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MetadataServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.metadata_service.transports.base.MetadataServiceTransport] = 'grpc_asyncio', 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 > MetadataServiceAsyncClientProperties
transport
Returns the transport used by the client instance.
Type | Description |
MetadataServiceTransport | The transport used by the client instance. |
Methods
MetadataServiceAsyncClient
MetadataServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.metadata_service.transports.base.MetadataServiceTransport] = 'grpc_asyncio', 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.
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 |
ClientOptions
Custom options for the client. It won't take effect if a |
Type | Description |
google.auth.exceptions.MutualTlsChannelError | If mutual TLS transport creation failed for any reason. |
add_context_artifacts_and_executions
add_context_artifacts_and_executions(request: Optional[Union[google.cloud.aiplatform_v1.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_v1
def sample_add_context_artifacts_and_executions():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.AddContextArtifactsAndExecutionsRequest(
context="context_value",
)
# Make the request
response = client.add_context_artifacts_and_executions(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
artifacts |
:class:`Sequence[str]`
The resource names of the Artifacts to attribute to the Context. Format: |
executions |
:class:`Sequence[str]`
The resource names of the Executions to associate with the Context. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.AddContextArtifactsAndExecutionsResponse | Response message for MetadataService.AddContextArtifactsAndExecutions. |
add_context_children
add_context_children(request: Optional[Union[google.cloud.aiplatform_v1.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_v1
def sample_add_context_children():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.AddContextChildrenRequest(
context="context_value",
)
# Make the request
response = client.add_context_children(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.AddContextChildrenRequest, dict]
The request object. Request message for MetadataService.AddContextChildren. |
context |
`str`
Required. The resource name of the parent Context. Format: |
child_contexts |
:class:`Sequence[str]`
The resource names of the child Contexts. This corresponds to the |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.AddContextChildrenResponse | Response message for MetadataService.AddContextChildren. |
add_execution_events
add_execution_events(request: Optional[Union[google.cloud.aiplatform_v1.types.metadata_service.AddExecutionEventsRequest, dict]] = None, *, execution: Optional[str] = None, events: Optional[Sequence[google.cloud.aiplatform_v1.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_v1
def sample_add_execution_events():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.AddExecutionEventsRequest(
execution="execution_value",
)
# Make the request
response = client.add_execution_events(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
events |
:class:`Sequence[google.cloud.aiplatform_v1.types.Event]`
The Events to create and add. This corresponds to the |
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. |
Type | Description |
google.cloud.aiplatform_v1.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_v1.types.metadata_service.CreateArtifactRequest, dict]] = None, *, parent: Optional[str] = None, artifact: Optional[google.cloud.aiplatform_v1.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_v1
def sample_create_artifact():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.CreateArtifactRequest(
parent="parent_value",
)
# Make the request
response = client.create_artifact(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
artifact |
Artifact
Required. The Artifact to create. This corresponds to the |
artifact_id |
`str`
The {artifact} portion of the resource name with the format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Artifact | Instance of a general artifact. |
create_context
create_context(request: Optional[Union[google.cloud.aiplatform_v1.types.metadata_service.CreateContextRequest, dict]] = None, *, parent: Optional[str] = None, context: Optional[google.cloud.aiplatform_v1.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_v1
def sample_create_context():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.CreateContextRequest(
parent="parent_value",
)
# Make the request
response = client.create_context(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
context |
Context
Required. The Context to create. This corresponds to the |
context_id |
`str`
The {context} portion of the resource name with the format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Context | Instance of a general context. |
create_execution
create_execution(request: Optional[Union[google.cloud.aiplatform_v1.types.metadata_service.CreateExecutionRequest, dict]] = None, *, parent: Optional[str] = None, execution: Optional[google.cloud.aiplatform_v1.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_v1
def sample_create_execution():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.CreateExecutionRequest(
parent="parent_value",
)
# Make the request
response = client.create_execution(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
execution |
Execution
Required. The Execution to create. This corresponds to the |
execution_id |
`str`
The {execution} portion of the resource name with the format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Execution | Instance of a general execution. |
create_metadata_schema
create_metadata_schema(request: Optional[Union[google.cloud.aiplatform_v1.types.metadata_service.CreateMetadataSchemaRequest, dict]] = None, *, parent: Optional[str] = None, metadata_schema: Optional[google.cloud.aiplatform_v1.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_v1
def sample_create_metadata_schema():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = client.create_metadata_schema(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
metadata_schema |
MetadataSchema
Required. The MetadataSchema to create. This corresponds to the |
metadata_schema_id |
`str`
The {metadata_schema} portion of the resource name with the format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.MetadataSchema | Instance of a general MetadataSchema. |
