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, ...
One way to track GPU usage is by monitoring memory usage in a console with the nvidia-smi command. The problem with this approach is that peakGPUusage and out-of-memory happen so fast that you can’t quite pinpoint which part of your code is causing the memory overflow. For this, we ...
例如：C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin2.4.2复制粘贴文件里“include...
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...
summary: Print a summary of memory allocation statistics. delayed_free: Delay freeing memory blocks to reduce memory fragmentation. initial_pool_size:<size>: Set the initial size of the memory pool in bytes. For example, you can set CUDA_ALLOC_CONF to enable the memory pool allocator and pri...
具体描述可以看Massively reduce LayerNorm/RMSNorm GPU memory usage in modern networks by tricking ...
# and no extra memory usage torch.compile(model)# reduce-overhead:optimizes to reduce the framework overhead # and uses some extra memory.Helps speed up small models torch.compile(model,mode="reduce-overhead")# max-autotune:optimizes to produce the fastest model,# but takes a very long ...
上文已经分析了如何启动/接受反向传播,如何进入分布式autograd 引擎,本文和下文就看看如何分布式引擎如何运作。通过本文的学习,读者可以对 dist.autograd 引擎基本静态架构和总体执行逻辑有所了解。 0x01 支撑系统 我们首先看看一些引擎内部支撑系统。 1.1 引擎入口 ...
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 ...