EnvironmentOperations Class

EnvironmentOperations.

You should not instantiate this class directly. Instead, you should create an MLClient instance that instantiates it for you and attaches it as an attribute.

Inheritance

azure.ai.ml._scope_dependent_operations._ScopeDependentOperations

EnvironmentOperations

Constructor

EnvironmentOperations(operation_scope: OperationScope, operation_config: OperationConfig, service_client: AzureMachineLearningWorkspaces | AzureMachineLearningWorkspaces, all_operations: OperationsContainer, **kwargs: Any)

Parameters

Name	Description
operation_scope Required	<xref:azure.ai.ml._scope_dependent_operations.OperationScope> Scope variables for the operations classes of an MLClient object.
operation_config Required	<xref:azure.ai.ml._scope_dependent_operations.OperationConfig> Common configuration for operations classes of an MLClient object.
service_client Required	Union[ <xref:azure.ai.ml._restclient.v2023_04_01_preview._azure_machine_learning_workspaces.AzureMachineLearningWorkspaces>, <xref:azure.ai.ml._restclient.v2021_10_01_dataplanepreview._azure_machine_learning_workspaces. AzureMachineLearningWorkspaces>] Service client to allow end users to operate on Azure Machine Learning Workspace resources (ServiceClient042023Preview or ServiceClient102021Dataplane).
all_operations Required	<xref:azure.ai.ml._scope_dependent_operations.OperationsContainer> All operations classes of an MLClient object.

Methods

archive	Archive an environment or an environment version.
create_or_update	Returns created or updated environment asset.
get	Returns the specified environment asset.
list	List all environment assets in workspace.
restore	Restore an archived environment version.
share	Note This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information. Share a environment asset from workspace to registry.

archive

Archive an environment or an environment version.

archive(name: str, version: str | None = None, label: str | None = None, **kwargs: Any) -> None

Parameters

Name	Description
name Required	str Name of the environment.
version Required	str Version of the environment.
label Required	str Label of the environment. (mutually exclusive with version)

Examples

Archive example.

   ml_client.environments.archive("create-environment", "2.0")

create_or_update

Returns created or updated environment asset.

create_or_update(environment: Environment) -> Environment

Parameters

Name	Description
environment Required	<xref:azure.ai.ml.entities._assets.Environment> Environment object

Returns

Type	Description
Environment	Created or updated Environment object

Exceptions

Type	Description
ValidationException	Raised if Environment cannot be successfully validated. Details will be provided in the error message.
EmptyDirectoryError	Raised if local path provided points to an empty directory.

Examples

Create environment.

   from azure.ai.ml.entities import BuildContext, Environment

   env_docker_context = Environment(
       build=BuildContext(
           path="./sdk/ml/azure-ai-ml/tests/test_configs/environment/environment_files",
           dockerfile_path="DockerfileNonDefault",
       ),
       name="create-environment",
       version="2.0",
       description="Environment created from a Docker context.",
   )
   ml_client.environments.create_or_update(env_docker_context)

get

Returns the specified environment asset.

get(name: str, version: str | None = None, label: str | None = None) -> Environment

Parameters

Name	Description
name Required	str Name of the environment.
version Required	str Version of the environment.
label Required	str Label of the environment. (mutually exclusive with version)

Returns

Type	Description
Environment	Environment object

Exceptions

Type	Description
ValidationException	Raised if Environment cannot be successfully validated. Details will be provided in the error message.

Examples

Get example.

   ml_client.environments.get("create-environment", "2.0")

list

List all environment assets in workspace.

list(name: str | None = None, *, list_view_type: ListViewType = ListViewType.ACTIVE_ONLY) -> Iterable[Environment]

Parameters

Name	Description
name Required	Optional[str] Name of the environment.

Keyword-Only Parameters

Name	Description
list_view_type	View type for including/excluding (for example) archived environments. Default: ACTIVE_ONLY.

Returns

Type	Description
ItemPaged[Environment]	An iterator like instance of Environment objects.

Examples

List example.

   ml_client.environments.list()

restore

Restore an archived environment version.

restore(name: str, version: str | None = None, label: str | None = None, **kwargs: Any) -> None

Parameters

Name	Description
name Required	str Name of the environment.
version Required	str Version of the environment.
label Required	str Label of the environment. (mutually exclusive with version)

Examples

Restore example.

   ml_client.environments.restore("create-environment", "2.0")

share

Note

This is an experimental method, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Share a environment asset from workspace to registry.

share(name: str, version: str, *, share_with_name: str, share_with_version: str, registry_name: str) -> Environment

Parameters

Name	Description
name Required	str Name of environment asset.
version Required	str Version of environment asset.

Keyword-Only Parameters

Name	Description
share_with_name	str Name of environment asset to share with.
share_with_version	str Version of environment asset to share with.
registry_name	str Name of the destination registry.

Returns

Type	Description
Environment	Environment asset object.