Comments (2)
The function below (numpy to tensor conversion) likely causes the memory leak:
"""in rlcycle/common/utils/common_utils.py"""
def np2tensor(np_arr: np.ndarray, device: torch.device):
"""Convert numpy array to tensor"""
tensor_output = torch.FloatTensor(np_arr).to(device)
if device.type == "cuda":
tensor_output.cuda(non_blocking=True)
return tensor_output
def np2tensor2(np_arr: np.ndarray, device: torch.device):
"""Convert numpy array to tensor"""
tensor_output = torch.from_numpy(np_arr).float().to(device)
if device.type == "cuda":
tensor_output.cuda(non_blocking=True)
return tensor_output
from rlcycle.
Fixed memory leak in PR #12
Here's what changed:
- Numpy to tensor conversion:
def np2tensor(np_arr: np.ndarray, use_cuda: bool):
"""Convert numpy array to tensor"""
if use_cuda:
return torch.from_numpy(np_arr).cuda(non_blocking=True).float()
return torch.from_numpy(np_arr).cpu().float()
This was recommended by Pytorch in this discussion thread.
-
Use
cuda()
instead `.to(device)' to cast cuda tensors. Recommended by Pytorch in this discussion thread. -
Added a memory profiler for debugging.
from rlcycle.
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