torch.cuda. empty_cache ()[source]. Releases all unoccupied cached memory currently held by the caching allocator so that those can be used in other GPU ...
torch.cuda.empty_cache() [source] Releases all unoccupied cached memory currently held by the caching allocator so that those can be used in other GPU application and visible in nvidia-smi. Note. empty_cache () doesn’t increase the amount of GPU memory available for PyTorch. However, it may help reduce fragmentation of GPU memory in certain ...
Pytorch lightning calls torch.cuda.emptycache() at times, e.g. at the end of the ... If the cache is emptied in this way, it will not allocate memory on any ...
05.09.2019 · 🐛 Bug I have 2 gpus, when I clear data on gpu1, empty_cache() always write ~500M data to gpu0. I observe this in torch 1.0.1.post2 and 1.1.0. To Reproduce The following code will reproduce the behavior: After torch.cuda.empty_cache(), ~5...
09.01.2019 · About torch.cuda.empty_cache () lixin4ever January 9, 2019, 9:16am #1. Recently, I used the function torch.cuda.empty_cache () to empty the unused memory after processing each batch and it indeed works (save at least 50% memory compared to the code not using this function). At the same time, the time cost does not increase too much and the ...
20.10.2020 · 🐛 Bug When I train a model the tensors get kept in GPU memory. The command torch.cuda.empty_cache() "releases all unused cached memory from PyTorch so that those can be used by other GPU applications" which is great, but how do you clear...
12.11.2019 · Currently, I am using PyTorch built with CPU only support. When I run inference, somehow information for that input file is stored in cache and memory keeps on increasing for every new unique file used for inference. On the other hand, memory usage does not increase if i use the same file again and again. Is there a way to clear cache like cuda.empty_cache() in …
21.02.2021 · The code to be instrumented is this. for i, batch in enumerate (self.test_dataloader): # torch.cuda.empty_cache () # torch.synchronize () # if empty_cache is used # start timer for copy batch = tuple (t.to (device) for t in batch) # to GPU (or CPU) when gpu torch.cuda.synchronize () # stop timer for copy b_input_ids, b_input_mask, b_labels ...