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)