create_metadata_store
create_metadata_store(request: Optional[Union[google.cloud.aiplatform_v1.types.metadata_service.CreateMetadataStoreRequest, dict]] = None, *, parent: Optional[str] = None, metadata_store: Optional[google.cloud.aiplatform_v1.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_v1
def sample_create_metadata_store():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
metadata_store |
MetadataStore
Required. The MetadataStore to create. This corresponds to the |
metadata_store_id |
`str`
The {metadatastore} portion of the resource name with the format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | 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_v1.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_v1
def sample_delete_artifact():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.DeleteArtifactRequest, dict]
The request object. Request message for MetadataService.DeleteArtifact. |
name |
`str`
Required. The resource name of the Artifact to delete. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | 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_v1.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_v1
def sample_delete_context():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.DeleteContextRequest, dict]
The request object. Request message for MetadataService.DeleteContext. |
name |
`str`
Required. The resource name of the Context to delete. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | 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_v1.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_v1
def sample_delete_execution():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.DeleteExecutionRequest, dict]
The request object. Request message for MetadataService.DeleteExecution. |
name |
`str`
Required. The resource name of the Execution to delete. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | 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_v1.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_v1
def sample_delete_metadata_store():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.DeleteMetadataStoreRequest, dict]
The request object. Request message for MetadataService.DeleteMetadataStore. |
name |
`str`
Required. The resource name of the MetadataStore to delete. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | 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.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
MetadataServiceAsyncClient | 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.
Name | Description |
info |
dict
The service account private key info. |
Type | Description |
MetadataServiceAsyncClient | 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.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
MetadataServiceAsyncClient | The constructed client. |
get_artifact
get_artifact(request: Optional[Union[google.cloud.aiplatform_v1.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_v1
def sample_get_artifact():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.GetArtifactRequest(
name="name_value",
)
# Make the request
response = client.get_artifact(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.GetArtifactRequest, dict]
The request object. Request message for MetadataService.GetArtifact. |
name |
`str`
Required. The resource name of the Artifact to retrieve. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Artifact | Instance of a general artifact. |
get_context
get_context(request: Optional[Union[google.cloud.aiplatform_v1.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_v1
def sample_get_context():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.GetContextRequest(
name="name_value",
)
# Make the request
response = client.get_context(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.GetContextRequest, dict]
The request object. Request message for MetadataService.GetContext. |
name |
`str`
Required. The resource name of the Context to retrieve. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Context | Instance of a general context. |
get_execution
get_execution(request: Optional[Union[google.cloud.aiplatform_v1.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_v1
def sample_get_execution():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.GetExecutionRequest(
name="name_value",
)
# Make the request
response = client.get_execution(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.GetExecutionRequest, dict]
The request object. Request message for MetadataService.GetExecution. |
name |
`str`
Required. The resource name of the Execution to retrieve. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Execution | Instance of a general execution. |
get_metadata_schema
get_metadata_schema(request: Optional[Union[google.cloud.aiplatform_v1.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_v1
def sample_get_metadata_schema():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.GetMetadataSchemaRequest(
name="name_value",
)
# Make the request
response = client.get_metadata_schema(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.GetMetadataSchemaRequest, dict]
The request object. Request message for MetadataService.GetMetadataSchema. |
name |
`str`
Required. The resource name of the MetadataSchema to retrieve. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.MetadataSchema | Instance of a general MetadataSchema. |
get_metadata_store
get_metadata_store(request: Optional[Union[google.cloud.aiplatform_v1.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_v1
def sample_get_metadata_store():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.GetMetadataStoreRequest(
name="name_value",
)
# Make the request
response = client.get_metadata_store(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.GetMetadataStoreRequest, dict]
The request object. Request message for MetadataService.GetMetadataStore. |
name |
`str`
Required. The resource name of the MetadataStore to retrieve. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.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.
Name | Description |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. Only the |
Type | Description |
google.auth.exceptions.MutualTLSChannelError | If any errors happen. |
Type | Description |
Tuple[str, Callable[[], Tuple[bytes, bytes]]] | returns the API endpoint and the client cert source to use. |
get_transport_class
get_transport_class()
Returns an appropriate transport class.
