Install extra python packages in your user script so the package installation happens in the script run as part of your code instead of asking Azure ML to treat them as part of a new environment.

If the existing environment is a docker image, use a dockerfile from this docker image so you only need to add one layer to install a few extra packagers.

If you only need a few extra packages on top of an existing environment,.

To avoid unnecessary image building, consider:

This image building process can take some time and delay your job start. The job won't start until the image is built and pushed to the container registry. You will see a image build log file in the logs and monitor the image build progress. If it is a new environment, Azure ML will have a job preparation stage to build a new docker image for the new environment. When the conda dependencies are managed by Azure ML ( user_managed_dependencies=False, by default), Azure ML will check whether the same environment has already been materialized into a docker image in the Azure Container Registry associated with the Azure ML workspace.

environment_variables = 'EXAMPLE_VALUE' Copy Hints and tips #

Environnement conda python version