def builder(self, *params):
args = dict(zip([p.name for p in self.parameters], params))
try:
model_pytorch = self.model_fn(**args)
return ModelPytorch(None, source=model_pytorch)
except:
str_param = ','.join('{0}={1!r}'.format(k, v)
for k, v in args.items())
print("Failed to build model with params: {}".format(str_param))
return None
def builder(self, *params):
args = dict(zip([p.name for p in self.parameters], params))
try:
model_pytorch = self.model_fn(**args)
## save it to a temp file indeed
username = os.environ.get('USER')
os.system('mkdir -p /tmp/{}'.format(username))
args_s = str(args).encode('utf-8')
hashs = hashlib.sha224(args_s).hexdigest()
model_path = "/tmp/{}/_{}_{}_pytorch.torch".format(
username, os.getpid(), hashs)
torch.save(model_pytorch, model_path)
return ModelPytorch(None, filename=model_path, gpus=self.gpus)
except:
str_param = ','.join('{0}={1!r}'.format(k, v)
for k, v in args.items())
print("Failed to build model with params: {}".format(str_param))
return None
hide_device = True
if args.torch:
logging.debug("Using pytorch")
if not args.optimizer.endswith("torch"):
args.optimizer = args.optimizer + 'torch'
import torch
if hide_device:
os.environ[
'CUDA_VISIBLE_DEVICES'] = device[-1] if 'gpu' in device else ''
logging.debug('set to device %s',
os.environ['CUDA_VISIBLE_DEVICES'])
else:
if 'gpu' in device:
torch.cuda.set_device(int(device[-1]))
model_builder = ModelPytorch(comm,
source=args.model,
weights=model_weights,
gpus=1 if 'gpu' in device else 0)
else:
if args.tf:
logging.debug("Using TensorFlow")
backend = 'tensorflow'
if not args.optimizer.endswith("tf"):
args.optimizer = args.optimizer + 'tf'
if hide_device:
os.environ['CUDA_VISIBLE_DEVICES'] = device[
-1] if 'gpu' in device else ''
logging.debug('set to device %s',
os.environ['CUDA_VISIBLE_DEVICES'])
else:
logging.debug("Using Theano")
backend = 'theano'
val_list = [s.strip() for s in val_list_file.readlines()]
comm = MPI.COMM_WORLD.Dup()
# Theano is the default backend; use tensorflow if --tf is specified.
# In the theano case it is necessary to specify the device before importing.
device = get_device(comm,
args.masters,
gpu_limit=args.max_gpus,
gpu_for_master=args.master_gpu)
hide_device = True
if args.torch:
print("Using pytorch")
import torch
model_builder = ModelPytorch(comm,
filename=args.model_json,
weights=args.model_weights,
gpus=1)
else:
if args.tf:
backend = 'tensorflow'
if hide_device:
os.environ['CUDA_VISIBLE_DEVICES'] = device[
-1] if 'gpu' in device else ''
print('set to device', os.environ['CUDA_VISIBLE_DEVICES'])
else:
backend = 'theano'
os.environ[
'THEANO_FLAGS'] = "profile=%s,device=%s,floatX=float32" % (
args.profile, device.replace('gpu', 'cuda'))
os.environ['KERAS_BACKEND'] = backend
def builder(self, *params):
args = dict(zip([p.name for p in self.parameters], params))
model_pytorch = self.model_fn(**args)
return ModelPytorch(None, filename=model_pytorch, gpus=self.gpus)
args.masters,
gpu_limit=args.max_gpus,
gpu_for_master=args.master_gpu)
hide_device = True
if args.torch:
print("Using pytorch")
import torch
if hide_device:
os.environ[
'CUDA_VISIBLE_DEVICES'] = device[-1] if 'gpu' in device else ''
print('set to device', os.environ['CUDA_VISIBLE_DEVICES'])
else:
if 'gpu' in device:
torch.cuda.set_device(int(device[-1]))
model_builder = ModelPytorch(comm,
filename=args.model_json,
weights=model_weights,
gpus=1 if 'gpu' in device else 0)
else:
if args.tf:
backend = 'tensorflow'
if not args.optimizer.endswith("tf"):
args.optimizer = args.optimizer + 'tf'
if hide_device:
os.environ['CUDA_VISIBLE_DEVICES'] = device[
-1] if 'gpu' in device else ''
print('set to device', os.environ['CUDA_VISIBLE_DEVICES'])
else:
backend = 'theano'
os.environ[
'THEANO_FLAGS'] = "profile=%s,device=%s,floatX=float32" % (
args.profile, device.replace('gpu', 'cuda'))