def execute(self, context):
hook = CloudMLHook(
gcp_conn_id=self._gcp_conn_id, delegate_to=self._delegate_to)
if self._operation == 'create':
hook.create_model(self._project_id, self._model)
elif self._operation == 'get':
hook.get_model(self._project_id, self._model['name'])
else:
raise ValueError('Unknown operation: {}'.format(self._operation))
def execute(self, context):
hook = CloudMLHook(self.gcp_conn_id, self.delegate_to)
try:
finished_prediction_job = hook.create_job(
self.project_id,
self.prediction_job_request)
except errors.HttpError:
raise
if finished_prediction_job['state'] != 'SUCCEEDED':
logging.error(
'Batch prediction job failed: %s',
str(finished_prediction_job))
raise RuntimeError(finished_prediction_job['errorMessage'])
return finished_prediction_job['predictionOutput']
def execute(self, context):
hook = CloudMLHook(
gcp_conn_id=self._gcp_conn_id, delegate_to=self._delegate_to)
if self._operation == 'create':
assert self._version is not None
return hook.create_version(self._project_name, self._model_name,
self._version)
elif self._operation == 'set_default':
return hook.set_default_version(
self._project_name, self._model_name,
self._version['name'])
elif self._operation == 'list':
return hook.list_versions(self._project_name, self._model_name)
elif self._operation == 'delete':
return hook.delete_version(self._project_name, self._model_name,
self._version['name'])
else:
raise ValueError('Unknown operation: {}'.format(self._operation))
def execute(self, context):
job_id = _normalize_cloudml_job_id(self._job_id)
training_request = {
'jobId': job_id,
'trainingInput': {
'scaleTier': self._scale_tier,
'packageUris': self._package_uris,
'pythonModule': self._training_python_module,
'region': self._region,
'args': self._training_args,
}
}
if self._mode == 'DRY_RUN':
logging.info('In dry_run mode.')
logging.info(
'CloudML Training job request is: {}'.format(training_request))
return
hook = CloudMLHook(gcp_conn_id=self._gcp_conn_id,
delegate_to=self._delegate_to)
# Helper method to check if the existing job's training input is the
# same as the request we get here.
def check_existing_job(existing_job):
return existing_job.get('trainingInput', None) == \
training_request['trainingInput']
try:
finished_training_job = hook.create_job(self._project_id,
training_request,
check_existing_job)
except errors.HttpError:
raise
if finished_training_job['state'] != 'SUCCEEDED':
logging.error('CloudML training job failed: {}'.format(
str(finished_training_job)))
raise RuntimeError(finished_training_job['errorMessage'])
def execute(self, context):
hook = CloudMLHook(gcp_conn_id=self._gcp_conn_id,
delegate_to=self._delegate_to)
if self._operation == 'create':
hook.create_model(self._project_id, self._model)
elif self._operation == 'get':
hook.get_model(self._project_id, self._model['name'])
else:
raise ValueError('Unknown operation: {}'.format(self._operation))
def execute(self, context):
job_id = _normalize_cloudml_job_id(self._job_id)
training_request = {
'jobId': job_id,
'trainingInput': {
'scaleTier': self._scale_tier,
'packageUris': self._package_uris,
'pythonModule': self._training_python_module,
'region': self._region,
'args': self._training_args,
}
}
if self._mode == 'DRY_RUN':
logging.info('In dry_run mode.')
logging.info(
'CloudML Training job request is: {}'.format(training_request))
return
hook = CloudMLHook(
gcp_conn_id=self._gcp_conn_id, delegate_to=self._delegate_to)
# Helper method to check if the existing job's training input is the
# same as the request we get here.
def check_existing_job(existing_job):
return existing_job.get('trainingInput', None) == \
training_request['trainingInput']
try:
finished_training_job = hook.create_job(
self._project_id, training_request, check_existing_job)
except errors.HttpError:
raise
if finished_training_job['state'] != 'SUCCEEDED':
logging.error('CloudML training job failed: {}'.format(
str(finished_training_job)))
raise RuntimeError(finished_training_job['errorMessage'])
def execute(self, context):
hook = CloudMLHook(gcp_conn_id=self._gcp_conn_id,
delegate_to=self._delegate_to)
if self._operation == 'create':
assert self._version is not None
return hook.create_version(self._project_id, self._model_name,
self._version)
elif self._operation == 'set_default':
return hook.set_default_version(self._project_id, self._model_name,
self._version['name'])
elif self._operation == 'list':
return hook.list_versions(self._project_id, self._model_name)
elif self._operation == 'delete':
return hook.delete_version(self._project_id, self._model_name,
self._version['name'])
else:
raise ValueError('Unknown operation: {}'.format(self._operation))