Class MetadataServiceClient (1.12.1) 

MetadataServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1beta1.services.metadata_service.transports.base.MetadataServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Service for reading and writing metadata entries.

Inheritance

builtins.object > MetadataServiceClient

Properties

transport

Returns the transport used by the client instance.

Returns
Type Description
MetadataServiceTransport The transport used by the client instance.

Methods

MetadataServiceClient

MetadataServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1beta1.services.metadata_service.transports.base.MetadataServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Instantiates the metadata service client.

Parameters
Name Description
credentials Optional[google.auth.credentials.Credentials]

The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.
transport Union[str, MetadataServiceTransport]

The transport to use. If set to None, a transport is chosen automatically.
client_options google.api_core.client_options.ClientOptions

Custom options for the client. It won't take effect if a transport instance is provided. (1) The api_endpoint property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the api_endpoint property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used.
client_info google.api_core.gapic_v1.client_info.ClientInfo

The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you're developing your own client library.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If mutual TLS transport creation failed for any reason.

__exit__

__exit__(type, value, traceback)

Releases underlying transport's resources.

add_context_artifacts_and_executions

add_context_artifacts_and_executions(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.AddContextArtifactsAndExecutionsRequest, dict]] = None, *, context: Optional[str] = None, artifacts: Optional[Sequence[str]] = None, executions: Optional[Sequence[str]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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.

from google.cloud import aiplatform_v1beta1

def sample_add_context_artifacts_and_executions():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.AddContextArtifactsAndExecutionsRequest(
        context="context_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.AddContextArtifactsAndExecutionsRequest, dict]

The request object. Request message for MetadataService.AddContextArtifactsAndExecutions.
context str

Required. The resource name of the Context that the Artifacts and Executions belong to. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context} This corresponds to the context field on the request instance; if request is provided, this should not be set.
artifacts Sequence[str]

The resource names of the Artifacts to attribute to the Context. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact} This corresponds to the artifacts field on the request instance; if request is provided, this should not be set.
executions Sequence[str]

The resource names of the Executions to associate with the Context. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} This corresponds to the executions field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.AddContextArtifactsAndExecutionsResponse Response message for MetadataService.AddContextArtifactsAndExecutions.

add_context_children

add_context_children(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.AddContextChildrenRequest, dict]] = None, *, context: Optional[str] = None, child_contexts: Optional[Sequence[str]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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.

from google.cloud import aiplatform_v1beta1

def sample_add_context_children():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.AddContextChildrenRequest(
        context="context_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.AddContextChildrenRequest, dict]

The request object. Request message for MetadataService.AddContextChildren.
context str

Required. The resource name of the parent Context. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context} This corresponds to the context field on the request instance; if request is provided, this should not be set.
child_contexts Sequence[str]

The resource names of the child Contexts. This corresponds to the child_contexts field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.AddContextChildrenResponse Response message for MetadataService.AddContextChildren.

add_execution_events

add_execution_events(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.AddExecutionEventsRequest, dict]] = None, *, execution: Optional[str] = None, events: Optional[Sequence[google.cloud.aiplatform_v1beta1.types.event.Event]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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.

from google.cloud import aiplatform_v1beta1

def sample_add_execution_events():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.AddExecutionEventsRequest(
        execution="execution_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.AddExecutionEventsRequest, dict]

The request object. Request message for MetadataService.AddExecutionEvents.
execution str

Required. The resource name of the Execution that the Events connect Artifacts with. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} This corresponds to the execution field on the request instance; if request is provided, this should not be set.
events Sequence[google.cloud.aiplatform_v1beta1.types.Event]

The Events to create and add. This corresponds to the events field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.AddExecutionEventsResponse Response message for MetadataService.AddExecutionEvents.

artifact_path

artifact_path(project: str, location: str, metadata_store: str, artifact: str)

Returns a fully-qualified artifact string.

common_billing_account_path

common_billing_account_path(billing_account: str)

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str)

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str)

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str)

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str)

Returns a fully-qualified project string.

