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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)