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)