context_path

context_path(project: str, location: str, metadata_store: str, context: str)

Returns a fully-qualified context string.

create_artifact

create_artifact(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.CreateArtifactRequest, dict]] = None, *, parent: Optional[str] = None, artifact: Optional[google.cloud.aiplatform_v1beta1.types.artifact.Artifact] = None, artifact_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates an Artifact associated with a MetadataStore.

from google.cloud import aiplatform_v1beta1

def sample_create_artifact():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateArtifactRequest(
        parent="parent_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CreateArtifactRequest, dict]

The request object. Request message for MetadataService.CreateArtifact.
parent str

Required. The resource name of the MetadataStore where the Artifact should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
artifact google.cloud.aiplatform_v1beta1.types.Artifact

Required. The Artifact to create. This corresponds to the artifact field on the request instance; if request is provided, this should not be set.
artifact_id str

The {artifact} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact} If not provided, the Artifact's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /`a-z][0-9]`-/. Must be unique across all Artifacts in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Artifact.) This corresponds to the artifact_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Artifact Instance of a general artifact.

create_context

create_context(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.CreateContextRequest, dict]] = None, *, parent: Optional[str] = None, context: Optional[google.cloud.aiplatform_v1beta1.types.context.Context] = None, context_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a Context associated with a MetadataStore.

from google.cloud import aiplatform_v1beta1

def sample_create_context():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateContextRequest(
        parent="parent_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CreateContextRequest, dict]

The request object. Request message for MetadataService.CreateContext.
parent str

Required. The resource name of the MetadataStore where the Context should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
context google.cloud.aiplatform_v1beta1.types.Context

Required. The Context to create. This corresponds to the context field on the request instance; if request is provided, this should not be set.
context_id str

The {context} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}. If not provided, the Context's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /`a-z][0-9]`-/. Must be unique across all Contexts in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Context.) This corresponds to the context_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Context Instance of a general context.

create_execution

create_execution(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.CreateExecutionRequest, dict]] = None, *, parent: Optional[str] = None, execution: Optional[google.cloud.aiplatform_v1beta1.types.execution.Execution] = None, execution_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates an Execution associated with a MetadataStore.

from google.cloud import aiplatform_v1beta1

def sample_create_execution():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateExecutionRequest(
        parent="parent_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CreateExecutionRequest, dict]

The request object. Request message for MetadataService.CreateExecution.
parent str

Required. The resource name of the MetadataStore where the Execution should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
execution google.cloud.aiplatform_v1beta1.types.Execution

Required. The Execution to create. This corresponds to the execution field on the request instance; if request is provided, this should not be set.
execution_id str

The {execution} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} If not provided, the Execution's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /`a-z][0-9]`-/. Must be unique across all Executions in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Execution.) This corresponds to the execution_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Execution Instance of a general execution.

create_metadata_schema

create_metadata_schema(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.CreateMetadataSchemaRequest, dict]] = None, *, parent: Optional[str] = None, metadata_schema: Optional[google.cloud.aiplatform_v1beta1.types.metadata_schema.MetadataSchema] = None, metadata_schema_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a MetadataSchema.

from google.cloud import aiplatform_v1beta1

def sample_create_metadata_schema():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    metadata_schema = aiplatform_v1beta1.MetadataSchema()
    metadata_schema.schema = "schema_value"

    request = aiplatform_v1beta1.CreateMetadataSchemaRequest(
        parent="parent_value",
        metadata_schema=metadata_schema,
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CreateMetadataSchemaRequest, dict]

The request object. Request message for MetadataService.CreateMetadataSchema.
parent str

Required. The resource name of the MetadataStore where the MetadataSchema should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
metadata_schema google.cloud.aiplatform_v1beta1.types.MetadataSchema

Required. The MetadataSchema to create. This corresponds to the metadata_schema field on the request instance; if request is provided, this should not be set.
metadata_schema_id str

