def create_simple(id, last_weights=False):
job_backend = JobBackend()
job_backend.load(id)
job_model = job_backend.get_job_model()
if last_weights:
weights_path = job_model.get_weights_filepath_latest()
else:
weights_path = job_model.get_weights_filepath_best()
if not os.path.exists(weights_path) or os.path.getsize(weights_path) == 0:
weight_url = job_backend.get_best_weight_url(id)
if not weight_url:
raise Exception("No weights available for this job.")
print(("Download weights %s to %s .." % (weight_url, weights_path)))
ensure_dir(os.path.dirname(weights_path))
f = open(weights_path, 'wb')
f.write(urllib.urlopen(weight_url).read())
f.close()
model.job_prepare(job_model)
general_logger = GeneralLogger(job_backend=job_backend)
trainer = Trainer(job_backend, general_logger)
job_model.set_input_shape(trainer)
model = job_model.get_built_model(trainer)
job_model.load_weights(model, weights_path)
return job_model, model
def start_model(self, parsed_args):
from aetros import keras_model_utils
from aetros.backend import JobBackend
from aetros.logger import GeneralLogger
from aetros.Trainer import Trainer
from aetros.keras_model_utils import ensure_dir
if not parsed_args.id:
print("No job id given.")
sys.exit(1)
print("...")
self.lock.acquire()
job_backend = JobBackend(parsed_args.id)
job_backend.load_light_job()
self.job_model = job_backend.get_job_model()
if parsed_args.weights:
weights_path = parsed_args.weights
elif parsed_args.latest:
weights_path = self.job_model.get_weights_filepath_latest()
else:
weights_path = self.job_model.get_weights_filepath_best()
print("Check weights ...")
if not os.path.exists(weights_path) or os.path.getsize(
weights_path) == 0:
weight_url = job_backend.get_best_weight_url(parsed_args.id)
if not weight_url:
print("No weights available for this job.")
exit(1)
print(
("Download weights %s to %s .." % (weight_url, weights_path)))
ensure_dir(os.path.dirname(weights_path))
f = open(weights_path, 'wb')
f.write(urllib.urlopen(weight_url).read())
f.close()
keras_model_utils.job_prepare(job_backend)
trainer = Trainer(job_backend)
self.job_model.set_input_shape(trainer)
print("Loading model ...")
model = self.job_model.get_built_model(trainer)
print(("Load weights %s ..." % (weights_path, )))
self.job_model.load_weights(model, weights_path)
print("Locked and loaded.")
self.lock.release()
return model
def start_model(self, parsed_args):
from aetros import keras_model_utils
from aetros.backend import JobBackend
from aetros.logger import GeneralLogger
from aetros.Trainer import Trainer
from aetros.keras_model_utils import ensure_dir
if not parsed_args.id:
print("No job id given.")
sys.exit(1)
print("...")
self.lock.acquire()
job_backend = JobBackend(parsed_args.id)
job_backend.load_light_job()
self.job_model = job_backend.get_job_model()
if parsed_args.weights:
weights_path = parsed_args.weights
elif parsed_args.latest:
weights_path = self.job_model.get_weights_filepath_latest()
else:
weights_path = self.job_model.get_weights_filepath_best()
print("Check weights ...")
if not os.path.exists(weights_path) or os.path.getsize(weights_path) == 0:
weight_url = job_backend.get_best_weight_url(parsed_args.id)
if not weight_url:
print("No weights available for this job.")
exit(1)
print(("Download weights %s to %s .." % (weight_url, weights_path)))
ensure_dir(os.path.dirname(weights_path))
f = open(weights_path, 'wb')
f.write(urllib.urlopen(weight_url).read())
f.close()
trainer = Trainer(job_backend)
self.job_model.set_input_shape(trainer)
print("Loading model ...")
model = self.job_model.get_built_model(trainer)
print(("Load weights %s ..." % (weights_path,)))
self.job_model.load_weights(model, weights_path)
print("Locked and loaded.")
self.lock.release()
return model