Enhance federated learning example

Signed-off-by: JimmyYang <yangjin39@huawei.com>

examples/federated_learning/surface_defect_detection/README.md

@@ -5,31 +5,51 @@ Using Federated Learning, we can solve the problem. Each place uses its own data
 
 
 ## Surface Defect Detection Experiment
-> Assume that there are two edge nodes (edge1 and edge2) and a cloud node. Data on the edge nodes cannot be migrated to the cloud due to privacy issues.
+> Assume that there are two edge nodes and a cloud node. Data on the edge nodes cannot be migrated to the cloud due to privacy issues.
 > Base on this scenario, we will demonstrate the surface inspection.
 
+### Prepare Nodes
+```
+CLOUD_NODE="cloud-node-name"
+EDGE1_NODE="edge1-node-name"
+EDGE2_NODE="edge2-node-name"
+```
+
 ### Install Sedna
 
 Follow the [Sedna installation document](/docs/setup/install.md) to install Sedna.
  
 ### Prepare Dataset
 
-Download [dataset](https://github.com/abin24/Magnetic-tile-defect-datasets.) and the [label file](/examples/federated_learning/surface_defect_detection/data/1.txt) to `/data` of edge1.  
-Download [dataset](https://github.com/abin24/Magnetic-tile-defect-datasets.) and the [label file](/examples/federated_learning/surface_defect_detection/data/2.txt) to `/data` of edge2.
-
-### Prepare Script
+Download [dataset](https://github.com/abin24/Magnetic-tile-defect-datasets.) and the [label file](/examples/federated_learning/surface_defect_detection/data/1.txt) to `/data` of ```EDGE1_NODE```.  
+```
+mkdir -p /data
+cd /data
+git clone https://github.com/abin24/Magnetic-tile-defect-datasets..git Magnetic-tile-defect-datasets
+curl -o 1.txt https://raw.githubusercontent.com/kubeedge/sedna/main/examples/federated_learning/surface_defect_detection/data/1.txt
+```
 
-Download the script [aggregate.py](/examples/federated_learning/surface_defect_detection/aggregation_worker/aggregate.py) to the `/code` of cloud node.
+Download [dataset](https://github.com/abin24/Magnetic-tile-defect-datasets.) and the [label file](/examples/federated_learning/surface_defect_detection/data/2.txt) to `/data` of ```EDGE2_NODE```.
+```
+mkdir -p /data
+cd /data
+git clone https://github.com/abin24/Magnetic-tile-defect-datasets..git Magnetic-tile-defect-datasets
+curl -o 2.txt https://raw.githubusercontent.com/kubeedge/sedna/main/examples/federated_learning/surface_defect_detection/data/2.txt
+```
 
-Download the script [training_worker](/examples/federated_learning/surface_defect_detection/training_worker/train.py) to the `/code` of edge1 and edge2.
+### Prepare Images
+This example uses these images:
+1. aggregation worker: ```kubeedge/sedna-example-federated-learning-surface-defect-detection-aggregation:v0.1.0```
+2. train worker: ```kubeedge/sedna-example-federated-learning-surface-defect-detection-train:v0.1.0```  
 
+These images are generated by the script [build_images.sh](/examples/build_image.sh).
 
 ### Create Federated Learning Job 
 
 #### Create Dataset
 
+create dataset for `$EDGE1_NODE`
 ```
-# create dataset for edge1
 kubectl create -f - <<EOF
 apiVersion: sedna.io/v1alpha1
 kind: Dataset
@@ -38,10 +58,12 @@ metadata:
 spec:
   url: "/data/1.txt"
   format: "txt"
-  nodeName: "edge1"
+  nodeName: $EDGE1_NODE
 EOF
+```
 
-# create dataset for edge2
+create dataset for `$EDGE2_NODE`
+```
 kubectl create -f - <<EOF
 apiVersion: sedna.io/v1alpha1
 kind: Dataset
@@ -50,12 +72,22 @@ metadata:
 spec:
   url: "/data/2.txt"
   format: "txt"
-  nodeName: "edge2"
+  nodeName: $EDGE2_NODE
 EOF
 ```
 
