使用torch.cuda.max_memory_allocated()函数可以查看程序运行期间最大已分配的显存量。这可以帮助你了解程序在高峰时的显存使用情况。 python if torch.cuda.is_available(): max_allocated_memory = torch.cuda.max_memory_allocated(device=None) print(f"Max allocated GPU memory: {max_allocated_memory / (102...
在分析PyTorch的显存时候,一定要使用torch.cuda里的显存分析函数,我用的最多的是torch.cuda.memory_allocated()和torch.cuda.max_memory_allocated(),前者可以精准地反馈当前进程中torch.Tensor所占用的GPU显存(注意是只包括torch.Tensor),后者则可以告诉我们到调用函数为止所达到的最大的显存占用字节数。 还有像torch...
可以在cmd中输入nvidia-smi，但是通常情况下直接在cmd中输入nvidia-smi是没有用的，那该怎么办呢 找路...
but if there is a place in the code where the main max_memory_allocated counter is updated won't this require a relatively simple change where instead of updating a single counter, it will update as many counters as there are registered to be updated? And...
defmax_memory_allocated(device:Union[Device,int]=None)->int: Expand All@@ -325,7 +327,7 @@ def max_memory_allocated(device: Union[Device, int] = None) -> int: See :ref:`cuda-memory-management` for more details about GPU memory ...
class torch.cuda.device_of(obj) torch.cuda.empty_cache() torch.cuda.get_device_capability(device=None) torch.cuda.get_device_name(device=None) torch.cuda.init() torch.cuda.ipc_collect() torch.cuda.is_available() torch.cuda.max_memory_allocated(device=None) ...
# 需要导入模块: import torch [as 别名]# 或者: from torch importceil[as 别名]defdecode(self, data_loader):self.model.eval()withtorch.no_grad():forxs, frame_lens, filenamesindata_loader:# predict phones using AMifself.use_cuda:
# 需要导入模块: import torch [as 别名]# 或者: from torch importargmin[as 别名]defleast_used_cuda_device()-> Generator:"""Contextmanager for automatically selecting the cuda device with the least allocated memory"""mem_allocs = get_cuda_max_memory_allocations() ...
🐛 Bug The manpage https://pytorch.org/docs/stable/cuda.html#torch.cuda.memory_allocated advertises that it's possible to pass an int argument but it doesn't work. And even if I create a device argument it doesn't work correctly in multi-...
OutOfMemoryError: CUDA out of memory. Tried to allocate 646.00 MiB (GPU 0; 14.76 GiB total capacity; 12.35 GiB already allocated; 529.75 MiB free; 13.41 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See ...