def _Run(self, args, version):
index_ref = args.CONCEPTS.index.Parse()
region = index_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
operation = client.IndexesClient().PatchBeta(index_ref, args)
if args.metadata_file is not None:
# Update index content.
op_ref = indexes_util.ParseIndexOperation(operation.name)
log.status.Print(
constants.OPERATION_CREATION_DISPLAY_MESSAGE.format(
name=operation.name,
verb='update index',
id=op_ref.Name(),
sub_commands='--index={}'.format(index_ref.Name())))
# We will not wait for the operation since it can take up to hours.
return operation
# Update index meta data.
response_msg = operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=indexes_util.ParseIndexOperation(operation.name),
log_method='update')
if response_msg is not None:
response = encoding.MessageToPyValue(response_msg)
if 'name' in response:
log.UpdatedResource(response['name'],
kind='AI Platform index')
return response_msg
def Run(self, args):
region_ref = args.CONCEPTS.region.Parse()
region = region_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(
version=constants.GA_VERSION, region=region):
client_instance = apis.GetClientInstance(
constants.AI_PLATFORM_API_NAME,
constants.AI_PLATFORM_API_VERSION[constants.GA_VERSION])
operation = client.ModelsClient(
client=client_instance,
messages=client_instance.MESSAGES_MODULE).UploadV1(
region_ref,
args.display_name,
args.description,
args.artifact_uri,
args.container_image_uri,
args.container_command,
args.container_args,
args.container_env_vars,
args.container_ports,
args.container_predict_route,
args.container_health_route,
explanation_spec=self._BuildExplanationSpec(args))
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(
client=client_instance, messages=client_instance.MESSAGES_MODULE),
op=operation,
op_ref=models_util.ParseModelOperation(operation.name))
def _Run(args, version):
"""Create a new Vertex AI endpoint."""
validation.ValidateDisplayName(args.display_name)
region_ref = args.CONCEPTS.region.Parse()
args.region = region_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version,
region=args.region):
endpoints_client = client.EndpointsClient(version=version)
operation_client = operations.OperationsClient()
if version == constants.GA_VERSION:
op = endpoints_client.Create(
region_ref, args.display_name,
labels_util.ParseCreateArgs(
args, endpoints_client.messages.
GoogleCloudAiplatformV1Endpoint.LabelsValue),
args.description, args.network, args.endpoint_id)
else:
op = endpoints_client.CreateBeta(
region_ref, args.display_name,
labels_util.ParseCreateArgs(
args, endpoints_client.messages.
GoogleCloudAiplatformV1beta1Endpoint.LabelsValue),
args.description, args.network, args.endpoint_id)
response_msg = operations_util.WaitForOpMaybe(
operation_client, op, endpoints_util.ParseOperation(op.name))
if response_msg is not None:
response = encoding.MessageToPyValue(response_msg)
if 'name' in response:
log.status.Print(('Created Vertex AI endpoint: {}.').format(
response['name']))
return response_msg
def _Run(self, args, model_ref, region):
with endpoint_util.AiplatformEndpointOverrides(
version=constants.BETA_VERSION, region=region):
operation = client.ModelsClient().Delete(model_ref)
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=models_util.ParseModelOperation(operation.name))
def _Run(self, args):
model_ref = args.CONCEPTS.model.Parse()
region = model_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(
version=constants.BETA_VERSION, region=region):
operation = client.ModelsClient().Delete(model_ref)
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=models_util.ParseModelOperation(operation.name))
def _Run(args, version):
"""Deploy a model to an existing Vertex AI endpoint."""
