Class MetadataServiceAsyncClient (1.70.0)

Service for reading and writing metadata entries.

Return the API endpoint used by the client instance.

Returns the transport used by the client instance.

Return the universe domain used by the client instance.

Instantiates the metadata service async client.

Adds a set of Artifacts and Executions to a Context. If any of the Artifacts or Executions have already been added to a Context, they are simply skipped.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_add_context_artifacts_and_executions():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.AddContextArtifactsAndExecutionsRequest(
        context="context_value",
    )

    # Make the request
    response = await client.add_context_artifacts_and_executions(request=request)

    # Handle the response
    print(response)

Adds a set of Contexts as children to a parent Context. If any of the child Contexts have already been added to the parent Context, they are simply skipped. If this call would create a cycle or cause any Context to have more than 10 parents, the request will fail with an INVALID_ARGUMENT error.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_add_context_children():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.AddContextChildrenRequest(
        context="context_value",
    )

    # Make the request
    response = await client.add_context_children(request=request)

    # Handle the response
    print(response)

Adds Events to the specified Execution. An Event indicates whether an Artifact was used as an input or output for an Execution. If an Event already exists between the Execution and the Artifact, the Event is skipped.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_add_execution_events():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.AddExecutionEventsRequest(
        execution="execution_value",
    )

    # Make the request
    response = await client.add_execution_events(request=request)

    # Handle the response
    print(response)

Returns a fully-qualified artifact string.

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Returns a fully-qualified billing_account string.

Returns a fully-qualified folder string.

Returns a fully-qualified location string.

Returns a fully-qualified organization string.

Returns a fully-qualified project string.

Returns a fully-qualified context string.

Creates an Artifact associated with a MetadataStore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_artifact():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateArtifactRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.create_artifact(request=request)

    # Handle the response
    print(response)

Creates a Context associated with a MetadataStore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_context():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateContextRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.create_context(request=request)

    # Handle the response
    print(response)

Creates an Execution associated with a MetadataStore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_execution():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateExecutionRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.create_execution(request=request)

    # Handle the response
    print(response)

Creates a MetadataSchema.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_metadata_schema():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    metadata_schema = aiplatform_v1.MetadataSchema()
    metadata_schema.schema = "schema_value"

    request = aiplatform_v1.CreateMetadataSchemaRequest(
        parent="parent_value",
        metadata_schema=metadata_schema,
    )

    # Make the request
    response = await client.create_metadata_schema(request=request)

    # Handle the response
    print(response)

Initializes a MetadataStore, including allocation of resources.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_metadata_store():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateMetadataStoreRequest(
        parent="parent_value",
    )

    # Make the request
    operation = client.create_metadata_store(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes an Artifact.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_artifact():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteArtifactRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_artifact(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a stored Context.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_context():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteContextRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_context(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes an Execution.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_execution():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteExecutionRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_execution(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a single MetadataStore and all its child resources (Artifacts, Executions, and Contexts).

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_metadata_store():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteMetadataStoreRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_metadata_store(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Returns a fully-qualified execution string.

Creates an instance of this client using the provided credentials file.

Creates an instance of this client using the provided credentials info.

Creates an instance of this client using the provided credentials file.

Retrieves a specific Artifact.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_artifact():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetArtifactRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_artifact(request=request)

    # Handle the response
    print(response)

Retrieves a specific Context.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_context():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetContextRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_context(request=request)

    # Handle the response
    print(response)

Retrieves a specific Execution.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_execution():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetExecutionRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_execution(request=request)

    # Handle the response
    print(response)

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Gets information about a location.

Retrieves a specific MetadataSchema.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_metadata_schema():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetMetadataSchemaRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_metadata_schema(request=request)

    # Handle the response
    print(response)

Retrieves a specific MetadataStore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_metadata_store():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetMetadataStoreRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_metadata_store(request=request)

    # Handle the response
    print(response)

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variable is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Gets the latest state of a long-running operation.

Returns an appropriate transport class.

Lists Artifacts in the MetadataStore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_artifacts():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListArtifactsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_artifacts(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists Contexts on the MetadataStore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_contexts():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListContextsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_contexts(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists Executions in the MetadataStore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_executions():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListExecutionsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_executions(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists information about the supported locations for this service.

Lists MetadataSchemas.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_metadata_schemas():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListMetadataSchemasRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_metadata_schemas(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists MetadataStores for a Location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_metadata_stores():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListMetadataStoresRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_metadata_stores(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists operations that match the specified filter in the request.

Returns a fully-qualified metadata_schema string.

Returns a fully-qualified metadata_store string.

Parses a artifact path into its component segments.

Parse a billing_account path into its component segments.

Parse a folder path into its component segments.

Parse a location path into its component segments.

Parse a organization path into its component segments.

Parse a project path into its component segments.

Parses a context path into its component segments.

Parses a execution path into its component segments.

Parses a metadata_schema path into its component segments.

Parses a metadata_store path into its component segments.

Purges Artifacts.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_purge_artifacts():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.PurgeArtifactsRequest(
        parent="parent_value",
        filter="filter_value",
    )

    # Make the request
    operation = client.purge_artifacts(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Purges Contexts.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_purge_contexts():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.PurgeContextsRequest(
        parent="parent_value",
        filter="filter_value",
    )

    # Make the request
    operation = client.purge_contexts(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Purges Executions.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_purge_executions():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.PurgeExecutionsRequest(
        parent="parent_value",
        filter="filter_value",
    )

    # Make the request
    operation = client.purge_executions(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Retrieves lineage of an Artifact represented through Artifacts and Executions connected by Event edges and returned as a LineageSubgraph.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_query_artifact_lineage_subgraph():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.QueryArtifactLineageSubgraphRequest(
        artifact="artifact_value",
    )

    # Make the request
    response = await client.query_artifact_lineage_subgraph(request=request)

    # Handle the response
    print(response)

Retrieves Artifacts and Executions within the specified Context, connected by Event edges and returned as a LineageSubgraph.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_query_context_lineage_subgraph():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.QueryContextLineageSubgraphRequest(
        context="context_value",
    )

    # Make the request
    response = await client.query_context_lineage_subgraph(request=request)

    # Handle the response
    print(response)

Obtains the set of input and output Artifacts for this Execution, in the form of LineageSubgraph that also contains the Execution and connecting Events.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_query_execution_inputs_and_outputs():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.QueryExecutionInputsAndOutputsRequest(
        execution="execution_value",
    )

    # Make the request
    response = await client.query_execution_inputs_and_outputs(request=request)

    # Handle the response
    print(response)

Remove a set of children contexts from a parent Context. If any of the child Contexts were NOT added to the parent Context, they are simply skipped.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_remove_context_children():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.RemoveContextChildrenRequest(
        context="context_value",
    )

    # Make the request
    response = await client.remove_context_children(request=request)

    # Handle the response
    print(response)

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Tests the specified IAM permissions against the IAM access control policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Updates a stored Artifact.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_artifact():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateArtifactRequest(
    )

    # Make the request
    response = await client.update_artifact(request=request)

    # Handle the response
    print(response)

Updates a stored Context.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_context():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateContextRequest(
    )

    # Make the request
    response = await client.update_context(request=request)

    # Handle the response
    print(response)

Updates a stored Execution.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_execution():
    # Create a client
    client = aiplatform_v1.MetadataServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateExecutionRequest(
    )

    # Make the request
    response = await client.update_execution(request=request)

    # Handle the response
    print(response)

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.