.1 1.12.0 0.7.3 1.12.0
AzureML-ACPT-pytorch-1.11-py38-cuda11.5-gpu Ubuntu 20.04 cu115 3.8 1.11.0 1.11.1 0.7.3 1.11.0
AzureML-ACPT-pytorch-1.11-py38-cuda11.3-gpu Ubuntu 20.04 cu113 3.8 1.11.0 1.11.1 0.7.3 1.11.0


Currently, due to underlying cuda and cluster incompatibilities, on NC series only AzureML-ACPT-pytorch-1.11-py38-cuda11.3-gpu with cuda 11.3 can be used.


Name: AzureML-pytorch-1.10-ubuntu18.04-py38-cuda11-gpu
Description: An environment for deep learning with PyTorch containing the AzureML Python SDK and other Python packages.

  • GPU: Cuda11
  • OS: Ubuntu18.04
  • PyTorch: 1.10

Other available PyTorch environments:

  • AzureML-pytorch-1.9-ubuntu18.04-py37-cuda11-gpu
  • AzureML-pytorch-1.8-ubuntu18.04-py37-cuda11-gpu
  • AzureML-pytorch-1.7-ubuntu18.04-py37-cuda11-gpu


Name: AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu
Description: An environment for machine learning with Scikit-learn, LightGBM, XGBoost, Dask containing the AzureML Python SDK and other packages.

  • OS: Ubuntu18.04
  • Dask: 2021.6
  • LightGBM: 3.2
  • Scikit-learn: 0.24
  • XGBoost: 1.4


Name: AzureML-sklearn-1.0-ubuntu20.04-py38-cpu
Description: An environment for tasks such as regression, clustering, and classification with Scikit-learn. Contains the AzureML Python SDK and other Python packages.

  • OS: Ubuntu20.04
  • Scikit-learn: 1.0

Other available Sklearn environments:

  • AzureML-sklearn-0.24-ubuntu18.04-py37-cpu


Name: AzureML-tensorflow-2.4-ubuntu18.04-py37-cuda11-gpu
Description: An environment for deep learning with TensorFlow containing the AzureML Python SDK and other Python packages.

  • GPU: Cuda11
  • Horovod: 2.4.1
  • OS: Ubuntu18.04
  • TensorFlow: 2.4

Automated ML (AutoML)

Azure ML pipeline training workflows that use AutoML automatically selects a curated environment based on the compute type and whether DNN is enabled. AutoML provides the following curated environments:

Name Compute Type DNN enabled
AzureML-AutoML CPU No
AzureML-AutoML-DNN CPU Yes

For more information on AutoML and Azure ML pipelines, see use automated ML in an Azure Machine Learning pipeline in Python.


Version updates for supported environments, including the base images they reference, are released every two weeks to address vulnerabilities no older than 30 days. Based on usage, some environments may be deprecated (hidden from the product but usable) to support more common machine learning scenarios.