def deploy(self, pod_template_spec):
"""Deploys the training job"""
# TODO: Update deploy interface to pass image directly instad of
# PodTemplateSpec.
# Retrieve image uri from pod template spec.
image_uri = pod_template_spec.containers[0].image
self._job_name = 'fairing_job_{}'.format(utils.random_tag())
request_dict = {
'jobId': self._job_name,
'trainingInput': {
'scaleTier': self.scale_tier,
'masterConfig': {
'imageUri': image_uri,
},
'region': self._region
}
}
try:
response = self._ml.projects().jobs().create(
parent='projects/{}'.format(self._project_id),
body=request_dict).execute()
print('Job submitted successfully.')
self.get_logs()
except errors.HttpError as err:
print('There was an error submitting the job.')
print(err._get_reason())
def create_request_dict(self, pod_template_spec):
"""Return the request to be sent to the ML Engine API."""
# TODO: Update deploy interface to pass image directly instad of
# PodTemplateSpec.
# Retrieve image uri from pod template spec.
image_uri = pod_template_spec.containers[0].image
self._job_name = 'fairing_job_{}'.format(utils.random_tag())
# Merge explicitly specified parameters with the job config dictionary
request_dict = self._job_config
request_dict['jobId'] = self._job_name
if 'trainingInput' not in request_dict:
request_dict['trainingInput'] = {}
if self.scale_tier:
request_dict['trainingInput']['scaleTier'] = self.scale_tier
if 'masterConfig' not in request_dict['trainingInput']:
request_dict['trainingInput']['masterConfig'] = {}
request_dict['trainingInput']['masterConfig']['imageUri'] = image_uri
if self._region:
request_dict['trainingInput']['region'] = self._region
return request_dict