list_artifacts
list_artifacts(request: Optional[Union[google.cloud.aiplatform_v1.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_v1
def sample_list_artifacts():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.ListArtifactsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_artifacts(request=request)
# Handle the response
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListArtifactsRequest, dict]
The request object. Request message for MetadataService.ListArtifacts. |
parent |
`str`
Required. The MetadataStore whose Artifacts should be listed. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.metadata_service.pagers.ListArtifactsAsyncPager | 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_v1.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_v1
def sample_list_contexts():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.ListContextsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_contexts(request=request)
# Handle the response
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListContextsRequest, dict]
The request object. Request message for MetadataService.ListContexts |
parent |
`str`
Required. The MetadataStore whose Contexts should be listed. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.metadata_service.pagers.ListContextsAsyncPager | 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_v1.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_v1
def sample_list_executions():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.ListExecutionsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_executions(request=request)
# Handle the response
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListExecutionsRequest, dict]
The request object. Request message for MetadataService.ListExecutions. |
parent |
`str`
Required. The MetadataStore whose Executions should be listed. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.metadata_service.pagers.ListExecutionsAsyncPager | 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_v1.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_v1
def sample_list_metadata_schemas():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListMetadataSchemasRequest, dict]
The request object. Request message for MetadataService.ListMetadataSchemas. |
parent |
`str`
Required. The MetadataStore whose MetadataSchemas should be listed. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.metadata_service.pagers.ListMetadataSchemasAsyncPager | 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_v1.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_v1
def sample_list_metadata_stores():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListMetadataStoresRequest, dict]
The request object. Request message for MetadataService.ListMetadataStores. |
parent |
`str`
Required. The Location whose MetadataStores should be listed. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.metadata_service.pagers.ListMetadataStoresAsyncPager | 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_v1.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_v1
def sample_purge_artifacts():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.PurgeArtifactsRequest, dict]
The request object. Request message for MetadataService.PurgeArtifacts. |
parent |
`str`
Required. The metadata store to purge Artifacts from. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | 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_v1.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_v1
def sample_purge_contexts():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.PurgeContextsRequest, dict]
The request object. Request message for MetadataService.PurgeContexts. |
parent |
`str`
Required. The metadata store to purge Contexts from. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | 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_v1.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_v1
def sample_purge_executions():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.PurgeExecutionsRequest, dict]
The request object. Request message for MetadataService.PurgeExecutions. |
parent |
`str`
Required. The metadata store to purge Executions from. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | 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_v1.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_v1
def sample_query_artifact_lineage_subgraph():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.QueryArtifactLineageSubgraphRequest(
artifact="artifact_value",
)
# Make the request
response = client.query_artifact_lineage_subgraph(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
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. |
Type | Description |
google.cloud.aiplatform_v1.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_v1.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_v1
def sample_query_context_lineage_subgraph():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.QueryContextLineageSubgraphRequest(
context="context_value",
)
# Make the request
response = client.query_context_lineage_subgraph(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
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. |
Type | Description |
google.cloud.aiplatform_v1.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_v1.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_v1
def sample_query_execution_inputs_and_outputs():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.QueryExecutionInputsAndOutputsRequest(
execution="execution_value",
)
# Make the request
response = client.query_execution_inputs_and_outputs(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.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: |
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. |
Type | Description |
google.cloud.aiplatform_v1.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_v1.types.metadata_service.UpdateArtifactRequest, dict]] = None, *, artifact: Optional[google.cloud.aiplatform_v1.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_v1
def sample_update_artifact():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.UpdateArtifactRequest(
)
# Make the request
response = client.update_artifact(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.UpdateArtifactRequest, dict]
The request object. Request message for MetadataService.UpdateArtifact. |
artifact |
Artifact
Required. The Artifact containing updates. The Artifact's Artifact.name field is used to identify the Artifact to be updated. Format: |
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 |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Artifact | Instance of a general artifact. |
update_context
update_context(request: Optional[Union[google.cloud.aiplatform_v1.types.metadata_service.UpdateContextRequest, dict]] = None, *, context: Optional[google.cloud.aiplatform_v1.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_v1
def sample_update_context():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.UpdateContextRequest(
)
# Make the request
response = client.update_context(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.UpdateContextRequest, dict]
The request object. Request message for MetadataService.UpdateContext. |
context |
Context
Required. The Context containing updates. The Context's Context.name field is used to identify the Context to be updated. Format: |
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 |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Context | Instance of a general context. |
update_execution
update_execution(request: Optional[Union[google.cloud.aiplatform_v1.types.metadata_service.UpdateExecutionRequest, dict]] = None, *, execution: Optional[google.cloud.aiplatform_v1.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_v1
def sample_update_execution():
# Create a client
client = aiplatform_v1.MetadataServiceClient()
# Initialize request argument(s)
request = aiplatform_v1.UpdateExecutionRequest(
)
# Make the request
response = client.update_execution(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.UpdateExecutionRequest, dict]
The request object. Request message for MetadataService.UpdateExecution. |
execution |
Execution
Required. The Execution containing updates. The Execution's Execution.name field is used to identify the Execution to be updated. Format: |
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 |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Execution | Instance of a general execution. |