def Run(self, args): with endpoint_util.MlEndpointOverrides(region=args.region): client = versions_api.VersionsClient() return versions_util.Delete(client, operations.OperationsClient(), args.version, model=args.model)
def Run(self, args): versions_client = versions_api.VersionsClient() labels = versions_util.ParseCreateLabels(versions_client, args) framework = flags.FRAMEWORK_MAPPER.GetEnumForChoice(args.framework) accelerator = flags.ParseAcceleratorFlag(args.accelerator) return versions_util.Create(versions_client, operations.OperationsClient(), args.version, model=args.model, origin=args.origin, staging_bucket=args.staging_bucket, runtime_version=args.runtime_version, config_file=args.config, asyncronous=args.async_, labels=labels, description=args.description, machine_type=args.machine_type, framework=framework, python_version=args.python_version, prediction_class=args.prediction_class, package_uris=args.package_uris, service_account=args.service_account, accelerator_config=accelerator, explanation_method=args.explanation_method, num_integral_steps=args.num_integral_steps, num_paths=args.num_paths)
def _Run(args): region = region_util.GetRegion(args) with endpoint_util.MlEndpointOverrides(region=region): models_client = models.ModelsClient() operations_client = operations.OperationsClient() models_util.Update(models_client, operations_client, args) log.UpdatedResource(args.model, kind='ai platform model')
def _Run(args): with endpoint_util.MlEndpointOverrides(region=args.region): versions_client = versions_api.VersionsClient() operations_client = operations.OperationsClient() version_ref = args.CONCEPTS.version.Parse() versions_util.Update(versions_client, operations_client, version_ref, args) log.UpdatedResource(args.version, kind='AI Platform version')
def Run(self, args): versions_client = versions_api.VersionsClient() operations_client = operations.OperationsClient() version_ref = args.CONCEPTS.version.Parse() versions_util.Update(versions_client, operations_client, version_ref, args) log.UpdatedResource(args.model, kind='ml engine model')
def Run(self, args): region = region_util.GetRegion(args) with endpoint_util.MlEndpointOverrides(region=region): client = versions_api.VersionsClient() labels = versions_util.ParseCreateLabels(client, args) framework = flags.FRAMEWORK_MAPPER.GetEnumForChoice(args.framework) accelerator = flags.ParseAcceleratorFlag(args.accelerator) return versions_util.Create(client, operations.OperationsClient(), args.version, model=args.model, origin=args.origin, staging_bucket=args.staging_bucket, runtime_version=args.runtime_version, config_file=args.config, asyncronous=args.async_, description=args.description, labels=labels, machine_type=args.machine_type, framework=framework, python_version=args.python_version, accelerator_config=accelerator, min_nodes=args.min_nodes, max_nodes=args.max_nodes, metrics=args.metric_targets, autoscaling_hidden=False)
def _Run(args): region = region_util.GetRegion(args) with endpoint_util.MlEndpointOverrides(region=region): client = versions_api.VersionsClient() return versions_util.Delete(client, operations.OperationsClient(), args.version, model=args.model)
def Run(self, args): return versions_util.Create(versions_api.VersionsClient('v1'), operations.OperationsClient('v1'), args.version, model=args.model, origin=args.origin, staging_bucket=args.staging_bucket, runtime_version=args.runtime_version, async_=args. async)
def Run(self, args): versions_client = versions_api.VersionsClient() operations_client = operations.OperationsClient() version_ref = args.CONCEPTS.version.Parse() versions_util.Update(versions_client, operations_client, version_ref, args, enable_user_code=True) log.UpdatedResource(args.version, kind='ML Engine version')
def Run(self, args): versions_client = versions_api.VersionsClient() labels = versions_util.ParseCreateLabels(versions_client, args) return versions_util.Create(versions_client, operations.OperationsClient(), args.version, model=args.model, origin=args.origin, staging_bucket=args.staging_bucket, runtime_version=args.runtime_version, config_file=args.config, async_=args. async, labels=labels)
def Run(self, args): versions_client = versions_api.VersionsClient() labels = versions_util.ParseCreateLabels(versions_client, args) framework = flags.FRAMEWORK_MAPPER.GetEnumForChoice(args.framework) return versions_util.Create(versions_client, operations.OperationsClient(), args.version, model=args.model, origin=args.origin, staging_bucket=args.staging_bucket, runtime_version=args.runtime_version, config_file=args.config, async_=args. async, description=args.description, labels=labels, framework=framework)
def Run(self, args): with endpoint_util.MlEndpointOverrides(region=args.region): client = versions_api.VersionsClient() labels = versions_util.ParseCreateLabels(client, args) framework = flags.FRAMEWORK_MAPPER.GetEnumForChoice(args.framework) return versions_util.Create(client, operations.OperationsClient(), args.version, model=args.model, origin=args.origin, staging_bucket=args.staging_bucket, runtime_version=args.runtime_version, config_file=args.config, asyncronous=args.async_, description=args.description, labels=labels, framework=framework, python_version=args.python_version)
