def from_params( # type: ignore
cls,
params: Params,
serialization_dir: str,
recover: bool = False,
cache_directory: str = None,
cache_prefix: str = None,
) -> "CallbackTrainer":
pieces = TrainerPieces.from_params(params, serialization_dir, recover,
cache_directory, cache_prefix)
model = pieces.model
params = pieces.params
validation_iterator = pieces.validation_iterator or pieces.iterator
shuffle = params.pop_bool("shuffle", True)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = parse_cuda_device(params.pop("cuda_device", -1))
if isinstance(cuda_device, list):
model_device = cuda_device[0]
else:
model_device = cuda_device
if model_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(model_device)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer = Optimizer.from_params(parameters, params.pop("optimizer"))
callbacks_params = params.pop("callbacks", [])
callbacks: List[Callback] = [
Callback.from_params(
params=callback_params,
model=model,
optimizer=optimizer,
instances=pieces.train_dataset,
iterator=pieces.iterator,
shuffle=shuffle,
validation_data=pieces.validation_dataset,
validation_iterator=validation_iterator,
serialization_dir=serialization_dir,
) for callback_params in callbacks_params
]
params.assert_empty(cls.__name__)
return cls(
model,
pieces.train_dataset,
pieces.iterator,
optimizer,
num_epochs=num_epochs,
shuffle=shuffle,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
callbacks=callbacks,
)
def from_params( # type: ignore
cls,
params: Params,
serialization_dir: str,
recover: bool = False,
) -> "CallbackTrainer":
pieces = TrainerPieces.from_params(params, serialization_dir, recover)
model = pieces.model
params = pieces.params
validation_iterator = pieces.validation_iterator or pieces.iterator
shuffle = params.pop_bool("shuffle", True)
num_epochs = params.pop_int("num_epochs", 20)
cuda_device = parse_cuda_device(params.pop("cuda_device", -1))
check_for_gpu(cuda_device)
if cuda_device >= 0:
# Moving model to GPU here so that the optimizer state gets constructed on
# the right device.
model = model.cuda(cuda_device)
parameters = [[n, p] for n, p in model.named_parameters()
if p.requires_grad]
optimizer = Optimizer.from_params(parameters, params.pop("optimizer"))
callbacks_params = params.pop("callbacks", [])
callbacks: List[Callback] = [
Callback.from_params(
params=callback_params,
model=model,
optimizer=optimizer,
instances=pieces.train_dataset,
iterator=pieces.iterator,
shuffle=shuffle,
validation_data=pieces.validation_dataset,
validation_iterator=validation_iterator,
serialization_dir=serialization_dir,
) for callback_params in callbacks_params
]
distributed = params.pop_bool("distributed", False)
world_size = params.pop_int("world_size", 1)
if distributed:
rank = cuda_device
else:
rank = 0
params.assert_empty(cls.__name__)
return cls(
model,
pieces.train_dataset,
pieces.iterator,
optimizer,
num_epochs=num_epochs,
shuffle=shuffle,
serialization_dir=serialization_dir,
cuda_device=cuda_device,
callbacks=callbacks,
distributed=distributed,
rank=rank,
world_size=world_size,
)