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...
print("GPU usage:", torch.cuda.device_count()) 在上述示例中,我们首先导入了torch库,并创建了一个10x10的随机张量。然后,使用torch.cuda.memory_allocated()函数查看内存占用,使用torch.cuda.device_count()函数查看GPU使用情况。详细介绍PyTorch提供了多个函数和属性来查看内存占用和GPU使用情况。下面详细介绍这些...
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...
Tracking Memory Usage with GPUtil 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...
| NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page)| | Chip | Bus-Id | AICore(%) Memory-Usage(MB) HBM-Usage(MB) | +===+===+===+ | 0 910B1 | OK | 95.7 36 0 / 0 | | 0 | 0000:C1:00.0 | 0 0 / 0 3306 / 65536 | +===+===...
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(); ...
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...
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...
model(model,train_dataloader) val_acc,val_loss=test_model(model,val_dataloader) #Check memory usage. handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0) info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle) memory_used=info.used memory_used=(memory_used/1024)/1024 print(f"Epoc...