torch.cuda.caching_allocator_delete(mem) self.assertEqual(torch.cuda.memory_allocated(), prev)def test_check_error(self): # Assert this call doesn't raise. torch.cuda.check_error(0)with self.assertRaisesRegex( torch.cuda.CudaError, "out of memory|hipErrorOutOfMemory" ...
with torch.no_grad(): # 使用model进行预测的代码 pass 感谢@zhaz 的提醒,我把 torch.cuda.empty_cache() 的使用原因更新一下...the caching allocator so that those can be used in other GPU application and visible innvidia-smi. torch.cuda.empty_cache...而 torch.cuda.empty_cache() 的作用就...
pytorch/c10/cuda/CUDACachingAllocator.cpp Lines 821 to 844 in 23fffb5 struct PrivatePool { PrivatePool() : large_blocks(/*small=*/false, this), small_blocks(/*small=*/true, this) {} PrivatePool(const PrivatePool&) = delete; PrivatePool(PrivatePool&&) = delete; PrivatePool...
return torch_npu._C._npu_npuCachingAllocator_raw_alloc(size, stream) def caching_allocator_delete(mem_ptr): r"""Deletes memory allocated using the NPU memory allocator.Memory allocated with :func:`~torch_npu.npu.caching_allocator_alloc`.is...
torch.FatalError: cuda runtime error (2) : out of memory at /opt/conda/conda-bld/pytorch_1524590031827/work/aten/src/THC/generic/THCStorage.cu:58 想必这是所有炼丹师们最不想看到的错误,没有之一.显卡、显卡驱动、显存、GPU、CUDA、cuDNN 显卡 Video card,Graphics card,又叫显示接口卡,是...
The caching allocator can be configured via ENV to not split blocks larger than a defined size (see Memory Management section of the Cuda Semantics documentation). This helps avoid memory framentation but may have a performance penalty. Additional outputs to assist with tuning and evaluating imp...
The associated device and stream are tracked inside the allocator. Args: mem_ptr (int): memory address to be freed by the allocator. .. note:: See :ref:`cuda-memory-management` for more details about GPU memory management. """ torch._C._cuda_cudaCachingAllocator_raw_delete(mem_ptr) ...
Summary configure_torch_cuda_allocator changes Log warning instead of raising when PYTORCH_CUDA_ALLOC_CONF is set to a different value than is configured in invokeai.yaml. Log info instead of rais...
The caching allocator can be configured via ENV to not split blocks larger than a defined size (see Memory Management section of the Cuda Semantics documentation). This helps avoid memory framentation but may have a performance penalty. Additional outputs to assist with tuning and evaluating imp...
The caching allocator can be configured via ENV to not split blocks larger than a defined size (see Memory Management section of the Cuda Semantics documentation). This helps avoid memory framentation but may have a performance penalty. Additional outputs to assist with tuning and evaluating imp...