az ml environment
Note
This reference is part of the ml extension for the Azure CLI (version 2.15.0 or higher). The extension will automatically install the first time you run an az ml environment command. Learn more about extensions.
Manage Azure ML environments.
Azure ML environments define the execution environment for jobs and endpoint deployments, encapsulating the dependencies for training and inference. These environment definitions are built into Docker images.
Commands
Name | Description | Type | Status |
---|---|---|---|
az ml environment archive |
Archive an environment. |
Extension | GA |
az ml environment create |
Create an environment. |
Extension | GA |
az ml environment list |
List environments in a workspace. |
Extension | GA |
az ml environment restore |
Restore an archived environment. |
Extension | GA |
az ml environment share |
Share a specific environment from workspace to registry. |
Extension | GA |
az ml environment show |
Show details for an environment. |
Extension | GA |
az ml environment update |
Update an environment. |
Extension | GA |
az ml environment archive
Archive an environment.
Archiving an environment will hide 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 an environment container or a specific environment version. Archiving an environment container will archive all versions of the environment under that given name. You can restore an archived environment using az ml environment restore
. If the entire environment container is archived, you cannot restore individual versions of the environment - you will need to restore the environment container.
az ml environment archive --name
[--label]
[--registry-name]
[--resource-group]
[--version]
[--workspace-name]
Examples
Archive an environment container (archives all versions of that environment)
az ml environment archive --name my-env --resource-group my-resource-group --workspace-name my-workspace
Archive a specific environment version
az ml environment archive --name my-env --version 1 --resource-group my-resource-group --workspace-name my-workspace
Required Parameters
Name of the environment.
Optional Parameters
Label of the environment.
If provided, the command will target the registry instead of a workspace. Hence resource group and workspace won't be required.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Version of the environment.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
az ml environment create
Create an environment.
Environments can be defined from a Docker image, Dockerfile, or Conda file. Azure ML maintains a set of CPU and GPU Docker images that you can use as base images. For information on these images, see https://github.com/Azure/AzureML-Containers.
The created environment will be tracked in the workspace under the specified name and version.
az ml environment create [--build-context]
[--conda-file]
[--datastore]
[--description]
[--dockerfile-path]
[--file]
[--image]
[--name]
[--no-wait]
[--os-type]
[--registry-name]
[--resource-group]
[--set]
[--tags]
[--version]
[--workspace-name]
Examples
Create an environment from a YAML specification file
az ml environment create --file my_env.yml --resource-group my-resource-group --workspace-name my-workspace
Create an environment from a docker image
az ml environment create --name my-env --version 1 --file my_env.yml --image pytorch/pytorch --resource-group my-resource-group --workspace-name my-workspace
Create an environment from a build context
az ml environment create --name my-env --version 1 --file my_env.yml --build-context envs/context/ --dockerfile-path Dockerfile --resource-group my-resource-group --workspace-name my-workspace
Create an environment from a conda specification
az ml environment create --name my-env --version 1 --file my_env.yml --conda-file conda_dep.yml --image mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04 --resource-group my-resource-group --workspace-name my-workspace
Create an environment in registry from a YAML specification file
az ml environment create --file my_env.yml --registry-name my-registry-name --resource-group my-resource-group
Optional Parameters
Local path to the directory to use as a Docker build context. --build-context/-b and --image/-i are mutually exclusive arguments.
Local path to a conda specification file. --image/-i must also be specified if this argument is used.
The datastore to upload the local artifact to.
Description of the environment.
Relative path to the Dockerfile within the directory specified by --build-context/-b. If omitted, './Dockerfile' is used.
Local path to the YAML file containing the Azure ML environment specification. The YAML reference docs for environment can be found at: https://aka.ms/ml-cli-v2-environment-yaml-reference.
Docker image. --image/-i and --build-context/-b are mutually exclusive arguments.
Name of the environment.
Do not wait for the long-running operation to finish.
Type of operating system. Allowed values: linux, windows. Default: linux.
If provided, the command will target the registry instead of a workspace. Hence resource group and workspace won't be required.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Update an object by specifying a property path and value to set. Example: --set property1.property2=.
Space-separated key-value pairs for the tags of the object.
