importGPUtildefcheck_gpu_usage():gpus=GPUtil.getGPUs()forgpuingpus:print(f"GPU{gpu.id}:")print(f" Utilization:{gpu.memoryUtil*100}%")print(f" Free Memory:{gpu.memoryFree}MB")print(f" Used Memory:{gpu.memoryUsed}MB")print(f" Temperature:{gpu.temperature}°C")check_gpu_usage() 1....
这段代码首先初始化pynvml库,然后获取NVIDIA GPU的数量,并遍历每个GPU以获取其显存的总量和已使用量,最后关闭pynvml库。 使用GPUtil库 安装GPUtil库: 使用pip安装GPUtil库。 bash pip install GPUtil 编写Python脚本来获取GPU显存占用: python import GPUtil def check_gpu_usage(): gpus = GPUtil.getGPUs() for...
下面是一个简单的Python代码示例,用来获取GPU的使用率: importsubprocessdefget_gpu_usage():result=subprocess.check_output(['nvidia-smi','--query-gpu=utilization.gpu','--format=csv,noheader,nounits'])gpu_usage=[int(i)foriinresult.strip().split()]returngpu_usageif__name__=='__main__':gpu_...
import torch import inspect from torchvision import models from gpu_mem_track import MemTracker # 引用显存跟踪代码 device = torch.device('cuda:0') frame = inspect.currentframe() gpu_tracker = MemTracker(frame) # 创建显存检测对象 gpu_tracker.track() # 开始检测 cnn = models.vgg19(pretrained=T...
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===+===+===| | 0 NVIDIA GeForce ... Off | 00000000:01:00.0 N/A | N/A | | N/A 51C P8 N/A / N/...
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===+===+===| | 0 NVIDIA GeForce ... Off | 00000000:01:00.0 N/A | N/A | | N/A 51C P8 N/A / N/...
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===+===+===| | 0 Tesla P40 Off | 00000000:88:00.0 Off | 0 | | N/A 26C P8 10W / 250W | 10MiB / 22919...
main 克隆/下载 git config --global user.name userName git config --global user.email userEmail 分支285 标签3781 yibrenthFix minor heartbeat memory leak (#10892)bfb7aae1天前 7582 次提交 提交 .changeset Fix minor heartbeat memory leak (#10892) ...
本文介绍了基于Python的分布式框架Ray的基本安装与使用。Ray框架下不仅可以通过conda和Python十分方便的构建一个集群,还可以自动的对分布式任务进行并发处理,且支持GPU分布式任务的提交,极大的简化了手动分布式开发的工作量。
= 1# Enable fp16/bf16 training (set bf16 to True with an A100)fp16 = Falsebf16 = True# Batch size per GPU for trainingper_device_train_batch_size = 4# Number of update steps to accumulate the gradients forgradient_accumulation_steps = 1# Enable gradient checkpointinggradient_check...