sedna/examples/federated_learning/surface_defect_detection_v2

• Using Federated Learning Job in Surface Defect Detection Scenario
• • Surface Defect Detection Experiment
  • • Prepare Nodes
    • Install Sedna
    • Prepare Dataset
    • Prepare Images
    • Create Federated Learning Job
    • • Create Dataset
      • Create Model
      • Start Federated Learning Job
    • Check Federated Learning Status
    • Check Federated Learning Train Result

Using Federated Learning Job in Surface Defect Detection Scenario

This case introduces how to use federated learning job in surface defect detection scenario.
In the safety surface defect detection, data is scattered in different places (such as server node, camera or others) and cannot be aggregated due to data privacy and bandwidth. As a result, we cannot use all the data for training.
Using Federated Learning, we can solve the problem. Each place uses its own data for model training ,uploads the weight to the cloud for aggregation, and obtains the aggregation result for model update.

Surface Defect Detection Experiment

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 to install Sedna.

Prepare Dataset

Download dataset and the label file 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 dataset and the label file 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

Prepare Images

This example uses these images:

1. aggregation worker: kubeedge/sedna-example-federated-learning-surface-defect-detection-aggregation:v0.3.0
2. train worker: kubeedge/sedna-example-federated-learning-surface-defect-detection-train:v0.3.0

These images are generated by the script build_images.sh.

Create Federated Learning Job

Create Dataset

create dataset for $EDGE1_NODE

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Dataset
metadata:
  name: "edge1-surface-defect-detection-dataset"
spec:
  url: "/data/1.txt"
  format: "txt"
  nodeName: $EDGE1_NODE
EOF

create dataset for $EDGE2_NODE

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Dataset
metadata:
  name: "edge2-surface-defect-detection-dataset"
spec:
  url: "/data/2.txt"
  format: "txt"
  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
kind: Model
metadata:
  name: "surface-defect-detection-model"
spec:
  url: "/model"
  format: "pb"
EOF

Start Federated Learning Job

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: FederatedLearningJob
metadata:
  name: surface-defect-detection
spec:
  aggregationWorker:
    model:
      name: "surface-defect-detection-model"
    template:
      spec:
        nodeName: $CLOUD_NODE
        containers:
          - image: kubeedge/sedna-example-federated-learning-surface-defect-detection-aggregation:v0.3.0
            name:  agg-worker
            imagePullPolicy: IfNotPresent
            env: # user defined environments
              - name: "exit_round"
                value: "3"
            resources:  # user defined resources
              limits:
                memory: 2Gi
  trainingWorkers:
    - dataset:
        name: "edge1-surface-defect-detection-dataset"
      template:
        spec:
          nodeName: $EDGE1_NODE
          containers:
            - image: kubeedge/sedna-example-federated-learning-surface-defect-detection-train:v0.3.0
              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:v0.3.0
              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

Check Federated Learning Status

kubectl get federatedlearningjob surface-defect-detection

Check Federated Learning Train Result

After the job completed, you will find the model generated on the directory /model in $EDGE1_NODE and $EDGE2_NODE.