Version of the environment.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
az ml environment list
List environments in a workspace.
az ml environment list [--archived-only]
[--include-archived]
[--max-results]
[--name]
[--registry-name]
[--resource-group]
[--workspace-name]
Examples
List all the environments in a workspace
az ml environment list --resource-group my-resource-group --workspace-name my-workspace
List all the environment versions for the specified name in a workspace
az ml environment list --name my-env --resource-group my-resource-group --workspace-name my-workspace
List all the environments in a workspace using --query argument to execute a JMESPath query on the results of commands.
az ml environment list --query "[].{Name:name}" --output table --resource-group my-resource-group --workspace-name my-workspace
List all the environments in a registry
az ml environment list --registry-name my-registry-name --resource-group my-resource-group
List all the environment versions for the specified name in a registry
az ml environment list --name my-env --registry-name my-registry-name --resource-group my-resource-group
Optional Parameters
List archived environments only.
List archived environments and active environments.
Max number of results to return.
Name of the environment. If provided, all the environment versions under this name will be returned.
If provided, the command will target the registry instead of a workspace. Hence resource group and workspace won't be required.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
az ml environment restore
Restore an archived environment.
When an archived environment is restored, it will no longer be hidden from list queries (az ml environment list
). If an entire environment container is archived, you can restore that archived container. This will restore all versions of the environment under that given name. You cannot restore only a specific environment version if the entire environment container is archived - you will need to restore the entire container. If only an individual environment version was archived, you can restore that specific version.
az ml environment restore --name
[--label]
[--registry-name]
[--resource-group]
[--version]
[--workspace-name]
Examples
Restore an archived environment container (restores all versions of that environment)
az ml environment restore --name my-env --resource-group my-resource-group --workspace-name my-workspace
Restore a specific archived environment version
az ml environment restore --name my-env --version 1 --resource-group my-resource-group --workspace-name my-workspace
Required Parameters
Name of the environment.
Optional Parameters
Label of the environment.
If provided, the command will target the registry instead of a workspace. Hence resource group and workspace won't be required.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Version of the environment.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
az ml environment share
Share a specific environment from workspace to registry.
Copy an existing environment from a workspace to a registry for cross-workspace reuse.
az ml environment share --name
--registry-name
--share-with-name
--share-with-version
--version
[--resource-group]
[--workspace-name]
Examples
Share an existing environment from workspace to registry
az ml environment share --name my-environment --version my-version --resource-group my-resource-group --workspace-name my-workspace --share-with-name new-name-in-registry --share-with-version new-version-in-registry --registry-name my-registry
Required Parameters
Name of the environment.
Destination registry.
Name of the environment to be created with.
Version of the environment to be created with.
Version of the environment.
Optional Parameters
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
az ml environment show
Show details for an environment.
az ml environment show --name
[--label]
[--registry-name]
[--resource-group]
[--version]
[--workspace-name]
Examples
Show details for an environment with the specified name and version
az ml environment show --name my-env --version 1 --resource-group my-resource-group --workspace-name my-workspace
Show details for an environment in registry with the specified name and version
az ml environment show --name my-env --version 1 --registry-name my-registry-name --resource-group my-resource-group
Required Parameters
Name of the environment.
Optional Parameters
Label of the environment.
If provided, the command will target the registry instead of a workspace. Hence resource group and workspace won't be required.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Version of the environment.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.
az ml environment update
Update an environment.
Only the 'description' and 'tags' properties can be updated.
az ml environment update --name
[--add]
[--force-string]
[--label]
[--registry-name]
[--remove]
[--resource-group]
[--set]
[--version]
[--workspace-name]
Required Parameters
Name of the environment.
Optional Parameters
Add an object to a list of objects by specifying a path and key value pairs. Example: --add property.listProperty <key=value, string or JSON string>
.
When using 'set' or 'add', preserve string literals instead of attempting to convert to JSON.
Label of the environment.
If provided, the command will target the registry instead of a workspace. Hence resource group and workspace won't be required.
Remove a property or an element from a list. Example: --remove property.list <indexToRemove>
OR --remove propertyToRemove
.
Name of resource group. You can configure the default group using az configure --defaults group=<name>
.
Update an object by specifying a property path and value to set. Example: --set property1.property2=<value>
.
Version of the environment.
Name of the Azure ML workspace. You can configure the default workspace using az configure --defaults workspace=<name>
.
Global Parameters
Increase logging verbosity to show all debug logs.
Show this help message and exit.
Only show errors, suppressing warnings.
Output format.
JMESPath query string. See http://jmespath.org/ for more information and examples.
Name or ID of subscription. You can configure the default subscription using az account set -s NAME_OR_ID
.
Increase logging verbosity. Use --debug for full debug logs.