Can schedule full GPUs and partial GPUs side-by-side about gpushare-scheduler-extender

[cheyang]

The pod with gpu-count can be scheduled but it won't be enabled with GPU capabilities in device plugin.

It's just an extended resource for keeping the GPU count. See gpu count definition.

I think I can make gpu-count disable in scheduling time. Such as aliyun.com/gpu-count is not allowed to schedule, it's only for keeping the info of GPU count. What's your idea?

[reverson]

Oh, interesting, what I'm seeing is that I'm getting access to the GPUs regardless of what I select for the GPU request (gpu-mem, gpu-count, or no gpu at all). However, I think my teammate discovered that using the cuda-vector-add example from the k8s documentation causes the nvidia-docker2 runtime to auto add all the GPUs to the docker image.

In terms of workflow I'd like to be able to use this plugin to manage both our workflow for shared GPUs as well as whole GPUs.

If that's not possible, then I'd definitely prefer to see gpu-count be an unscheduled resource as you describe.

[cheyang]

I think the reason is that you are using nvidia's cuda docker base image which includes the environment [NVIDIA_VISIBLE_DEVICES=all]
https://github.com/NVIDIA/nvidia-container-runtime#nvidia_visible_devices. That cause the nvidia-docker2 load nvidia runtime for the container. I think you can set NVIDIA_VISIBLE_DEVICES=void when building your docker image.

[cicijohn1983]

你好 我在运行示例的时候报这个错误nvidia-container-cli: device error: unknown device id: no-gpu-has-1024MiB-to-run，请问一下怎么解决这个问题，和显卡驱动有关系吗？