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,...
具体描述可以看Massively reduce LayerNorm/RMSNorm GPU memory usage in modern networks by tricking tor...
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, ...
主要关心的参数为Memory-Usage,如下图所示,1块GPU的显存都被极大的占用了,但是GPU-Util(GPU利用率为55),那么说明后台有进程在消耗GPU资源。 命令行输入fuser -v /dev/nvidia*,就会显示使用GPU的进程;把消耗资源的进程关闭即可:命令行输入kill 进程号,再次显示GPU的使用情况(其实可以根据nvidia-smi显示的最下面那...
PS 模式下的 DP,会造成负载不均衡,因为充当 server 的 GPU 需要一定的显存用来保存 worker 节点计算出的局部梯度;另外 server 还需要将更新后的模型参数 broadcast 到每个 worker,server 的带宽就成了 server 与worker 之间的通信瓶颈,server 与 worker 之间的通信成本会随着 worker 数目的增加而线性增加。
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 ...
In the following sections, I’ll cover some approaches to reduce GPU memory usage. Building a Completely CPU-based Pipeline Let’s look at the example CPU pipeline first. The CPU-based pipeline is useful for when peak throughput isn’t required (e.g., when working with medium & large size...
It will reduce memory usage and speed up computations but you won’t be able to backprop (which you don’t want in an eval script).model.eval() will notify all your layers that you are in eval mode, that way, batchnorm or dropout layers will work in eval mode instead of training ...
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...
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 OOMprint("GPU Usage after allcoating a bunch of Tensors")gpu_usage()delte...