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):
response = client.CustomJobsClient(version=constants.BETA_VERSION).Create(
region_ref.RelativeName(), args.worker_pool_spec, args.config,
args.display_name, args.python_package_uris, args.args, args.command,
validation.GetAndValidateKmsKey(args), args.network,
args.service_account)
log.status.Print(
constants.CUSTOM_JOB_CREATION_DISPLAY_MESSAGE.format(
id=custom_jobs_util.ParseJobName(response.name),
state=response.state))
return response
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):
algorithm = arg_utils.ChoiceToEnum(
args.algorithm, client.HpTuningJobsClient.GetAlgorithmEnum())
response = client.HpTuningJobsClient().Create(
parent=region_ref.RelativeName(),
config_path=args.config,
display_name=args.display_name,
max_trial_count=args.max_trial_count,
parallel_trial_count=args.parallel_trial_count,
algorithm=algorithm,
kms_key_name=validation.GetAndValidateKmsKey(args))
log.status.Print(
constants.HPTUNING_JOB_CREATION_DISPLAY_MESSAGE.format(
id=hp_tuning_jobs_util.ParseJobName(response.name),
state=response.state))
return response
def Run(self, args):
project = properties.VALUES.core.project.GetOrFail()
region_ref = args.CONCEPTS.region.Parse()
region = region_ref.AsDict()['locationsId']
validation.ValidateRegion(region)
with endpoint_util.AiplatformEndpointOverrides(
version=self._version, region=region):
api_client = client.CustomJobsClient(version=self._version)
job_spec = self._PrepareJobSpec(args, api_client, project)
labels = labels_util.ParseCreateArgs(
args,
api_client.CustomJobMessage().LabelsValue)
response = api_client.Create(
parent=region_ref.RelativeName(),
display_name=args.display_name,
job_spec=job_spec,
kms_key_name=common_validation.GetAndValidateKmsKey(args),
labels=labels)
self._DisplayResult(response)
return response
def Create(self, location_ref, args):
"""Creates a model deployment monitoring job."""
endpoint_ref = _ParseEndpoint(args.endpoint, location_ref)
job_spec = api_util.GetMessage('ModelDeploymentMonitoringJob',
self._version)()
kms_key_name = common_validation.GetAndValidateKmsKey(args)
if kms_key_name is not None:
job_spec.encryptionSpec = api_util.GetMessage(
'EncryptionSpec', self._version)(kmsKeyName=kms_key_name)
if args.monitoring_config_from_file:
data = yaml.load_path(args.monitoring_config_from_file)
if data:
job_spec = messages_util.DictToMessageWithErrorCheck(
data,
api_util.GetMessage('ModelDeploymentMonitoringJob',
self._version))
else:
job_spec.modelDeploymentMonitoringObjectiveConfigs = self._ConstructObjectiveConfigForCreate(
location_ref, endpoint_ref.RelativeName(),
args.feature_thresholds, args.feature_attribution_thresholds,
args.dataset, args.bigquery_uri, args.data_format,
args.gcs_uris, args.target_field, args.training_sampling_rate)
job_spec.endpoint = endpoint_ref.RelativeName()
job_spec.displayName = args.display_name
enable_anomaly_cloud_logging = False if args.anomaly_cloud_logging is None else args.anomaly_cloud_logging
job_spec.modelMonitoringAlertConfig = api_util.GetMessage(
'ModelMonitoringAlertConfig',
self._version)(enableLogging=enable_anomaly_cloud_logging,
emailAlertConfig=api_util.GetMessage(
'ModelMonitoringAlertConfigEmailAlertConfig',
self._version)(userEmails=args.emails))
job_spec.loggingSamplingStrategy = api_util.GetMessage(
'SamplingStrategy',
self._version)(randomSampleConfig=api_util.GetMessage(
'SamplingStrategyRandomSampleConfig', self._version)(
sampleRate=args.prediction_sampling_rate))
job_spec.modelDeploymentMonitoringScheduleConfig = api_util.GetMessage(
'ModelDeploymentMonitoringScheduleConfig',
self._version)(monitorInterval='{}s'.format(
six.text_type(3600 * int(args.monitoring_frequency))))
if args.predict_instance_schema:
job_spec.predictInstanceSchemaUri = args.predict_instance_schema
if args.analysis_instance_schema:
job_spec.analysisInstanceSchemaUri = args.analysis_instance_schema
if args.log_ttl:
job_spec.logTtl = '{}s'.format(
six.text_type(86400 * int(args.log_ttl)))
if args.sample_predict_request:
instance_json = model_monitoring_jobs_util.ReadInstanceFromArgs(
args.sample_predict_request)
job_spec.samplePredictInstance = encoding.PyValueToMessage(
extra_types.JsonValue, instance_json)
if self._version == constants.ALPHA_VERSION:
return self._service.Create(
self.messages.
AiplatformProjectsLocationsModelDeploymentMonitoringJobsCreateRequest(
parent=location_ref.RelativeName(),
googleCloudAiplatformV1alpha1ModelDeploymentMonitoringJob=
job_spec))
elif self._version == constants.BETA_VERSION:
return self._service.Create(
self.messages.
AiplatformProjectsLocationsModelDeploymentMonitoringJobsCreateRequest(
parent=location_ref.RelativeName(),
googleCloudAiplatformV1beta1ModelDeploymentMonitoringJob=
job_spec))
else:
return self._service.Create(
self.messages.
AiplatformProjectsLocationsModelDeploymentMonitoringJobsCreateRequest(
parent=location_ref.RelativeName(),
googleCloudAiplatformV1ModelDeploymentMonitoringJob=job_spec
))