def Run(self, args): versions_client = versions_api.VersionsClient() labels = versions_util.ParseCreateLabels(versions_client, args) framework = flags.FRAMEWORK_MAPPER.GetEnumForChoice(args.framework) return versions_util.Create(versions_client, operations.OperationsClient(), args.version, model=args.model, origin=args.origin, staging_bucket=args.staging_bucket, runtime_version=args.runtime_version, config_file=args.config, asyncronous=args. async, description=args.description, labels=labels, machine_type=args.machine_type, framework=framework, python_version=args.python_version, prediction_class=args.prediction_class, package_uris=args.package_uris)
def Run(self, args): region = region_util.GetRegion(args) with endpoint_util.MlEndpointOverrides(region=region): client = versions_api.VersionsClient() labels = versions_util.ParseCreateLabels(client, args) framework = flags.FRAMEWORK_MAPPER.GetEnumForChoice(args.framework) accelerator = flags.ParseAcceleratorFlag(args.accelerator) return versions_util.Create( client, operations.OperationsClient(), args.version, model=args.model, origin=args.origin, staging_bucket=args.staging_bucket, runtime_version=args.runtime_version, config_file=args.config, asyncronous=args.async_, labels=labels, description=args.description, machine_type=args.machine_type, framework=framework, python_version=args.python_version, prediction_class=args.prediction_class, package_uris=args.package_uris, service_account=args.service_account, accelerator_config=accelerator, explanation_method=args.explanation_method, num_integral_steps=args.num_integral_steps, num_paths=args.num_paths, image=args.image, command=args.command, container_args=args.args, env_vars=args.env_vars, ports=args.ports, predict_route=args.predict_route, health_route=args.health_route, min_nodes=args.min_nodes, max_nodes=args.max_nodes, metrics=args.metric_targets, containers_hidden=False, autoscaling_hidden=False)
def _Run(args): with endpoint_util.MlEndpointOverrides(region=args.region): client = operations.OperationsClient() return operations_util.Describe(client, args.operation)
def Run(self, args): return operations_util.List(operations.OperationsClient('v1'))
def SetUp(self): self.operations_client = operations.OperationsClient(self.API_VERSION) self.operation_get_request = self.msgs.MlProjectsOperationsGetRequest( name='projects/{}/operations/operation'.format(self.Project())) self.StartObjectPatch(time, 'sleep')
def Run(self, args): with endpoint_util.MlEndpointOverrides(region=args.region): models_client = models.ModelsClient() operations_client = operations.OperationsClient() models_util.Update(models_client, operations_client, args) log.UpdatedResource(args.model, kind='ml engine model')
def Run(self, args): models_client = models.ModelsClient() operations_client = operations.OperationsClient() models_util.Update(models_client, operations_client, args) log.UpdatedResource(args.model, kind='ml engine model')
def _Run(args): region = region_util.GetRegion(args) with endpoint_util.MlEndpointOverrides(region=region): models_client = models.ModelsClient() operations_client = operations.OperationsClient() return models_util.Delete(models_client, operations_client, args.model)
def Run(self, args): return versions_util.Delete(versions_api.VersionsClient(), operations.OperationsClient(), args.version, model=args.model)
def Run(self, args): return operations_util.Describe(operations.OperationsClient(), args.operation)
def SetUp(self): self.operations_client = operations.OperationsClient() self.StartPatch('time.sleep')
def Run(self, args): return operations_util.Wait(operations.OperationsClient('v1'), args.operation)
def Run(self, args): return operations_util.Cancel(operations.OperationsClient(), args.operation)
def Run(self, args): models_client = models.ModelsClient() operations_client = operations.OperationsClient() return models_util.Delete(models_client, operations_client, args.model)
def Run(self, args): with endpoint_util.MlEndpointOverrides(region=args.region): client = operations.OperationsClient() return operations_util.Wait(client, args.operation)
def Run(self, args): jobs_client = jobs.JobsClient() operations_client = operations.OperationsClient() jobs_util.Update(jobs_client, operations_client, args) log.UpdatedResource(args.job, kind='ml engine job')
def Run(self, args): versions_client = versions_api.VersionsClient() operations_client = operations.OperationsClient() version_ref = args.CONCEPTS.version.Parse() versions_util.Update(versions_client, operations_client, version_ref, args,) log.UpdatedResource(args.version, kind='AI Platform version')
def Run(self, args): with endpoint_util.MlEndpointOverrides(region=args.region): models_client = models.ModelsClient() operations_client = operations.OperationsClient() return models_util.Delete(models_client, operations_client, args.model)