The {metadata_schema} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/metadataSchemas/{metadataschema} If not provided, the MetadataStore's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /`a-z][0-9]`-/. Must be unique across all MetadataSchemas in the parent Location. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting MetadataSchema.) This corresponds to the metadata_schema_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.MetadataSchema Instance of a general MetadataSchema.

create_metadata_store

create_metadata_store(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.CreateMetadataStoreRequest, dict]] = None, *, parent: Optional[str] = None, metadata_store: Optional[google.cloud.aiplatform_v1beta1.types.metadata_store.MetadataStore] = None, metadata_store_id: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Initializes a MetadataStore, including allocation of resources.

from google.cloud import aiplatform_v1beta1

def sample_create_metadata_store():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.CreateMetadataStoreRequest(
        parent="parent_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.CreateMetadataStoreRequest, dict]

The request object. Request message for MetadataService.CreateMetadataStore.
parent str

Required. The resource name of the Location where the MetadataStore should be created. Format: projects/{project}/locations/{location}/ This corresponds to the parent field on the request instance; if request is provided, this should not be set.
metadata_store google.cloud.aiplatform_v1beta1.types.MetadataStore

Required. The MetadataStore to create. This corresponds to the metadata_store field on the request instance; if request is provided, this should not be set.
metadata_store_id str

The {metadatastore} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore} If not provided, the MetadataStore's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /`a-z][0-9]`-/. Must be unique across all MetadataStores in the parent Location. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting MetadataStore.) This corresponds to the metadata_store_id field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be MetadataStore Instance of a metadata store. Contains a set of metadata that can be queried.

delete_artifact

delete_artifact(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.DeleteArtifactRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes an Artifact.

from google.cloud import aiplatform_v1beta1

def sample_delete_artifact():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteArtifactRequest(
        name="name_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.DeleteArtifactRequest, dict]

The request object. Request message for MetadataService.DeleteArtifact.
name str

Required. The resource name of the Artifact to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_context

delete_context(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.DeleteContextRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a stored Context.

from google.cloud import aiplatform_v1beta1

def sample_delete_context():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteContextRequest(
        name="name_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.DeleteContextRequest, dict]

The request object. Request message for MetadataService.DeleteContext.
name str

Required. The resource name of the Context to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_execution

delete_execution(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.DeleteExecutionRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes an Execution.

from google.cloud import aiplatform_v1beta1

def sample_delete_execution():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteExecutionRequest(
        name="name_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.DeleteExecutionRequest, dict]

The request object. Request message for MetadataService.DeleteExecution.
name str

Required. The resource name of the Execution to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_metadata_store

delete_metadata_store(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.DeleteMetadataStoreRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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

from google.cloud import aiplatform_v1beta1

def sample_delete_metadata_store():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteMetadataStoreRequest(
        name="name_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.DeleteMetadataStoreRequest, dict]

The request object. Request message for MetadataService.DeleteMetadataStore.
name str

Required. The resource name of the MetadataStore to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

execution_path

execution_path(project: str, location: str, metadata_store: str, execution: str)

Returns a fully-qualified execution string.

from_service_account_file

from_service_account_file(filename: str, *args, **kwargs)

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

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
MetadataServiceClient The constructed client.

from_service_account_info

from_service_account_info(info: dict, *args, **kwargs)

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

Parameter
Name Description
info dict

The service account private key info.
Returns
Type Description
MetadataServiceClient The constructed client.

from_service_account_json

from_service_account_json(filename: str, *args, **kwargs)

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

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
MetadataServiceClient The constructed client.

get_artifact

get_artifact(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.GetArtifactRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Retrieves a specific Artifact.

from google.cloud import aiplatform_v1beta1

def sample_get_artifact():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetArtifactRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.GetArtifactRequest, dict]

The request object. Request message for MetadataService.GetArtifact.
name str

Required. The resource name of the Artifact to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Artifact Instance of a general artifact.

get_context

get_context(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.GetContextRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Retrieves a specific Context.

from google.cloud import aiplatform_v1beta1

def sample_get_context():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetContextRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.GetContextRequest, dict]

