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Posted to issues@yunikorn.apache.org by wi...@apache.org on 2023/11/13 08:22:39 UTC
(yunikorn-site) branch master updated: [YUNIKORN-1968] Update "Run NVIDIA GPU Jobs in workload" (#331)
This is an automated email from the ASF dual-hosted git repository.
wilfreds pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/yunikorn-site.git
The following commit(s) were added to refs/heads/master by this push:
new 323bd21a33 [YUNIKORN-1968] Update "Run NVIDIA GPU Jobs in workload" (#331)
323bd21a33 is described below
commit 323bd21a337c77b2e76de40595896f77941b732b
Author: KatLantyss <re...@gmail.com>
AuthorDate: Mon Nov 13 19:22:21 2023 +1100
[YUNIKORN-1968] Update "Run NVIDIA GPU Jobs in workload" (#331)
Closes: #331
Signed-off-by: Wilfred Spiegelenburg <wi...@apache.org>
---
docs/user_guide/workloads/run_nvidia.md | 51 +++++++++++++++++++--------------
1 file changed, 29 insertions(+), 22 deletions(-)
diff --git a/docs/user_guide/workloads/run_nvidia.md b/docs/user_guide/workloads/run_nvidia.md
index 644910851f..b75648851c 100644
--- a/docs/user_guide/workloads/run_nvidia.md
+++ b/docs/user_guide/workloads/run_nvidia.md
@@ -26,10 +26,10 @@ under the License.
-->
## Yunikorn with NVIDIA GPUs
-This guide gives an overview of how to set up NVIDIA Device Plugin which enable user to run GPUs with Yunikorn, for more details please check [**Kubernetes with GPUs**](https://docs.nvidia.com/datacenter/cloud-native/kubernetes/install-k8s.html#option-2-installing-kubernetes-using-kubeadm).
+This guide gives an overview of how to set up NVIDIA Device Plugin which enable user to run GPUs with Yunikorn, for more details please check [**NVIDIA device plugin for Kubernetes**](https://github.com/NVIDIA/k8s-device-plugin#nvidia-device-plugin-for-kubernetes).
### Prerequisite
-Before following the steps below, Yunikorn need to deploy on the [**Kubernetes with GPUs**](https://docs.nvidia.com/datacenter/cloud-native/kubernetes/install-k8s.html#install-kubernetes).
+Before following the steps below, Yunikorn need to deploy on the Kubernetes with GPUs.
### Install NVIDIA Device Plugin
Add the nvidia-device-plugin helm repository.
@@ -42,14 +42,17 @@ helm repo list
Verify the latest release version of the plugin is available.
```
helm search repo nvdp --devel
-NAME CHART VERSION APP VERSION DESCRIPTION
-nvdp/nvidia-device-plugin 0.12.3 0.12.3 A Helm chart for ...
+NAME CHART VERSION APP VERSION DESCRIPTION
+nvdp/nvidia-device-plugin 0.14.1 0.14.1 A Helm chart for ...
```
Deploy the device plugin
```
kubectl create namespace nvidia
-helm install --generate-name nvdp/nvidia-device-plugin --namespace nvidia --version 0.12.3
+helm install nvidia-device-plugin nvdp/nvidia-device-plugin \
+ --namespace nvidia \
+ --create-namespace \
+ --version 0.14.1
```
Check the status of the pods to ensure NVIDIA device plugin is running
@@ -65,25 +68,29 @@ kube-system kube-apiserver-katlantyss-nzxt 1/1 Running 4 (11
kube-system kube-controller-manager-katlantyss-nzxt 1/1 Running 3 (11h ago) 11h
kube-system kube-proxy-4wz7r 1/1 Running 1 (11h ago) 11h
kube-system kube-scheduler-katlantyss-nzxt 1/1 Running 4 (11h ago) 11h
-kube-system nvidia-device-plugin-1659451060-c92sb 1/1 Running 1 (11h ago) 11h
+nvidia nvidia-device-plugin-1659451060-c92sb 1/1 Running 1 (11h ago) 11h
```
### Testing NVIDIA Device Plugin
Create a gpu test yaml file.
-```
+```yaml
# gpu-pod.yaml
- apiVersion: v1
- kind: Pod
- metadata:
- name: gpu-operator-test
- spec:
- restartPolicy: OnFailure
- containers:
- - name: cuda-vector-add
- image: "nvidia/samples:vectoradd-cuda10.2"
- resources:
- limits:
- nvidia.com/gpu: 1
+apiVersion: v1
+kind: Pod
+metadata:
+ name: gpu-pod
+spec:
+ restartPolicy: Never
+ containers:
+ - name: cuda-container
+ image: nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda11.7.1-ubi8
+ resources:
+ limits:
+ nvidia.com/gpu: 1 #requesting 1 GPU
+ tolerations:
+ - key: nvidia.com/gpu
+ operator: Exists
+ effect: NoSchedule
```
Deploy the application.
```
@@ -91,14 +98,14 @@ kubectl apply -f gpu-pod.yaml
```
Check the logs to ensure the app completed successfully.
```
-kubectl get pods gpu-operator-test
+kubectl get pod gpu-pod
NAME READY STATUS RESTARTS AGE
-gpu-operator-test 0/1 Completed 0 9d
+gpu-pod 0/1 Completed 0 9d
```
Check the result.
```
-kubectl logs gpu-operator-test
+kubectl logs gpu-pod
[Vector addition of 50000 elements]
Copy input data from the host memory to the CUDA device
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