- #Nvidia performance monitor install
- #Nvidia performance monitor license
- #Nvidia performance monitor free
USER PGRP PID %CPU %MEM STARTED TIME COMMAND Which has output like: Every 0.1s: ps f -o user,pgrp,pid,pcpu,pmem,start,time,command -p `sudo lsof -n -w -t /dev/nvi. So, in the end, it looks like: watch -n 0.1 'ps f -o user,pgrp,pid,pcpu,pmem,start,time,command -p `sudo lsof -n -w -t /dev/nvidia*`' Lastly, I combine it with watch to get a continuous update. To open it up to all processes owned by any user, I add a sudo before the lsof. One disadvantage, though, is it's limited to processes owned by the user that executes the command. One advantage of this over nvidia-smi is that it'll show process forks as well as main processes that use the GPU. That one is similar to just doing ps u but adds the process group ID and removes some other fields. ps f shows nice formatting for child/parent process relationships / hierarchies, and -o specifies a custom formatting. retrieves a list of all processes using an nvidia GPU owned by the current user, and ps -p. That'll show all nvidia GPU-utilizing processes and some stats about them. I use this one a lot: ps f -o user,pgrp,pid,pcpu,pmem,start,time,command -p `lsof -n -w -t /dev/nvidia*` |=|ĮDIT: In latest NVIDIA drivers, this support is limited to Tesla Cards.Īnother useful monitoring approach is to use ps filtered on processes that consume your GPUs. | Fan Temp Power Usage /Cap | Memory Usage | GPU Util. On linux, nVidia-smi 295.41 gives you just what you want.
#Nvidia performance monitor install
See Copyright Notice for more details.ĭownload and install latest stable CUDA driver (4.2) from here.
#Nvidia performance monitor free
Please feel free to use it as a dependency for your own projects.
#Nvidia performance monitor license
Note: nvitop is dual-licensed by the GPLv3 License and Apache-2.0 License. 'process/gpu_memory_utilization': this_process.gpu_memory_utilization(), '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 ( recommended): 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.
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