The request object. Request message for MetadataService.GetContext.
name str

Required. The resource name of the Context to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Context Instance of a general context.

get_execution

get_execution(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.GetExecutionRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Retrieves a specific Execution.

from google.cloud import aiplatform_v1beta1

def sample_get_execution():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetExecutionRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.GetExecutionRequest, dict]

The request object. Request message for MetadataService.GetExecution.
name str

Required. The resource name of the Execution to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Execution Instance of a general execution.

get_metadata_schema

get_metadata_schema(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.GetMetadataSchemaRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Retrieves a specific MetadataSchema.

from google.cloud import aiplatform_v1beta1

def sample_get_metadata_schema():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetMetadataSchemaRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.GetMetadataSchemaRequest, dict]

The request object. Request message for MetadataService.GetMetadataSchema.
name str

Required. The resource name of the MetadataSchema to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/metadataSchemas/{metadataschema} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.MetadataSchema Instance of a general MetadataSchema.

get_metadata_store

get_metadata_store(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.GetMetadataStoreRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Retrieves a specific MetadataStore.

from google.cloud import aiplatform_v1beta1

def sample_get_metadata_store():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetMetadataStoreRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.GetMetadataStoreRequest, dict]

The request object. Request message for MetadataService.GetMetadataStore.
name str

Required. The resource name of the MetadataStore to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.MetadataStore Instance of a metadata store. Contains a set of metadata that can be queried.

get_mtls_endpoint_and_cert_source

get_mtls_endpoint_and_cert_source(
    client_options: Optional[google.api_core.client_options.ClientOptions] = None,
)

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 variabel 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.

Parameter
Name Description
client_options google.api_core.client_options.ClientOptions

Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If any errors happen.
Returns
Type Description
Tuple[str, Callable[[], Tuple[bytes, bytes]]] returns the API endpoint and the client cert source to use.

list_artifacts

list_artifacts(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.ListArtifactsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists Artifacts in the MetadataStore.

from google.cloud import aiplatform_v1beta1

def sample_list_artifacts():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListArtifactsRequest(
        parent="parent_value",
    )

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.ListArtifactsRequest, dict]

The request object. Request message for MetadataService.ListArtifacts.
parent str

Required. The MetadataStore whose Artifacts should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.services.metadata_service.pagers.ListArtifactsPager Response message for MetadataService.ListArtifacts. Iterating over this object will yield results and resolve additional pages automatically.

list_contexts

list_contexts(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.ListContextsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists Contexts on the MetadataStore.

from google.cloud import aiplatform_v1beta1

def sample_list_contexts():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListContextsRequest(
        parent="parent_value",
    )

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.ListContextsRequest, dict]

The request object. Request message for MetadataService.ListContexts
parent str

Required. The MetadataStore whose Contexts should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.services.metadata_service.pagers.ListContextsPager Response message for MetadataService.ListContexts. Iterating over this object will yield results and resolve additional pages automatically.

list_executions

list_executions(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.ListExecutionsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists Executions in the MetadataStore.

from google.cloud import aiplatform_v1beta1

def sample_list_executions():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListExecutionsRequest(
        parent="parent_value",
    )

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.ListExecutionsRequest, dict]

The request object. Request message for MetadataService.ListExecutions.
parent str

Required. The MetadataStore whose Executions should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.services.metadata_service.pagers.ListExecutionsPager Response message for MetadataService.ListExecutions. Iterating over this object will yield results and resolve additional pages automatically.

list_metadata_schemas

list_metadata_schemas(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.ListMetadataSchemasRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists MetadataSchemas.

from google.cloud import aiplatform_v1beta1

def sample_list_metadata_schemas():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListMetadataSchemasRequest(
        parent="parent_value",
    )

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.ListMetadataSchemasRequest, dict]

The request object. Request message for MetadataService.ListMetadataSchemas.
parent str

Required. The MetadataStore whose MetadataSchemas should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.services.metadata_service.pagers.ListMetadataSchemasPager Response message for MetadataService.ListMetadataSchemas. Iterating over this object will yield results and resolve additional pages automatically.

