DGX-A100_GPU-Operator_testing: cluster_setup.md: new file

DGX-A100_GPU-Operator_testing/cluster_setup.md

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+# Prepare the cluster operators
+
+1. Install NFD Operator
+   - don't forget to create the NodeFeatureDiscoveries
+
+2. Install the NVIDIA GPU Operator
+   - don't forgot to create the ClusterPolicy
+
+2. Install the Local Storage Operator
+   - see next step for the creation of a LocalVolume
+
+# Setup the storage
+
+1. Create the local disk storage class
+
+```
+DISK_DEV=/dev/nvme2n1
+STORAGE_CLASS_NAME=local-sc-dgx
+NODE_NAME=dgxa100
+
+cat <<EOF | oc apply -f-
+apiVersion: local.storage.openshift.io/v1
+kind: LocalVolume
+metadata:
+  name: local-disks
+  namespace: openshift-local-storage
+spec:
+  nodeSelector:
+    nodeSelectorTerms:
+    - matchExpressions:
+        - key: kubernetes.io/hostname
+          operator: In
+          values:
+          - $NODE_NAME
+  storageClassDevices:
+    - storageClassName: $STORAGE_CLASS_NAME
+      volumeMode: Filesystem
+      fsType: xfs
+      devicePaths:
+        - $DISK_DEV
+EOF
+```
+
+# Enable user-workload monitoring
+
+```
+cat <<EOF | oc apply -f-
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: cluster-monitoring-config
+  namespace: openshift-monitoring
+data:
+  config.yaml: |
+    enableUserWorkload: true
+EOF
+```
+
+# Prepare the dataset and image for the benchmark
+
+1. Create the dataset PVC
+
+```
+PVC_NAME=benchmarking-coco-dataset
+NAMESPACE=default
+STORAGE_CLASS_NAME=local-sc-dgx
+
+cat <<EOF | oc apply -f-
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: $PVC_NAME
+  namespace: $NAMESPACE
+spec:
+  accessModes:
+    - ReadWriteOnce
+  resources:
+    requests:
+      storage: 80Gi
+  storageClassName: $STORAGE_CLASS_NAME
+EOF
+```
+
+2. Populate the PVC with the coco-dataset
+
+```
+NODE_NAME=dgxa100
+NAMESPACE=default
+PVC_NAME=benchmarking-coco-dataset
+./run_toolbox.py benchmarking download_coco_dataset $NODE_NAME \
+                 --namespace $NAMESPACE \
+                 --pvc-name $PVC_NAME
+```
+
+3. Build the MLPerf NVIDIA PyToch SSD image
+
+```
+oc patch configs.imageregistry.operator.openshift.io cluster --type merge --patch '{"spec":{"managementState":"Managed"}}'
+oc patch configs.imageregistry.operator.openshift.io cluster --type merge --patch '{"spec":{"storage":{"emptyDir":{}}}}'
+```
+
+```
+NAMESPACE=default
+cat <<EOF | oc apply -f-
+kind: ImageStream
+apiVersion: image.openshift.io/v1
+metadata:
+  name: mlperf
+  namespace: $NAMESPACE
+  labels:
+    app: mlperf
+spec: {}
+EOF
+```
+
+```
+NAMESPACE=default
+cat <<EOF | oc apply -f-
+apiVersion: build.openshift.io/v1
+kind: BuildConfig
+metadata:
+  labels:
+    app: mlperf
+  name: mlperf0.7
+  namespace: $NAMESPACE
+spec:
+  output:
+    to:
+      kind: ImageStreamTag
+      name: mlperf:ssd_0.7
+      namespace: default
+  resources: {}
+  source:
+    type: Git
+    git:
+      uri: "https://github.com/kpouget/training_results_v0.7.git"
+      ref: "master"
+    contextDir: NVIDIA/benchmarks/ssd/implementations/pytorch
+  triggers:
+  - type: "ConfigChange"
+  strategy:
+    type: Docker
+    dockerStrategy:
+      dockerfilePath: Dockerfile
+      from:
+        kind: DockerImage
+        name: nvcr.io/nvidia/pytorch:20.06-py3
+EOF
+```
+
+# Allow extra MIG modes
+
+```
+oc apply -f mig-custom.yaml
+
+set "/spec/migManager/config/name" = custom-mig-config
+```
+
+# Enable MIG metrics
+
+```
+oc patch clusterpolicy/gpu-cluster-policy --type='json' -p='[{"op": "replace", "path": "/spec/dcgmExporter/repository", "value": "nvcr.io/nvidia/k8s"}]'
+oc patch clusterpolicy/gpu-cluster-policy --type='json' -p='[{"op": "replace", "path": "/spec/dcgmExporter/image", "value": "dcgm-exporter"}]'
+oc patch clusterpolicy/gpu-cluster-policy --type='json' -p='[{"op": "replace", "path": "/spec/dcgmExporter/version", "value": "2.3.1-2.6.1-ubi8"}]'
+```