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Enable user to specify torch device in run_training #1943

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Enable user to specify torch device in run_training #1943

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10 changes: 9 additions & 1 deletion nnunetv2/run/run_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -249,9 +249,17 @@ def run_training_entry():
help="Use this to set the device the training should run with. Available options are 'cuda' "
"(GPU), 'cpu' (CPU) and 'mps' (Apple M1/M2). Do NOT use this to set which GPU ID! "
"Use CUDA_VISIBLE_DEVICES=X nnUNetv2_train [...] instead!")
parser.add_argument('--gpu_index', type=int, default=None, required=False,
help="Index of the GPU to use.")
args = parser.parse_args()

assert args.device in ['cpu', 'cuda', 'mps'], f'-device must be either cpu, mps or cuda. Other devices are not tested/supported. Got: {args.device}.'
if args.gpu_index is not None:
assert args.device == 'cuda', 'The GPU index can only be selected when running on a GPU'

assert args.gpu_index < torch.cuda.device_count(), (f'Specified gpu index {args.gpu_index} is out of range of '
f'available GPUs {torch.cuda.device_count()}')

if args.device == 'cpu':
# let's allow torch to use hella threads
import multiprocessing
Expand All @@ -261,7 +269,7 @@ def run_training_entry():
# multithreading in torch doesn't help nnU-Net if run on GPU
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
device = torch.device('cuda')
device = torch.device('cuda:'+str(args.gpu_index))
else:
device = torch.device('mps')

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7 changes: 2 additions & 5 deletions nnunetv2/training/nnUNetTrainer/nnUNetTrainer.py
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Expand Up @@ -97,11 +97,8 @@ def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dic
f"{dist.get_world_size()}."
f"Setting device to {self.device}")
self.device = torch.device(type='cuda', index=self.local_rank)
else:
if self.device.type == 'cuda':
# we might want to let the user pick this but for now please pick the correct GPU with CUDA_VISIBLE_DEVICES=X
self.device = torch.device(type='cuda', index=0)
print(f"Using device: {self.device}")

print(f"Using device: {self.device}")

# loading and saving this class for continuing from checkpoint should not happen based on pickling. This
# would also pickle the network etc. Bad, bad. Instead we just reinstantiate and then load the checkpoint we
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