validation.ValidateDisplayName(args.display_name)
if version != constants.GA_VERSION:
validation.ValidateAutoscalingMetricSpecs(
args.autoscaling_metric_specs)
endpoint_ref = args.CONCEPTS.endpoint.Parse()
args.region = endpoint_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version,
region=args.region):
endpoints_client = client.EndpointsClient(version=version)
operation_client = operations.OperationsClient()
if version == constants.GA_VERSION:
op = endpoints_client.DeployModel(
endpoint_ref,
args.model,
args.region,
args.display_name,
machine_type=args.machine_type,
accelerator_dict=args.accelerator,
min_replica_count=args.min_replica_count,
max_replica_count=args.max_replica_count,
enable_access_logging=args.enable_access_logging,
disable_container_logging=args.disable_container_logging,
service_account=args.service_account,
traffic_split=args.traffic_split,
deployed_model_id=args.deployed_model_id)
else:
op = endpoints_client.DeployModelBeta(
endpoint_ref,
args.model,
args.region,
args.display_name,
machine_type=args.machine_type,
accelerator_dict=args.accelerator,
min_replica_count=args.min_replica_count,
max_replica_count=args.max_replica_count,
autoscaling_metric_specs=args.autoscaling_metric_specs,
enable_access_logging=args.enable_access_logging,
enable_container_logging=args.enable_container_logging,
service_account=args.service_account,
traffic_split=args.traffic_split,
deployed_model_id=args.deployed_model_id)
response_msg = operations_util.WaitForOpMaybe(
operation_client, op, endpoints_util.ParseOperation(op.name))
if response_msg is not None:
response = encoding.MessageToPyValue(response_msg)
if 'deployedModel' in response and 'id' in response[
'deployedModel']:
log.status.Print(('Deployed a model to the endpoint {}. '
'Id of the deployed model: {}.').format(
endpoint_ref.AsDict()['endpointsId'],
response['deployedModel']['id']))
return response_msg
def _Run(self, args):
tensorboard_ref = args.CONCEPTS.tensorboard.Parse()
region = tensorboard_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(
version=constants.ALPHA_VERSION, region=region):
operation = client.TensorboardsClient().Delete(tensorboard_ref)
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=tensorboards_util.ParseTensorboardOperation(
operation.name))
def _Run(args, version):
"""Undeploy a model fro man existing AI Platform endpoint."""
endpoint_ref = args.CONCEPTS.endpoint.Parse()
args.region = endpoint_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version,
region=args.region):
endpoints_client = client.EndpointsClient(version=version)
operation_client = operations.OperationsClient()
op = endpoints_client.UndeployModelBeta(endpoint_ref, args)
return operations_util.WaitForOpMaybe(
operation_client, op, endpoints_util.ParseOperation(op.name))
def _Run(args, version):
tensorboard_run_ref = args.CONCEPTS.tensorboard_run.Parse()
region = tensorboard_run_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version=version,
region=region):
operation = client.TensorboardRunsClient(
version=version).Delete(tensorboard_run_ref)
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=tensorboards_util.ParseTensorboardOperation(operation.name))
def _Run(self, args, version):
index_endpoint_ref = args.CONCEPTS.index_endpoint.Parse()
region = index_endpoint_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
operation = client.IndexEndpointsClient().UndeployIndexBeta(
index_endpoint_ref, args)
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=index_endpoints_util.ParseIndexEndpointOperation(
operation.name))
def _Run(self, args, version):
index_ref = args.CONCEPTS.index.Parse()
region = index_ref.AsDict()['locationsId']
index_id = index_ref.AsDict()['indexesId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
console_io.PromptContinue(
'This will delete index [{}]...'.format(index_id),
cancel_on_no=True)
operation = client.IndexesClient().Delete(index_ref)
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=indexes_util.ParseIndexOperation(operation.name))
def _Run(self, args, version):
# This is the default operation name in the format of
# `projects/123/locations/us-central1/operations/456`.
operation_ref = args.CONCEPTS.operation.Parse()
project_id = operation_ref.AsDict()['projectsId']
region = operation_ref.AsDict()['locationsId']
operation_id = operation_ref.AsDict()['operationsId']
if args.index is not None:
# Override the operation name using the multi-parent format.
operation_ref = indexes_util.BuildIndexParentOperation(
project_id, region, args.index, operation_id)
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
return operations.OperationsClient().Get(operation_ref)
def Run(self, args):
region_ref = args.CONCEPTS.region.Parse()
region = region_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(
version=constants.BETA_VERSION, region=region):
operation = client.ModelsClient().UploadV1Beta1(
region_ref, args.display_name, args.description,
args.artifact_uri, args.container_image_uri,
args.container_command, args.container_args,
args.container_env_vars, args.container_ports,
args.container_predict_route, args.container_health_route)
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=models_util.ParseModelOperation(operation.name))
def _Run(args, version):
"""Delete an existing Vertex AI endpoint."""
endpoint_ref = args.CONCEPTS.endpoint.Parse()
args.region = endpoint_ref.AsDict()['locationsId']
endpoint_id = endpoint_ref.AsDict()['endpointsId']
with endpoint_util.AiplatformEndpointOverrides(version,
region=args.region):
endpoints_client = client.EndpointsClient(version=version)
operation_client = operations.OperationsClient()
console_io.PromptContinue(
'This will delete endpoint [{}]...'.format(endpoint_id),
cancel_on_no=True)
op = endpoints_client.Delete(endpoint_ref)
return operations_util.WaitForOpMaybe(
operation_client, op, endpoints_util.ParseOperation(op.name))
def _Run(args, version):
"""Run method for delete command."""
