def post(self):
"""
Executes a prep job to create an image corpus for training.
Use this method to start a prep job.
"""
job_def = request.json
job_def['process_json'] = True # Hardcode to process json file from project folder
job = Job(job_def['name'],job_def)
job.type = 'preprocess'
dt = newdt.now()
job.start_time = int(dt.timestamp()*1000)
job.request = {'full_path': request.full_path,'remote_addr':request.remote_addr,'method':request.method}
jb = aug_queue.enqueue(
preprocess, job,job_timeout=-1,result_ttl=86400,ttl=-1)
jb.meta['job_def'] = job_def
dt = newdt.now()
jb.meta['job_init_time'] = str(int(dt.timestamp()*1000))
jb.status = 'Running'
jb.save_meta()
json_str = job.to_json_string()
st = {
'BUCKET' : job.bucket,
'USE_GCS' : job.use_gcs,
'ACCESS_KEY' : access_key,
'SECRET_KEY' : secret_key,
'S3_URL' : s3_url
}
storage = Storage(st)
storage.upload_data(json_str,'jobs/running/{}_0_preprocess_r_{}.json'.format(str(job.start_time),jb.id),contentType='application/json')
storage.upload_data(json_str,'jobs/all/{}_0_preprocess_r_{}.json'.format(str(job.start_time),jb.id),contentType='application/json')
return {
"status": jb.status,
'job_id': jb.id,
'meta':jb.meta},201
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()
def post(self):
"""
Executes a training.
Use this method to start a training.
"""
job_def = request.json
job = Job(job_def['name'], job_def)
job.type = 'train'
dt = newdt.now()
job.start_time = int(dt.timestamp() * 1000)
job.request = {
'full_path': request.full_path,
'remote_addr': request.remote_addr,
'method': request.method
}
if hasattr(job, 'ml_engine') and job.ml_engine:
jb = train_queue.enqueue(train_mlengine,
job,
job_timeout=-1,
result_ttl=-1)
else:
jb = train_queue.enqueue(train_job_method, job, job_timeout=-1)
jb.meta['job_init_time'] = str(int(dt.timestamp() * 1000))
jb.meta['job_def'] = job_def
jb.save_meta()
json_str = job.to_json_string()
st = {
'BUCKET': job.bucket,
'USE_GCS': job.use_gcs,
'ACCESS_KEY': access_key,
'SECRET_KEY': secret_key,
'S3_URL': s3_url
}
storage = Storage(st)
storage.upload_data(json_str,
'jobs/running/{}_0_train_r_{}.json'.format(
str(job.start_time), jb.id),
contentType='application/json')
storage.upload_data(json_str,
'jobs/all/{}_0_train_r_{}.json'.format(
str(job.start_time), jb.id),
contentType='application/json')
return {
"status": jb.get_status(),
'job_id': jb.id,
'meta': jb.meta
}, 201
def test_process_all(self):
for jb in prep_job.jobs:
job = Job(jb['name'], jb)
job.type = 'preprocess'
job.init_temp(str(uuid.uuid4()))
try:
job.init_labels()
job.init_storage()
job.testcoco = {"info": {
"description": "COCO 2017 Dataset",
"url": "http://cocodataset.org",
"version": "1.0",
"year": 2018,
"contributor": "COCO Consortium",
"date_created": "2017/09/01"
},
"licenses": [],
"images": [],
"categories": [],
"annotations": [],
}
job.traincoco = {"info": {
"description": "COCO 2017 Dataset",
"url": "http://cocodataset.org",
"version": "1.0",
"year": 2018,
"contributor": "COCO Consortium",
"date_created": "2017/09/01"
},
"licenses": [],
"images": [],
"categories": [],
"annotations": [],
}
process_json(job)
create_label_pbtxt(job)
create_tf_example(job)
create_tf_example(job, False)
delete_staged(job)
upload_metadata(job)
finally:
job.cleanup()
def test_init_storage(self):
for jb in prep_job.jobs:
job = Job(jb['name'], jb)
job.type = 'preprocess'
job.init_temp(str(uuid.uuid4()))
try:
job.init_labels()
job.init_storage()
job.testcoco = {"info": {
"description": "COCO 2017 Dataset",
"url": "http://cocodataset.org",
"version": "1.0",
"year": 2018,
"contributor": "COCO Consortium",
"date_created": "2017/09/01"
},
"licenses": [],
"images": [],
"categories": [],
"annotations": [],
}
job.traincoco = {"info": {
"description": "COCO 2017 Dataset",
"url": "http://cocodataset.org",
"version": "1.0",
"year": 2018,
"contributor": "COCO Consortium",
"date_created": "2017/09/01"
},
"licenses": [],
"images": [],
"categories": [],
"annotations": [],
}
finally:
job.cleanup()
