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're 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.

Note

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.

Important

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

Curated environments

Azure Container for PyTorch (ACPT)

Description: Recommended environment for Deep Learning with PyTorch on Azure containing the Azure Machine Learning SDK with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA\RocM, and NebulaML combined with optimizers like ORT Training, +DeepSpeed+MSCCL+ORT MoE, and checkpointing using NebulaML and more.

To learn more, see Azure Container for PyTorch (ACPT).

Note

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

PyTorch

Name: AzureML-pytorch-1.10-ubuntu18.04-py38-cuda11-gpu
Description: An environment for deep learning with PyTorch containing the Azure Machine Learning 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

LightGBM

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

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

Sklearn

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 Azure Machine Learning 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

TensorFlow

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

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

Automated ML (AutoML)

Azure Machine Learning 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
AzureML-AutoML-GPU	GPU	No
AzureML-AutoML-DNN-GPU	GPU	Yes

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

Support

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.