multi gpu (2) 썸네일형 리스트형 [pytorch] DistributedDataParallel vs DataParallel 차이 The difference between DistributedDataParallel and DataParallel is: DistributedDataParallel uses multiprocessing where a process is created for each GPU, while DataParallel uses multithreading. By using multiprocessing, each GPU has its dedicated process, this avoids the performance overhead caused by GIL of Python interpreter. If you use DistributedDataParallel, you could use torch.distributed... pytorch 에러 DistributedDataParallel 에러 if self.reducer._rebuild_buckets(): RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by (1) passing the keyword argumentere not used in producing loss. You can enable unused parameter detection by (1) passing the k.. 이전 1 다음