list_metadata_stores

list_metadata_stores(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.ListMetadataStoresRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists MetadataStores for a Location.

from google.cloud import aiplatform_v1beta1

def sample_list_metadata_stores():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListMetadataStoresRequest(
        parent="parent_value",
    )

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.ListMetadataStoresRequest, dict]

The request object. Request message for MetadataService.ListMetadataStores.
parent str

Required. The Location whose MetadataStores should be listed. Format: projects/{project}/locations/{location} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.services.metadata_service.pagers.ListMetadataStoresPager Response message for MetadataService.ListMetadataStores. Iterating over this object will yield results and resolve additional pages automatically.

metadata_schema_path

metadata_schema_path(
    project: str, location: str, metadata_store: str, metadata_schema: str
)

Returns a fully-qualified metadata_schema string.

metadata_store_path

metadata_store_path(project: str, location: str, metadata_store: str)

Returns a fully-qualified metadata_store string.

parse_artifact_path

parse_artifact_path(path: str)

Parses a artifact path into its component segments.

parse_common_billing_account_path

parse_common_billing_account_path(path: str)

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str)

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str)

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str)

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str)

Parse a project path into its component segments.

parse_context_path

parse_context_path(path: str)

Parses a context path into its component segments.

parse_execution_path

parse_execution_path(path: str)

Parses a execution path into its component segments.

parse_metadata_schema_path

parse_metadata_schema_path(path: str)

Parses a metadata_schema path into its component segments.

parse_metadata_store_path

parse_metadata_store_path(path: str)

Parses a metadata_store path into its component segments.

purge_artifacts

purge_artifacts(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.PurgeArtifactsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Purges Artifacts.

from google.cloud import aiplatform_v1beta1

def sample_purge_artifacts():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.PurgeArtifactsRequest(
        parent="parent_value",
        filter="filter_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.PurgeArtifactsRequest, dict]

The request object. Request message for MetadataService.PurgeArtifacts.
parent str

Required. The metadata store to purge Artifacts from. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be PurgeArtifactsResponse Response message for MetadataService.PurgeArtifacts.

purge_contexts

purge_contexts(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.PurgeContextsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Purges Contexts.

from google.cloud import aiplatform_v1beta1

def sample_purge_contexts():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.PurgeContextsRequest(
        parent="parent_value",
        filter="filter_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.PurgeContextsRequest, dict]

The request object. Request message for MetadataService.PurgeContexts.
parent str

Required. The metadata store to purge Contexts from. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be PurgeContextsResponse Response message for MetadataService.PurgeContexts.

purge_executions

purge_executions(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.PurgeExecutionsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Purges Executions.

from google.cloud import aiplatform_v1beta1

def sample_purge_executions():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.PurgeExecutionsRequest(
        parent="parent_value",
        filter="filter_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.PurgeExecutionsRequest, dict]

The request object. Request message for MetadataService.PurgeExecutions.
parent str

Required. The metadata store to purge Executions from. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be PurgeExecutionsResponse Response message for MetadataService.PurgeExecutions.

query_artifact_lineage_subgraph

query_artifact_lineage_subgraph(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.QueryArtifactLineageSubgraphRequest, dict]] = None, *, artifact: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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

from google.cloud import aiplatform_v1beta1

def sample_query_artifact_lineage_subgraph():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.QueryArtifactLineageSubgraphRequest(
        artifact="artifact_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.QueryArtifactLineageSubgraphRequest, dict]

The request object. Request message for MetadataService.QueryArtifactLineageSubgraph.
artifact str

Required. The resource name of the Artifact whose Lineage needs to be retrieved as a LineageSubgraph. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact} The request may error with FAILED_PRECONDITION if the number of Artifacts, the number of Executions, or the number of Events that would be returned for the Context exceeds 1000. This corresponds to the artifact field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.LineageSubgraph A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

query_context_lineage_subgraph

query_context_lineage_subgraph(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.QueryContextLineageSubgraphRequest, dict]] = None, *, context: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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

from google.cloud import aiplatform_v1beta1

def sample_query_context_lineage_subgraph():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.QueryContextLineageSubgraphRequest(
        context="context_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.QueryContextLineageSubgraphRequest, dict]

