importGPUtilimporttimedefprint_memory_usage():# 获取所有可用的GPU信息gpus=GPUtil.getGPUs()# 遍历每个GPU的信息forgpuingpus:print(f"GPU ID:{gpu.id}")print(f"显存总量:{gpu.memoryTotal}MB")print(f"已用显存:{gpu.memoryUsed}MB")print(f"剩余显存:{gpu.memoryFree}MB")print(f"显存使用率:{gp...
# setting device on GPU if available, else CPU device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Using device:', device) print() #Additional Info when using cuda if device.type == 'cuda': print(torch.cuda.get_device_name(0)) print('Memory Usage:') pr...
id}:") print(f" Memory Used: {gpu.memoryUsed} MB") print(f" Memory Utilization: {gpu.memoryUtil * 100:.2f}%") check_gpu_usage() 这段代码使用GPUtil库来获取GPU的ID、已使用的显存量和显存利用率,并打印出来。 总结 以上两种方法都可以用来在Python中获取GPU显存的占用情况。选择哪种方法取决...
gpu_print[4, 4]() cuda.synchronize() cpu_print() 这个代码主要有两个函数,一个是用CPU执行,一个是用GPU执行,执行打印操作。关键在于@cuda.jit这个注解,让代码在GPU上执行。运行结果如下: $ /home/larry/anaconda3/bin/python /home/larry/code/pkslow-samples/python/src/main/python/cuda/print_test....
importtorch#setting device on GPU if available, else CPUdevice = torch.device('cuda'iftorch.cuda.is_available()else'cpu')print('Using device:', device)print()#Additional Info when using cudaifdevice.type =='cuda':print(torch.cuda.get_device_name(0))print('Memory Usage:')print('Allocated...
().total - psutil.virtual_memory().free) / float(psutil.virtual_memory().total)# 获取硬盘# for i in psutil.disk_partitions():## o = psutil.disk_usage(i.device)# print("盘的名称:", i.device)# print("fs类型:", i.fstype)# print("fs权限:", i.opts)# print(f"全部:{o.total}...
gpu_print[1, 2]() File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/compiler.py", line 862, in __getitem__ return self.configure(*args) File "/home/larry/anaconda3/lib/python3.9/site-packages/numba/cuda/compiler.py", line 857, in configure ...
---+|Processes:||GPUGICIPIDType Process nameGPUMemory||IDIDUsage||===||0N/AN/A1673G/usr/lib/xorg/Xorg 110MiB||0N/AN/A3015G/usr/lib/xorg/Xorg 661MiB||0N/AN/A3251G/usr/bin/gnome-shell 132MiB||0N/AN/A1142734G...AAAAAAAAA=--shared-files 64MiB||0N/...
python可以监控GPU程序，比如获取显卡的显存，可用显存，已用显存，pid等信息，具体可以查看笔记python监控...