Comments (9)
The nvidia plugin is also running on the master node which have no nvidia drivers and nvidia docker installed. Is this behaviour correct?
correct.
I can only run 1 GPU on my cluster at a time. For example, if I run the tensorflow notebook with 1 GPU, it works. But if I deploy another pod utilising another 1 GPU, the pod status gets stuck on pending, stating that there are insufficient GPU resource.
paste your yaml file and the output of kubectl describe node
for each node
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ok. I ran nvidia_pod.yaml and got the following error:
Message: 0/5 nodes are available: 5 Insufficient nvidia.com/gpu.
Attached are the description of each node.
node2.txt
node3.txt
node4.txt
node1.txt
nvidia_pod.yml.txt
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Am I suppose to have both:
alpha.kubernetes.io/nvidia-gpu: 1
nvidia.com/gpu: 1
in capacity and allocation fields?
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@jonathan-goh there is only 1 GPU on every node, so you can not request 2 GPUs in one pod.
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Hello @jonathan-goh !
@jonathan-goh there is only 1 GPU on every node, so you can not request 2 GPUs in one pod.
Looks like it thanks for handling this issue @pineking !
Am I suppose to have both:
alpha.kubernetes.io/nvidia-gpu: 1
nvidia.com/gpu: 1
in capacity and allocation fields?
Ideally it's better if you don't enable the Accelerator flags on kubelet. Though it shouldn't have any impact.
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@pineking oh ok. Sorry! I did not know that as I am really new to this! But lets say I want to do distributed learning on my cluster, how do I do that? Do I use a deployment?
@RenaudWasTaken That is the thing, I already removed it, reloaded and restarted the kube, but it is still there..
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@pineking oh ok. Sorry! I did not know that as I am really new to this! But lets say I want to do distributed learning on my cluster, how do I do that? Do I use a deployment?
If you are talking about MPI, that's not supported yet but we are working on it :)
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If you are talking about MPI, that's not supported yet but we are working on it :)
@RenaudWasTaken Are there some issues on GitHub or docs/links to track the progress?
@pineking oh ok. Sorry! I did not know that as I am really new to this! But lets say I want to do distributed learning on my cluster, how do I do that? Do I use a deployment?
@jonathan-goh For distributed training, you can create more than 1 pod (worker) , each pod has 1 GPU. https://github.com/kubeflow/kubeflow https://github.com/tensorflow/k8s
For TensorFolw and MPI, see https://github.com/uber/horovod
@RenaudWasTaken That is the thing, I already removed it, reloaded and restarted the kube, but it is still there..
@jonathan-goh I think you can ignore it.
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@RenaudWasTaken Are there some issues on GitHub or docs/links to track the progress?
Nope, it's on our roadmap but really depends on getting the Resource Class API merged.
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Related Issues (20)
- A pod can access all gpu resources even if no nvidia.com/gpu is configed. HOT 1
- Using CUDA MPS to enable GPU sharing, the pod occupies all GPU memory. HOT 11
- 0/1 nodes are available: 1 Insufficient nvidia.com/gpu HOT 2
- Limiting GPU Resource Usage per Docker Container with MPS Daemon
- K8s 1.24 failed to schedule using GPU-(error code CUDA driver HOT 6
- Access NVIDIA GPUs in K8s in a non-privileged container
- can't install 0.15.0-rc.2 HOT 3
- Device plugin does not start on MIG-enabled host due to insufficient permissions HOT 6
- Daemonset yaml file is not picking up Timeslicing configMap
- Create CDI spec error "libcuda.so.535.129.03 not found" in version "v0.15.0-rc.2" HOT 2
- Dedicated GPU's for time slicing on multi GPU set ups.
- How to mount containerPath to a hostPath for discover NVIDIA libraries w/o CDI spec HOT 5
- Using CUDA MPS to enable GPU sharing in K8S, error:error checking MPS daemon health HOT 2
- K3s in Docker (K3D) - `nvml error: insufficient permissions`
- Fix e2e tests HOT 1
- WSL2 - No devices found. Waiting indefinitely. HOT 3
- MPS use error: Failed to allocate device vector A (error code all CUDA-capable devices are busy or unavailable)! HOT 25
- Back-off restarting failed container nvidia-device-plugin-ctr HOT 3
- Error in nvidia-device-plugin pod. HOT 2
- Go Package: github.com/opencontainers/runc 1.0.0-rc93 < 1.1.12 - Local Sandbox Bypass Vulnerability HOT 1
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