The request object. Request message for MetadataService.QueryContextLineageSubgraph.
context str

Required. The resource name of the Context whose Artifacts and Executions should be retrieved as a LineageSubgraph. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context} The request may error with FAILED_PRECONDITION if the number of Artifacts, the number of Executions, or the number of Events that would be returned for the Context exceeds 1000. This corresponds to the context field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.LineageSubgraph A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

query_execution_inputs_and_outputs

query_execution_inputs_and_outputs(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.QueryExecutionInputsAndOutputsRequest, dict]] = None, *, execution: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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

from google.cloud import aiplatform_v1beta1

def sample_query_execution_inputs_and_outputs():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.QueryExecutionInputsAndOutputsRequest(
        execution="execution_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.QueryExecutionInputsAndOutputsRequest, dict]

The request object. Request message for MetadataService.QueryExecutionInputsAndOutputs.
execution str

Required. The resource name of the Execution whose input and output Artifacts should be retrieved as a LineageSubgraph. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} This corresponds to the execution field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.LineageSubgraph A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

update_artifact

update_artifact(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.UpdateArtifactRequest, dict]] = None, *, artifact: Optional[google.cloud.aiplatform_v1beta1.types.artifact.Artifact] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a stored Artifact.

from google.cloud import aiplatform_v1beta1

def sample_update_artifact():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.UpdateArtifactRequest(
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.UpdateArtifactRequest, dict]

The request object. Request message for MetadataService.UpdateArtifact.
artifact google.cloud.aiplatform_v1beta1.types.Artifact

Required. The Artifact containing updates. The Artifact's Artifact.name field is used to identify the Artifact to be updated. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact} This corresponds to the artifact field on the request instance; if request is provided, this should not be set.
update_mask google.protobuf.field_mask_pb2.FieldMask

Required. A FieldMask indicating which fields should be updated. Functionality of this field is not yet supported. This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Artifact Instance of a general artifact.

update_context

update_context(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.UpdateContextRequest, dict]] = None, *, context: Optional[google.cloud.aiplatform_v1beta1.types.context.Context] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a stored Context.

from google.cloud import aiplatform_v1beta1

def sample_update_context():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.UpdateContextRequest(
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.UpdateContextRequest, dict]

The request object. Request message for MetadataService.UpdateContext.
context google.cloud.aiplatform_v1beta1.types.Context

Required. The Context containing updates. The Context's Context.name field is used to identify the Context to be updated. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context} This corresponds to the context field on the request instance; if request is provided, this should not be set.
update_mask google.protobuf.field_mask_pb2.FieldMask

Required. A FieldMask indicating which fields should be updated. Functionality of this field is not yet supported. This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Context Instance of a general context.

update_execution

update_execution(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.metadata_service.UpdateExecutionRequest, dict]] = None, *, execution: Optional[google.cloud.aiplatform_v1beta1.types.execution.Execution] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a stored Execution.

from google.cloud import aiplatform_v1beta1

def sample_update_execution():
    # Create a client
    client = aiplatform_v1beta1.MetadataServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.UpdateExecutionRequest(
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1beta1.types.UpdateExecutionRequest, dict]

The request object. Request message for MetadataService.UpdateExecution.
execution google.cloud.aiplatform_v1beta1.types.Execution

Required. The Execution containing updates. The Execution's Execution.name field is used to identify the Execution to be updated. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} This corresponds to the execution field on the request instance; if request is provided, this should not be set.
update_mask google.protobuf.field_mask_pb2.FieldMask

Required. A FieldMask indicating which fields should be updated. Functionality of this field is not yet supported. This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry google.api_core.retry.Retry

Designation of what errors, if any, should be retried.
timeout float

The timeout for this request.
metadata Sequence[Tuple[str, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1beta1.types.Execution Instance of a general execution.