Using Joint Inference Service in Helmet Detection Scenario on S3

This example is based on the example: Using Joint Inference Service in Helmet Detection Scenario.

Prepare Nodes

Assume you have created a KubeEdge cluster that have one cloud node(e.g., cloud-node) and one edge node(e.g., edge-node).

Create a secret with your S3 user credential.

kubectl create -f - <<EOF
apiVersion: v1
kind: Secret
metadata:
  name: mysecret
  annotations:
    s3-endpoint: play.min.io # replace with your s3 endpoint
    s3-usehttps: "1" # by default 1, if testing with minio you can set to 0
stringData:
  ACCESS_KEY_ID: Q3AM3UQ867SPQQA43P2F
  SECRET_ACCESS_KEY: zuf+tfteSlswRu7BJ86wekitnifILbZam1KYY3TG
EOF

Prepare Model

• Download little model and big model.

• Put the unzipped model file into the bucket of your cloud storage service.

• Attach the created secret to the Model and create Model.

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Model
metadata:
  name: big-model
  spec:
    url : "s3://kubeedge/model/big-model/yolov3_darknet.pb"
    format: "pb"
    credentialName: mysecret
EOF

kubectl $action -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Model
metadata:
  name: little-model
spec:
  url: "s3://kubeedge/model/little-model/yolov3_resnet18.pb"
  format: "pb"
  credentialName: mysecret
EOF

Prepare Images

This example uses these images:

1. little model inference worker: kubeedge/sedna-example-joint-inference-helmet-detection-little:v0.3.0

2. big model inference worker: kubeedge/sedna-example-joint-inference-helmet-detection-big:v0.3.0

These images are generated by the script build_images.sh.

Prepare Job

• Make preparation in edge node

mkdir -p /joint_inference/output

• Attach the created secret to the Job and create Job.

LITTLE_MODEL_IMAGE=kubeedge/sedna-example-joint-inference-helmet-detection-little:v0.3.0
BIG_MODEL_IMAGE=kubeedge/sedna-example-joint-inference-helmet-detection-big:v0.3.0

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: JointInferenceService
metadata:
  name: helmet-detection-inference-example
  namespace: default
spec:
  edgeWorker:
    model:
      name: "helmet-detection-inference-little-model"
    hardExampleMining:
      name: "IBT"
      parameters:
        - key: "threshold_img"
          value: "0.9"
        - key: "threshold_box"
          value: "0.9"
    template:
      spec:
        nodeName: edge-node
        containers:
          - image: $LITTLE_MODEL_IMAGE
            imagePullPolicy: IfNotPresent
            name:  little-model
            env:  # user defined environments
              - name: input_shape
                value: "416,736"
              - name: "video_url"
                value: "rtsp://localhost/video"
              - name: "all_examples_inference_output"
                value: "/data/output"
              - name: "hard_example_cloud_inference_output"
                value: "/data/hard_example_cloud_inference_output"
              - name: "hard_example_edge_inference_output"
                value: "/data/hard_example_edge_inference_output"
            resources:  # user defined resources
              requests:
                memory: 64M
                cpu: 100m
              limits:
                memory: 2Gi
            volumeMounts:
              - name: outputdir
                mountPath: /data/
        volumes:   # user defined volumes
          - name: outputdir
            hostPath:
              # user must create the directory in host
              path: /joint_inference/output
              type: DirectoryOrCreate

  cloudWorker:
    model:
      name: "helmet-detection-inference-big-model"
    template:
      spec:
        nodeName: cloud-node
        containers:
          - image: $BIG_MODEL_IMAGE
            name:  big-model
            imagePullPolicy: IfNotPresent
            env:  # user defined environments
              - name: "input_shape"
                value: "544,544"
            resources:  # user defined resources
              requests:
                memory: 2Gi
EOF

Mock Video Stream for Inference in Edge Side

Refer to here.