[GitHub] [yunikorn-site] wilfred-s commented on a diff in pull request #200: [YUNIKORN-1355] Generic example of GPU scheduling with Yunikorn

[GitBox <gi...@apache.org>]

wilfred-s commented on code in PR #200:
URL: https://github.com/apache/yunikorn-site/pull/200#discussion_r1036618136


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docs/user_guide/workloads/run_nvidia.md:
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@@ -0,0 +1,346 @@
+---
+id: run_nvidia
+title: Run NVIDIA GPU Jobs
+description: How to run generic example of GPU scheduling with Yunikorn.
+keywords:
+ - NVIDIA GPU
+---
+
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements.  See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership.  The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License.  You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied.  See the License for the
+specific language governing permissions and limitations
+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).
+
+### 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).
+
+### Install NVIDIA Device Plugin
+Add the nvidia-device-plugin helm repository.
+```
+helm repo add nvdp https://nvidia.github.io/k8s-device-plugin
+helm repo update
+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 ...
+```
+
+Deploy the device plugin
+```
+kubectl create namespace nvidia
+helm install --generate-name nvdp/nvidia-device-plugin --namespace nvidia --version 0.12.3
+```
+
+Check the status of the pods to ensure NVIDIA device plugin is running
+```
+kubectl get pods -A
+
+NAMESPACE      NAME                                      READY   STATUS    RESTARTS      AGE
+kube-flannel   kube-flannel-ds-j24fx                     1/1     Running   1 (11h ago)   11h
+kube-system    coredns-78fcd69978-2x9l8                  1/1     Running   1 (11h ago)   11h
+kube-system    coredns-78fcd69978-gszrw                  1/1     Running   1 (11h ago)   11h
+kube-system    etcd-katlantyss-nzxt                      1/1     Running   3 (11h ago)   11h
+kube-system    kube-apiserver-katlantyss-nzxt            1/1     Running   4 (11h ago)   11h
+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
+```
+
+### Testing NVIDIA Device Plugin
+Create a gpu test yaml file.
+```
+# 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
+```
+Deploy the application.
+```
+kubectl apply -f gpu-pod.yaml
+```
+Check the logs to ensure the app completed successfully.
+```
+kubectl get pods gpu-operator-test
+
+NAME                READY   STATUS      RESTARTS   AGE
+gpu-operator-test   0/1     Completed   0          9d
+```
+Check the result.
+```
+kubectl logs gpu-operator-test
+	
+[Vector addition of 50000 elements]
+Copy input data from the host memory to the CUDA device
+CUDA kernel launch with 196 blocks of 256 threads
+Copy output data from the CUDA device to the host memory
+Test PASSED
+Done
+```
+
+---
+## Enable GPU Time-Slicing(Optional)

Review Comment:
   nit: space between Slicing and the bracket.



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