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...
| 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 | +===+===...
The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. We've written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before. Ext...
)# Calling _rebuild_buckets before forward compuation,# It may allocate new buckets before deallocating old buckets# inside _rebuild_buckets. To save peak memory usage,# call _rebuild_buckets before the peak memory usage increases# during forward computation.# This should be called only once dur...
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...
As part of an overall effort to simplify our public facing API's for Distributed Checkpointing, we've decided to deprecate usage of the coordinator_rank and no_dist parameters under torch.distributed.checkpoint. In our opinion, these parameters can lead to confusion around the intended effect dur...