GPU ID HOW TO
GPU ID INSTALL

If setting this with python, make sure you are using strings for all environment variables, including numerical ones. If you want GPU load-balancing, make gpu_id random at each guest system start. Note that the environmental variable should be set before the guest system is started (so no chances of doing it in your Jupyter Notebook's terminal), for instance using docker run -e NVIDIA_VISIBLE_DEVICES=0 or env in Kubernetes or Openshift. This method based on NVIDIA_VISIBLE_DEVICES exposes only a single card to the system (with local ID zero), hence we also hard-code the other variable, CUDA_VISIBLE_DEVICES to 0 (mainly to prevent it from defaulting to an empty string that would indicate no GPU). You can verify that a different card is selected for each value of gpu_id by inspecting Bus-Id parameter in nvidia-smi run in a terminal in the guest system). Where gpu_id is the ID of your selected GPU, as seen in the host system's nvidia-smi (a 0-based integer) that will be made available to the guest system (e.g.

Set the following two environment variables: NVIDIA_VISIBLE_DEVICES=$gpu_id I know when writing using CUDA you can manage and control which CUDA resources to use but how would I manage this from the command line when running a compiled CUDA executable? I am testing this on a system running using the bash shell, on CentOS 6.8, with CUDA 8.0, GPUs, and NVIDIA driver 367.44. While not directly related to my question, using nbody -device=1 I was able to get the application to run on GPU 1 but using nbody -numdevices=2 did not run on both GPU 0 and 1.

GPU ID HOW TO

In addition to $CUDA_VISIBLE_DEVICES$, I saw other posts refer to the environment variable $CUDA_DEVICES but these were not set and I did not find information on how to use it. I looked at the related question, how to choose designated GPU to run CUDA program?, but deviceQuery command is not in the CUDA 8.0 bin directory. Then running nbody again but it also went to GPU 0. I tried setting it using CUDA_VISIBLE_DEVICES=1 Checking CUDA_VISIBLE_DEVICES using echo $CUDA_VISIBLE_DEVICES

GPU ID INSTALL

In a multi-GPU computer, how do I designate which GPU a CUDA job should run on?Īs an example, when installing CUDA, I opted to install the NVIDIA_CUDA-_Samples then ran several instances of the nbody simulation, but they all ran on one GPU 0 GPU 1 was completely idle (monitored using watch -n 1 nvidia-dmi).

Gpu id