optimizer.zero_grad()
def criterion(output, target, steps, batch_size):
loss = F.cross_entropy(output, target)
loss.backward()
return loss
Dennis Bappert dbpprt
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| import argparse | |
| import logging | |
| import os | |
| import torch | |
| import torch.distributed as dist | |
| import torch.nn.functional as F | |
| import torch.utils.data.distributed | |
| from torch.nn.utils import clip_grad_norm_ |
dbpprt
/ oom.md
Last active
September 6, 2021 13:27
A simple helper function to handle OOM errors while training with PyTorch. On my Windows system I sometimes get strange OutOfMemory errors in the middle of a training job. This wrapper tries to recover by freeing up as much memory as possible and splits the batches into half.