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):
index_endpoint_client = client.IndexEndpointsClient(
version=version)
try:
if version == constants.GA_VERSION:
result = index_endpoint_client.Patch(
index_endpoint_ref, args)
else:
result = index_endpoint_client.PatchBeta(
index_endpoint_ref, args)
except errors.NoFieldsSpecifiedError:
available_update_args = [
'display_name', 'description', 'update_labels',
'clear_labels', 'remove_labels'
]
if not any(
args.IsSpecified(arg)
for arg in available_update_args):
raise
log.status.Print('No update to perform.')
return None
else:
log.UpdatedResource(result.name,
kind='Vertex AI index endpoint')
return result
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, version):
validation.ValidateDisplayName(args.display_name)
index_endpoint_ref = args.CONCEPTS.index_endpoint.Parse()
project_id = index_endpoint_ref.AsDict()['projectsId']
region = index_endpoint_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
index_endpoint_client = client.IndexEndpointsClient(
version=version)
if version == constants.GA_VERSION:
operation = index_endpoint_client.DeployIndex(
index_endpoint_ref, args)
else:
operation = index_endpoint_client.DeployIndexBeta(
index_endpoint_ref, args)
op_ref = index_endpoints_util.ParseIndexEndpointOperation(
operation.name)
# TODO(b/208506223): Support `--async` flag.
index_endpoint_id = op_ref.AsDict()['indexEndpointsId']
log.status.Print(
constants.OPERATION_CREATION_DISPLAY_MESSAGE.format(
name=operation.name,
verb='deploy index',
id=op_ref.Name(),
sub_commands='--index-endpoint={} [--project={}]'.format(
index_endpoint_id, project_id)))
return operation
def _Run(args, version):
"""Run method for update command."""
validation.ValidateDisplayName(args.display_name)
model_monitoring_job_ref = args.CONCEPTS.monitoring_job.Parse()
region = model_monitoring_job_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
try:
result = client.ModelMonitoringJobsClient(version=version).Patch(
model_monitoring_job_ref, args)
except errors.NoFieldsSpecifiedError:
available_update_args = [
'display_name',
'emails',
'prediction_sampling_rate',
'feature_thresholds',
'monitoring_config_from_file',
'monitoring_frequency',
'analysis_instance_schema',
'log_ttl',
'update_labels',
'clear_labels',
'remove_labels',
'anomaly_cloud_logging',
]
if not any(args.IsSpecified(arg) for arg in available_update_args):
raise
log.status.Print('No update to perform.')
return None
else:
log.UpdatedResource(
result.name, kind='Vertex AI model deployment monitoring job')
return result
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(args, version):
"""Create a new Vertex AI Tensorboard experiment."""
validation.ValidateDisplayName(args.display_name)
tensorboard_ref = args.CONCEPTS.tensorboard.Parse()
region = tensorboard_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
tensorboard_experiments_client = client.TensorboardExperimentsClient(
version=version)
response = tensorboard_experiments_client.Create(tensorboard_ref, args)
if response.name:
log.status.Print(('Created Vertex AI Tensorboard experiment: {}.').format(
response.name))
return response
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.IndexesClient().CreateBeta(region_ref, args)
op_ref = indexes_util.ParseIndexOperation(operation.name)
index_id = op_ref.AsDict()['indexesId']
log.status.Print(
constants.OPERATION_CREATION_DISPLAY_MESSAGE.format(
name=operation.name,
verb='create index',
id=op_ref.Name(),
sub_commands='--index={}'.format(index_id)))
return operation
def _Run(args, version):
"""Update an existing Vertex AI 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)
def GetLabels():
return endpoints_client.Get(endpoint_ref).labels
try:
if version == constants.GA_VERSION:
op = endpoints_client.Patch(
endpoint_ref,
labels_util.ProcessUpdateArgsLazy(
args, endpoints_client.messages.
GoogleCloudAiplatformV1Endpoint.LabelsValue,
GetLabels),
display_name=args.display_name,
description=args.description,
traffic_split=args.traffic_split,
clear_traffic_split=args.clear_traffic_split)
else:
op = endpoints_client.PatchBeta(
endpoint_ref,
labels_util.ProcessUpdateArgsLazy(
args, endpoints_client.messages.
GoogleCloudAiplatformV1beta1Endpoint.LabelsValue,
GetLabels),
display_name=args.display_name,
description=args.description,
traffic_split=args.traffic_split,
clear_traffic_split=args.clear_traffic_split)
except errors.NoFieldsSpecifiedError:
available_update_args = [
'display_name', 'traffic_split', 'clear_traffic_split',
'update_labels', 'clear_labels', 'remove_labels', 'description'
]
if not any(args.IsSpecified(arg) for arg in available_update_args):
raise
log.status.Print('No update to perform.')
return None
else:
log.UpdatedResource(op.name, kind='Vertex AI endpoint')
return op
def _Run(args, version):
"""Create a new Vertex AI Tensorboard time series."""
validation.ValidateDisplayName(args.display_name)
tensorboard_run_ref = args.CONCEPTS.tensorboard_run.Parse()
region = tensorboard_run_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version, region=region):
tensorboard_runs_client = client.TensorboardTimeSeriesClient(
version=version)
response = tensorboard_runs_client.Create(tensorboard_run_ref, args)
response_msg = encoding.MessageToPyValue(response)
if 'name' in response_msg:
log.status.Print(
('Created Vertex AI Tensorboard time series: {}.').format(
response_msg['name']))
return response
def _Run(args, version, release_prefix):
"""Run method for create command."""
validation.ValidateDisplayName(args.display_name)
region_ref = args.CONCEPTS.region.Parse()
region = region_ref.AsDict()['locationsId']
with endpoint_util.AiplatformEndpointOverrides(version=version,
region=region):
response = client.ModelMonitoringJobsClient(version=version).Create(
region_ref, args)
cmd_prefix = 'gcloud'
if release_prefix:
cmd_prefix += ' ' + release_prefix
log.status.Print(
constants.MODEL_MONITORING_JOB_CREATION_DISPLAY_MESSAGE.format(
id=model_monitoring_jobs_util.ParseJobName(response.name),
cmd_prefix=cmd_prefix,
state=response.state))
return response
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):
"""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)
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):
"""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
def _Run(args, version):
"""Update 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)
try:
op = endpoints_client.PatchBeta(endpoint_ref, args)
except errors.NoFieldsSpecifiedError:
available_update_args = [
'display_name', 'traffic_split', 'clear_traffic_split',
'update_labels', 'clear_labels', 'remove_labels', 'description'
]
if not any(args.IsSpecified(arg) for arg in available_update_args):
raise
log.status.Print('No update to perform.')
return None
else:
log.UpdatedResource(op.name, kind='AI Platform endpoint')
return op
def Run(self, args): validation.ValidateDisplayName(args.display_name) return _Run(args, constants.ALPHA_VERSION)
