Manage Azure Machine Learning environments with the CLI & SDK (v2)

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

Azure Machine Learning environments define the execution environments for your jobs or deployments and encapsulate the dependencies for your code. Azure Machine Learning uses the environment specification to create the Docker container that your training or scoring code runs in on the specified compute target. You can define an environment from a conda specification, Docker image, or Docker build context.

In this article, learn how to create and manage Azure Machine Learning environments using the SDK & CLI (v2).


Before following the steps in this article, make sure you have the following prerequisites:

  • An Azure Machine Learning workspace. If you don't have one, use the steps in the Quickstart: Create workspace resources article to create one.

  • The Azure CLI and the ml extension or the Azure Machine Learning Python SDK v2:


For a full-featured development environment, use Visual Studio Code and the Azure Machine Learning extension to manage Azure Machine Learning resources and train machine learning models.

Clone examples repository

To run the training examples, first clone the examples repository. For the CLI examples, change into the cli directory. For the SDK examples, change into the sdk/python/assets/environment directory:

git clone --depth 1

Note that --depth 1 clones only the latest commit to the repository, which reduces time to complete the operation.

Connect to the workspace


Use the following tabs to select the method you want to use to work with environments. Selecting a tab will automatically switch all the tabs in this article to the same tab. You can select another tab at any time.

When using the Azure CLI, you need identifier parameters - a subscription, resource group, and workspace name. While you can specify these parameters for each command, you can also set defaults that are used for all the commands. Use the following commands to set default values. Replace <subscription ID>, <Azure Machine Learning workspace name>, and <resource group> with the values for your configuration:

az account set --subscription <subscription ID>
az configure --defaults workspace=<Azure Machine Learning workspace name> group=<resource group>

Curated environments

There are two types of environments in Azure Machine Learning: curated and custom environments. Curated environments are predefined environments containing popular ML frameworks and tooling. Custom environments are user-defined and can be created via az ml environment create.

Curated environments are provided by Azure Machine Learning and are available by default. Azure Machine Learning routinely updates these environments with the latest framework version releases and maintains them for bug fixes and security patches. They're backed by cached Docker images, which reduce job preparation cost and model deployment time.

You can use these curated environments out of the box for training or deployment by referencing a specific version or latest version of the environment. Use the following syntax: azureml://registries/azureml/environment/<curated-environment-name>/versions/<version-number> or azureml://registries/azureml/environment/<curated-environment-name>/labels/latest. You can also use them as a reference for your own custom environments by modifying the Dockerfiles that back these curated environments.

You can see the set of available curated environments in the Azure Machine Learning studio UI, or by using the CLI (v2) via az ml environment list.


When working with curated environments in the CLI or SDK, the environment name begins with AzureML- followed by the name of the curated environment. When using the Azure Machine Learning studio, they do not have this prefix. The reason for this difference is that the studio UI displays curated and custom environments on separate tabs, so the prefix isn't necessary. The CLI and SDK do not have this separation, so the prefix is used to differentiate between curated and custom environments.

Create a custom environment

You can define an environment from a Docker image, a Docker build context, and a conda specification with Docker image.

Create an environment from a Docker image

To define an environment from a Docker image, provide the image URI of the image hosted in a registry such as Docker Hub or Azure Container Registry.

The following example is a YAML specification file for an environment defined from a Docker image. An image from the official PyTorch repository on Docker Hub is specified via the image property in the YAML file.

name: docker-image-example
image: pytorch/pytorch:latest
description: Environment created from a Docker image.

To create the environment:

az ml environment create --file assets/environment/docker-image.yml


Azure Machine Learning maintains a set of CPU and GPU Ubuntu Linux-based base images with common system dependencies. For example, the GPU images contain Miniconda, OpenMPI, CUDA, cuDNN, and NCCL. You can use these images for your environments, or use their corresponding Dockerfiles as reference when building your own custom images.

For the set of base images and their corresponding Dockerfiles, see the AzureML-Containers repo.