 #### Create Model
 
+create the directory `/model` in the host of `$EDGE1_NODE`
+```
+mkdir /model
+```
+create the directory `/model` in the host of `$EDGE2_NODE`
+```
+mkdir /model
+```
+
+create model
 ```
 kubectl create -f - <<EOF
 apiVersion: sedna.io/v1alpha1
@@ -80,46 +112,58 @@ spec:
   aggregationWorker:
     model:
       name: "surface-defect-detection-model"
-    nodeName: "cloud0"
-    workerSpec:
-      scriptDir: "/code"
-      scriptBootFile: "aggregate.py"
-      frameworkType: "tensorflow"
-      frameworkVersion: "2.3"
-      parameters:
-        - key: "exit_round"
-          value: "3"
+    template:
+      spec:
+        nodeName: $CLOUD_NODE
+        containers:
+          - image: kubeedge/sedna-example-federated-learning-surface-defect-detection-aggregation:v0.1.0
+            name:  agg-worker
+            imagePullPolicy: IfNotPresent
+            env: # user defined environments
+              - name: "exit_round"
+                value: "3"
+            resources:  # user defined resources
+              limits:
+                memory: 2Gi
   trainingWorkers:
-    - nodeName: "edge1"
-      dataset:
-        name: "edge-1-surface-defect-detection-dataset"
-      workerSpec:
-        scriptDir: "/code"
-        scriptBootFile: "train.py"
-        frameworkType: "tensorflow"
-        frameworkVersion: "2.3"
-        parameters:
-          - key: "batch_size"
-            value: "32"
-          - key: "learning_rate"
-            value: "0.001"
-          - key: "epochs"
-            value: "1"
-    - nodeName: "edge2"
-      dataset:
-          name: "edge-2-surface-defect-detection-dataset"
-      workerSpec:
-        scriptDir: "/code"
-        scriptBootFile: "train.py"
-        frameworkType: "tensorflow"
-        frameworkVersion: "2.3"
-        parameters:
-          - key: "batch_size"
-            value: "32"
-          - key: "learning_rate"
-            value: "0.001"
-          - key: "epochs"
-            value: "1"
+    - dataset:
+        name: "edge1-surface-defect-detection-dataset"
+      template:
+        spec:
+          nodeName: $EDGE1_NODE
+          containers:
+            - image: kubeedge/sedna-example-federated-learning-surface-defect-detection-train:v0.1.0
+              name:  train-worker
+              imagePullPolicy: IfNotPresent
+              env:  # user defined environments
+                - name: "batch_size"
+                  value: "32"
+                - name: "learning_rate"
+                  value: "0.001"
+                - name: "epochs"
+                  value: "2"
+              resources:  # user defined resources
+                limits:
+                  memory: 2Gi
+    - dataset:
+        name: "edge2-surface-defect-detection-dataset"
+      template:
+        spec:
+          nodeName: $EDGE2_NODE
+          containers:
+            - image: kubeedge/sedna-example-federated-learning-surface-defect-detection-train:v0.1.0
+              name:  train-worker
+              imagePullPolicy: IfNotPresent
+              env:  # user defined environments
+                - name: "batch_size"
+                  value: "32"
+                - name: "learning_rate"
+                  value: "0.001"
+                - name: "epochs"
+                  value: "2"
+              resources:  # user defined resources
+                limits:
+                  memory: 2Gi
 EOF
 ```
 
@@ -130,4 +174,4 @@ kubectl get federatedlearningjob surface-defect-detection
 ```
 
 ### Check Federated Learning Train Result
-After the job completed, you will find the model generated on the path `/model` in edge1 and edge2.
+After the job completed, you will find the model generated on the directory `/model` in `$EDGE1_NODE` and `EDGE2_NODE`.