def test_train_mlengine(self):
train_job.train_job['name'] = str(uuid.uuid4())
job = Job(train_job.train_job['name'], train_job.train_job)
job.type = 'preprocess'
job.init_temp(str(uuid.uuid4()))
try:
logging.info("step1")
job.init_storage()
logging.info("step2")
if not hasattr(job, 'label_file') or job.label_file is None:
job.label_file = 'corpus/' + job.prep_name + "/object-detection.pbtxt"
job.init_labels()
self.assertGreater(len(job.categories), 0)
logging.info("step3")
source = json.loads(
job.download_to_string('corpus/' + job.prep_name +
"/job_def.json"))
job.project_name = source['project_name']
logging.info("step4")
updateFileML(job)
logging.info("step5")
upload_model(job)
logging.info("step6")
upload_packages(job)
logging.info("step7")
start_ml_engine(job)
logging.info("step8")
history = json.loads(
job.download_to_string(
'corpus/' + job.prep_name + "/job_history.json", ))
upload_metadata(job, "training_jobs/" + job.name, history)
finally:
job.cleanup()
def test_train_mlengine_copy(self):
train_job.train_job['name'] = str(uuid.uuid4())
job = Job(train_job.train_job['name'], train_job.train_job)
job.type = 'preprocess'
job.init_temp(str(uuid.uuid4()))
try:
logging.info("step1")
job.init_storage()
logging.info("step2")
if hasattr(job,
'source_training') and job.source_training is not '':
sjd = json.loads(
job.download_to_string('training_jobs/' +
job.source_training +
"/job_def.json"))
job.num_train_steps += sjd['num_train_steps']
job.model = sjd['model']
st = 'training_jobs/{}/'.format(job.source_training)
dt = 'training_jobs/{}/'.format(job.name)
job.copy_folder(st, dt)
job.delete_cloud_file('{}{}'.format(dt, "job_def.json"))
job.delete_cloud_file('{}{}'.format(dt, "job_history.json"))
logging.info("step3")
if not hasattr(job, 'label_file') or job.label_file is None:
job.label_file = 'corpus/' + job.prep_name + "/object-detection.pbtxt"
job.init_labels()
self.assertGreater(len(job.categories), 0)
logging.info("step4")
source = json.loads(
job.download_to_string('corpus/' + job.prep_name +
"/job_def.json"))
job.project_name = source['project_name']
logging.info("step5")
updateFileML(job)
logging.info("step6")
upload_model(job)
logging.info("step7")
upload_packages(job)
logging.info("step8")
start_ml_engine(job)
logging.info("step9")
history = json.loads(
job.download_to_string(
'corpus/' + job.prep_name + "/job_history.json", ))
upload_metadata(job, "training_jobs/" + job.name, history)
finally:
job.cleanup()