Create an environment from a Docker build context

Instead of defining an environment from a prebuilt image, you can also define one from a Docker build context. To do so, specify the directory that serves as the build context. This directory should contain a Dockerfile (not larger than 1MB) and any other files needed to build the image.

The following example is a YAML specification file for an environment defined from a build context. The local path to the build context folder is specified in the build.path field, and the relative path to the Dockerfile within that build context folder is specified in the build.dockerfile_path field. If build.dockerfile_path is omitted in the YAML file, Azure Machine Learning looks for a Dockerfile named Dockerfile at the root of the build context.

In this example, the build context contains a Dockerfile named Dockerfile and a requirements.txt file that is referenced within the Dockerfile for installing Python packages.

name: docker-context-example
  path: docker-contexts/python-and-pip

To create the environment:

az ml environment create --file assets/environment/docker-context.yml

Azure Machine Learning starts building the image from the build context when the environment is created. You can monitor the status of the build and view the build logs in the studio UI.

Create an environment from a conda specification

You can define an environment using a standard conda YAML configuration file that includes the dependencies for the conda environment. See Creating an environment manually for information on this standard format.

You must also specify a base Docker image for this environment. Azure Machine Learning builds the conda environment on top of the Docker image provided. If you install some Python dependencies in your Docker image, those packages won't exist in the execution environment thus causing runtime failures. By default, Azure Machine Learning builds a Conda environment with dependencies you specified, and runs the job in that environment instead of using any Python libraries that you installed on the base image.

The following example is a YAML specification file for an environment defined from a conda specification. Here the relative path to the conda file from the Azure Machine Learning environment YAML file is specified via the conda_file property. You can alternatively define the conda specification inline using the conda_file property, rather than defining it in a separate file.

name: docker-image-plus-conda-example
conda_file: conda-yamls/pydata.yml
description: Environment created from a Docker image plus Conda environment.

To create the environment:

az ml environment create --file assets/environment/docker-image-plus-conda.yaml

Azure Machine Learning builds the final Docker image from this environment specification when the environment is used in a job or deployment. You can also manually trigger a build of the environment in the studio UI.

Manage environments

The SDK and CLI (v2) also allow you to manage the lifecycle of your Azure Machine Learning environment assets.


List all the environments in your workspace:

az ml environment list

List all the environment versions under a given name:

az ml environment list --name docker-image-example


Get the details of a specific environment:

az ml environment show --name docker-image-example --version 1


Update mutable properties of a specific environment:

az ml environment update --name docker-image-example --version 1 --set description="This is an updated description."


For environments, only description and tags can be updated. All other properties are immutable; if you need to change any of those properties you should create a new version of the environment.


Archiving an environment hides it by default from list queries (az ml environment list). You can still continue to reference and use an archived environment in your workflows. You can archive either all versions of an environment or only a specific version.

If you don't specify a version, all versions of the environment under that given name are archived. If you create a new environment version under an archived environment container, that new version is automatically set as archived as well.

Archive all versions of an environment:

az ml environment archive --name docker-image-example

Archive a specific environment version:

az ml environment archive --name docker-image-example --version 1


Archiving an environment's version does not delete the cached image in the container registry. If you wish to delete the cached image associated with a specific environment, you can use the command az acr repository delete on the environment's associated repository.

Use environments for training

To use a custom environment for a training job, specify the environment field of the job YAML configuration. You can either reference an existing registered Azure Machine Learning environment via environment: azureml:<environment-name>:<environment-version> or environment: azureml:<environment-name>@latest (to reference the latest version of an environment), or define an environment specification inline. If defining an environment inline, don't specify the name and version fields, as these environments are treated as "unregistered" environments and aren't tracked in your environment asset registry.

When you submit a training job, the building of a new environment can take several minutes. The duration depends on the size of the required dependencies. The environments are cached by the service. So as long as the environment definition remains unchanged, you incur the full setup time only once.

For more information on how to use environments in jobs, see Train models.

Use environments for model deployments

You can also use environments for your model deployments for both online and batch scoring. To do so, specify the environment field in the deployment YAML configuration.

For more information on how to use environments in deployments, see Deploy and score a machine learning model by using an online endpoint.

Next steps