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'process/gpu_sm_utilization': this_process.gpu_sm_utilization(), 'process/used_gpu_memory': float(this_process.gpu_memory()) / (1 << 20), # convert bytes to MiBs 'process/memory_percent': this_mory_percent(), 'process/cpu_percent': this_process.cpu_percent(), 'host/memory_percent': host.virtual_memory().percent,

'device/gpu_utilization': device.gpu_utilization(), 'device/memory_utilization': mory_utilization(), 'device/memory_used': float(mory_used()) / (1 << 20), # convert bytes to MiBs This_process = GpuProcess(os.getpid(), device) For example, integrate into PyTorch training code: import osįrom re import host, CudaDevice, HostProcess, GpuProcessįrom import SummaryWriter In addition, nvitop can be integrated into other applications. Nvitop comes with a tree-view screen and an environment screen: You can interrupt or kill your processes on the GPUs. Besides, it is responsive for user inputs in monitor mode.

Nvitop will show the GPU status like nvidia-smi but with additional fancy bars and history graphs.įor the processes, it will use psutil to collect process information and display the USER, %CPU, %MEM, TIME and COMMAND fields, which is much more detailed than nvidia-smi. Install the latest version from GitHub: pip3 install git+

Install from PyPI: pip3 install -upgrade nvitop It is written in pure Python and is easy to install. Recently, I have written a monitoring tool called nvitop, the interactive NVIDIA-GPU process viewer. | Fan Temp Power Usage /Cap | Memory Usage | GPU Util. There also is a list of compute processes and few more options but my graphic card (GeForce 9600 GT) is not fully supported. For Nvidia GPUs there is a tool nvidia-smi that can show memory usage, GPU utilization and temperature of GPU.