Using Lifelong Learning Job in Thermal Comfort Prediction Scenario

This document introduces how to use lifelong learning job in thermal comfort prediction scenario. Using the lifelong learning job, our application can automatically retrain, evaluate, and update models based on the data generated at the edge.

Thermal Comfort Prediction Experiment

Install Sedna

Follow the Sedna installation document to install Sedna.

Prepare Dataset

In this example, you can use ASHRAE Global Thermal Comfort Database II to initial lifelong learning job.

We provide a well-processed datasets, including train (trainData.csv), evaluation (testData.csv) and incremental (trainData2.csv) dataset.

cd /data
wget https://kubeedge.obs.cn-north-1.myhuaweicloud.com/examples/atcii-classifier/dataset.tar.gz
tar -zxvf dataset.tar.gz

Create Lifelong Job

In this example, $WORKER_NODE is a custom node, you can fill it which you actually run.

WORKER_NODE="edge-node"

Create Dataset

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Dataset
metadata:
  name: lifelong-dataset
spec:
  url: "/data/trainData.csv"
  format: "csv"
  nodeName: $WORKER_NODE
EOF

Also, you can replace trainData.csv with trainData2.csv which contained in dataset to trigger retraining.

Start The Lifelong Learning Job

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: LifelongLearningJob
metadata:
  name: atcii-classifier-demo
spec:
  dataset:
    name: "lifelong-dataset"
    trainProb: 0.8
  trainSpec:
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
          - image: kubeedge/sedna-example-lifelong-learning-atcii-classifier:v0.3.0
            name:  train-worker
            imagePullPolicy: IfNotPresent
            args: ["train.py"]  # training script
            env:  # