Merge pull request #4001 from windsonsea/baize

[en] Sync baize best practice and install docs

docs/en/docs/baize/best-practice/deploy-nfs-in-worker.md

@@ -0,0 +1,183 @@
+# Deploy NFS for Dataset Preheating
+
+## Background
+
+Datasets are a core data management function in intelligent computing power.
+By abstracting the dependency on data throughout the entire lifecycle of `MLOps`
+into datasets, users can manage various types of data in datasets so that
+training tasks can directly use the data in the dataset.
+
+When remote data is not within the working cluster, datasets provide the capability
+to automatically preheat data, supporting data preheating from sources such as
+`Git`, `S3`, `HTTP`, `NFS` to the local cluster.
+
+A storage service supporting the `ReadWriteMany` mode is needed for preheating
+remote data for the `dataset`, and it is recommended to deploy NFS within the cluster.
+
+This article mainly introduces how to quickly deploy an NFS service and add it as a
+`StorageClass` for the cluster.
+
+## Preparation
+
+* NFS by default uses the node's storage as a data caching point,
+  so it is necessary to ensure that the disk itself has enough disk space.
+* The installation method uses `Helm` and `Kubectl`, please make sure they are already installed.
+
+## Deployment Steps
+
+Several components need to be installed:
+
+* NFS Server
+* csi-driver-nfs
+* StorageClass
+
+### Initialize Namespace
+
+All system components will be installed in the `nfs` namespace,
+so it is necessary to create this namespace first.
+
+```bash
+kubectl create namespace nfs
+```
+
+### Install NFS Server
+
+Here is a simple YAML deployment file that can be used directly.
+
+!!! note
+
+    Be sure to check the `image:` and modify it to a domestic mirror based on the location of the cluster.
+
+```yaml titile="nfs-server.yaml"
+---
+kind: Service
+apiVersion: v1
+metadata:
+  name: nfs-server
+  namespace: nfs
+  labels:
+    app: nfs-server
+spec:
+  type: ClusterIP
+  selector:
+    app: nfs-server
+  ports:
+    - name: tcp-2049
+      port: 2049
+      protocol: TCP
+    - name: udp-111
+      port: 111
+      protocol: UDP
+---
+kind: Deployment
+apiVersion: apps/v1
+metadata:
+  name: nfs-server
+  namespace: nfs
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: nfs-server
+  template:
+    metadata:
+      name: nfs-server
+      labels:
+        app: nfs-server
+    spec:
+      nodeSelector:
+        "kubernetes.io/os": linux
+      containers:
+        - name: nfs-server
+          image: itsthenetwork/nfs-server-alpine:latest
+          env:
+            - name: SHARED_DIRECTORY
+              value: "/exports"
+          volumeMounts:
+            - mountPath: /exports
+              name: nfs-vol
+          securityContext:
+            privileged: true
+          ports:
+            - name: tcp-2049
+              containerPort: 2049
+              protocol: TCP
+            - name: udp-111
+              containerPort: 111
+              protocol: UDP
+      volumes:
+        - name: nfs-vol
+          hostPath:
+            path: /nfsdata  # Modify this to specify another path to store NFS shared data
+            type: DirectoryOrCreate
+```
+
+Save the above YAML as `nfs-server.yaml`, then run the following commands for deployment:
+
+```bash
+kubectl -n nfs apply -f nfs-server.yaml
+
+# Check the deployment result
+kubectl -n nfs get pod,svc
+```
+
+### Install csi-driver-nfs
+
+Installing `csi-driver-nfs` requires the use of `Helm`, please ensure it is installed beforehand.
+
+```bash
+# Add Helm repository
+helm repo add csi-driver-nfs https://mirror.ghproxy.com/https://raw.githubusercontent.com/kubernetes-csi/csi-driver-nfs/master/charts
+helm repo update csi-driver-nfs
+
+# Deploy csi-driver-nfs
+# The parameters here mainly optimize the image address to accelerate downloads in China
+helm upgrade --install csi-driver-nfs csi-driver-nfs/csi-driver-nfs \
+    --set image.nfs.repository=k8s.m.daocloud.io/sig-storage/nfsplugin \
+    --set image.csiProvisioner.repository=k8s.m.daocloud.io/sig-storage/csi-provisioner \
+    --set image.livenessProbe.repository=k8s.m.daocloud.io/sig-storage/livenessprobe \
+    --set image.nodeDriverRegistrar.repository=k8s.m.daocloud.io/sig-storage/csi-node-driver-registrar \
+    --namespace nfs \
+    --version v4.5.0
+```
+
+!!! warning
+
+    Not all images of `csi-nfs-controller` support `helm` parameters, so the `image` field of the `deployment` needs to be manually modified.
+    Change `image: registry.k8s.io` to `image: k8s.dockerproxy.com` to accelerate downloads in China.
+
+### Create StorageClass
+
+Save the following YAML as `nfs-sc.yaml`:
+
+```yaml title="nfs-sc.yaml"
+apiVersion: storage.k8s.io/v1
+kind: StorageClass
+metadata:
+  name: nfs-csi
+provisioner: nfs.csi.k8s.io
+parameters:
+  server: nfs-server.nfs.svc.cluster.local
+  share: /
+  # csi.storage.k8s.io/provisioner-secret is only needed for providing mountOptions in DeleteVolume
+  # csi.storage.k8s.io/provisioner-secret-name: "mount-options"
+  # csi.storage.k8s.io/provisioner-secret-namespace: "default"
+reclaimPolicy: Delete
+volumeBindingMode: Immediate
+mountOptions:
+  - nfsvers=4.1
+```
+
+then run the following command:
+
+```bash
+kubectl apply -f nfs-sc.yaml
+```
+
+## Test
+
+Create a dataset and set the dataset's **associated storage class** and
+`preheating method` to `NFS` to preheat remote data into the cluster.
+
+After the dataset is successfully created, you can see that the dataset's status is `preheating`,
+and you can start using it after the preheating is completed.