可以通过 Python 的psutil库来进行监控。 importpsutilimporttime# 获取当前内存使用情况defcheck_memory_usage():process=psutil.Process()memory_use=process.memory_info().rss/(1024**2)# 转换为 MBreturnmemory_usewhileTrue:print(f"当前内存使用量:{check_memory_usage()}MB")time.sleep(5)# 每 5 秒检...
在深度学习模型训练过程中,在服务器端或者本地pc端,输入nvidia-smi来观察显卡的GPU内存占用率(Memory-Usage),显卡的GPU利用率(GPU-util),然后采用top来查看CPU的线程数(PID数)和利用率(%CPU)。往往会发现很多问题,比如,GPU内存占用率低,显卡利用率低,CPU百分比低等等。接下来仔细分析这些问题和处理办法。 1. GP...
#Check memory. handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0) info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle) memory_used=info.used memory_used=(memory_used/1024)/1024 print(f"Epoch={epoch} Train Accuracy={train_acc} Train loss={train_loss} Validation accuracy={val_acc} Validation loss...
val_acc,val_loss=test_model(model,val_dataloader)#Checkmemory usage. handle=nvidia_smi.nvmlDeviceGetHandleByIndex(0) info=nvidia_smi.nvmlDeviceGetMemoryInfo(handle) memory_used=info.usedmemory_used=(memory_used/1024)/1024print(f"Epoch={epoch} Train Accuracy={train_acc} Train loss={train_loss...
time() - s) / NITER * 1000) print("check res cosine_similarity") assert ( torch.nn.functional.cosine_similarity( res.flatten(), res_compiled.flatten(), dim=0 ) > 0.9999 ) 测试结果如下,输入都是torch.randn(1,3,1024,1024).cuda(),其中reduce-overhead和max-autotune为torch.compile函数...
self._check_global_requires_backward_grad_sync(is_joined_rank=False) #!!! if self.device_ids: inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) if len(self.device_ids) == 1: output = self.module(*inputs[0], **kwargs[0]) else: # 单进程多线程多卡的情况 outputs ...
import torch import torch.nn as nn import torch.optim as optim import torchvision.transforms as transforms import torchvision.datasets as datasets # Check if GPU is available, and if not, use the CPU device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 加载CIFAR-10 CIFA...
Fix torch.nn.ConstantPadNd not preserving memory format (#50898) Fix dtype of first sample in torch.quasirandom.SobolEngine (#51578) Fixes bug in torch.sspaddmm (#45963) Check support_as_strided before using torch.empty_strided (#46746) Fix internal assert for torch.heaviside with cuda ...
torch.autograd.gradcheck(Fn.apply, (primal,), check_forward_ad=True, check_backward_ad=False, check_undefined_grad=False, check_batched_grad=False) True 功能API(beta) 我们还提供了 functorch 中用于计算雅可比向量积的更高级功能 API,根据您的用例,您可能会发现更简单使用。 功能API 的好处是不...
TORCH_CHECK(self_.sizes() == other_.sizes()); TORCH_INTERNAL_ASSERT(self_.device().type() == DeviceType::CPU); TORCH_INTERNAL_ASSERT(other_.device().type() == DeviceType::CPU); Tensor self = self_.contiguous(); Tensor other = other_.contiguous(); ...