Manage software environments in Azure Machine Learning studio

In this article, learn how to create and manage Azure Machine Learning environments in the Azure Machine Learning studio. Use the environments to track and reproduce your projects' software dependencies as they evolve.

The examples in this article show how to:

  • Browse curated environments.
  • Create an environment and specify package dependencies.
  • Edit an existing environment specification and its properties.
  • Rebuild an environment and view image build logs.

For a high-level overview of how environments work in Azure Machine Learning, see What are ML environments? For information, see How to set up a development environment for Azure Machine Learning.

Prerequisites

Browse curated environments

Curated environments contain collections of Python packages and are available in your workspace by default. These environments are backed by cached Docker images, which reduce the job preparation cost and support training and inferencing scenarios.

Click on an environment to see detailed information about its contents. For more information, see Azure Machine Learning curated environments.

Create an environment

To create an environment:

  1. Open your workspace in Azure Machine Learning studio.
  2. On the left side, select Environments.
  3. Select the Custom environments tab.
  4. Select the Create button.

Create an environment by selecting one of the following options:

  • Create a new docker context
  • Start from an existing environment
  • Upload existing docker context
  • Use existing docker image with optional conda file

Environment creation wizard

You can customize the configuration file, add tags and descriptions, and review the properties before creating the entity.

If a new environment is given the same name as an existing environment in the workspace, a new version of the existing one will be created.

View and edit environment details

Once an environment has been created, view its details by clicking on the name. Use the dropdown menu to select different versions of the environment. Here you can view metadata and the contents of the environment through its various dependencies.

Click on the pencil icons to edit tags, descriptions, configuration files under the Context tab.

Keep in mind that any changes to the Docker or Conda sections will create a new version of the environment.

Environments details page

View logs

Click on the Build log tab within the details page to view the logs of an environment version and the environment log analysis. Environment log analysis (Preview) is a feature that provides insight and relevant troubleshooting documentation to explain environment definition issues or image build failures. If you have feedback on the feature or the documentation, file it at https://aka.ms/azureml/environment/log-analysis-feedback

  • Build log contains the bare output from an Azure Container Registry (ACR) task or an Image Build Compute job.
  • Image build analysis is an analysis of the build log used to see the cause of the image build failure.
  • Environment definition analysis provides information about the environment definition if it goes against best practices for reproducibility, supportability, or security.

For an overview of common build failures, see https://aka.ms/azureml/environment/troubleshooting-guide

Rebuild an environment

In the details page, click on the rebuild button to rebuild the environment. Any unpinned package versions in your configuration files may be updated to the most recent version with this action.