model_monitoring_job_ref = args.CONCEPTS.monitoring_job.Parse()
region = model_monitoring_job_ref.AsDict()['locationsId']
model_monitoring_job_id = model_monitoring_job_ref.AsDict(
)['modelDeploymentMonitoringJobsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
console_io.PromptContinue(
'This will delete model deployment monitoring job [{}]...'.format(
model_monitoring_job_id),
cancel_on_no=True)
operation = client.ModelMonitoringJobsClient(
version=version).Delete(model_monitoring_job_ref)
return operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=model_monitoring_jobs_util.ParseMonitoringJobOperation(
operation.name))
def _Run(self, args, version):
validation.ValidateDisplayName(args.display_name)
index_endpoint_ref = args.CONCEPTS.index_endpoint.Parse()
region = index_endpoint_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
operation = client.IndexEndpointsClient().DeployIndexBeta(
index_endpoint_ref, args)
response_msg = operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=index_endpoints_util.ParseIndexEndpointOperation(
operation.name))
if response_msg is not None:
response = encoding.MessageToPyValue(response_msg)
if 'deployedIndex' in response and 'id' in response[
'deployedIndex']:
log.status.Print(('Id of the deployed index: {}.').format(
response['deployedIndex']['id']))
return response_msg
def _Run(args, version):
"""Create a new AI Platform endpoint."""
validation.ValidateDisplayName(args.display_name)
region_ref = args.CONCEPTS.region.Parse()
args.region = region_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version,
region=args.region):
endpoints_client = client.EndpointsClient(version=version)
operation_client = operations.OperationsClient()
op = endpoints_client.CreateBeta(region_ref, args)
response_msg = operations_util.WaitForOpMaybe(
operation_client, op, endpoints_util.ParseOperation(op.name))
if response_msg is not None:
response = encoding.MessageToPyValue(response_msg)
if 'name' in response:
log.status.Print(('Created AI Platform endpoint: {}.').format(
response['name']))
return response_msg
def _Run(self, args, version):
validation.ValidateDisplayName(args.display_name)
region_ref = args.CONCEPTS.region.Parse()
region = region_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
operation = client.IndexEndpointsClient().CreateBeta(
region_ref, args)
response_msg = operations_util.WaitForOpMaybe(
operations_client=operations.OperationsClient(),
op=operation,
op_ref=index_endpoints_util.ParseIndexEndpointOperation(
operation.name))
if response_msg is not None:
response = encoding.MessageToPyValue(response_msg)
if 'name' in response:
log.status.Print(
('Created AI Platform index endpoint: {}.').format(
response['name']))
return response_msg
def _Run(args, version):
"""Undeploy a model fro man existing Vertex AI endpoint."""
endpoint_ref = args.CONCEPTS.endpoint.Parse()
args.region = endpoint_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=args.region):
endpoints_client = client.EndpointsClient(version=version)
operation_client = operations.OperationsClient()
if version == constants.GA_VERSION:
op = endpoints_client.UndeployModel(
endpoint_ref,
args.deployed_model_id,
traffic_split=args.traffic_split)
else:
op = endpoints_client.UndeployModelBeta(
endpoint_ref,
args.deployed_model_id,
traffic_split=args.traffic_split)
return operations_util.WaitForOpMaybe(
operation_client, op, endpoints_util.ParseOperation(op.name))
def _Run(args, version):
"""Deploy a model to an existing AI Platform endpoint."""
validation.ValidateDisplayName(args.display_name)
endpoint_ref = args.CONCEPTS.endpoint.Parse()
args.region = endpoint_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version,
region=args.region):
endpoints_client = client.EndpointsClient(version=version)
operation_client = operations.OperationsClient()
op = endpoints_client.DeployModelBeta(endpoint_ref, args)
response_msg = operations_util.WaitForOpMaybe(
operation_client, op, endpoints_util.ParseOperation(op.name))
if response_msg is not None:
response = encoding.MessageToPyValue(response_msg)
if 'deployedModel' in response and 'id' in response[
'deployedModel']:
log.status.Print(('Deployed a model to the endpoint {}. '
'Id of the deployed model: {}.').format(
endpoint_ref.AsDict()['endpointsId'],
response['deployedModel']['id']))
return response_msg
