torch1.6, cuda10.2, 驱动440 参数设置:shuffle=True, num_workers=8, pin_memory=True; 现象1:该代码在另外一台电脑上,可以将GPU利用率稳定在96%左右 现象2:在个人电脑上,CPU利用率比较低,导致数据加载慢,GPU利用率浮动,训练慢约4倍;有意思的是,偶然开始训练时,CPU利用率高,可以让GPU跑起来,但仅仅几分钟,...
1: 这个nvidia forum中提到,应该是GPU默认打开了ECC(error correcting code, 错误检查和纠正),会占用显存和降低显卡性能,打开Persistence Mode Enabled(用root执行nvidia-smi -pm 1)后5、6号显卡的显卡使用率恢复正常水平,问题解决。 2:对于DataLoader函数而言: torch.utils.data.DataLoader(dataset, batch_size=1, ...
By setting the CUDA_ALLOC_CONF environment variable, users can modify the behavior of the memory allocator to better suit their specific requirements. This can help improve memory usage efficiency, reduce memory fragmentation, and potentially enhance the performance of PyTorch applications running on GPU...
During my training process, I have a validation step, if I add validation after each epoch of training, the memory cost will almost be doubled! Even if I use the same network object, I just call net.eval() and the forward function. So I have to reduce batch_size if I enable ...
这个应该是你的bug导致的，如果你已经安装了cuda，且代码能获取到cuda，那就肯定能使用gpu训练。这种情况...
This will make sure that the space held by the process is released. importtorchfromGPUtilimportshowUtilizationasgpu_usageprint("Initial GPU Usage")gpu_usage()tensorList=[]forxinrange(10):tensorList.append(torch.randn(10000000,10).cuda())# reduce the size of tensor if you are getting OOM...
This can reduce peak memory usage, where the saved memory size will be equal to the total gradients size. Moreover, it avoids the overhead of copying between gradients and allreduce communication buckets. When gradients are views, detach_() cannot be called on the gradients. If hitting such...
Reduce memory usage for torch.mm when only one input requires gradient (#45777) Reduce autograd engine startup cost (#47592) Make torch.svd backward formula more memory and computationally efficient. (#50109) CUDA Fix perfornance issue of GroupNorm on CUDA when feature map is small. (#4617...
activations can consume significant GPU memory during training. Activation offloading is a technique that instead moves these tensors to CPU memory after the forward pass and later fetches them back to GPU when they are needed. This approach can substantially red...
通过搭建本地的k8s GPU环境,可以方便的进行AI相关的开发和测试,也能充分利用闲置的笔记本GPU性能。利用kueue、karmada、kuberay和ray等框架,让GPU等异构算力调度在云原生成为可能。 概述 Kubernetes的核心优势在于其能够提供一个可扩展、灵活且高度可配置的平台,使得应用程序的部署、扩展和管理变得前所未有的简单。通用...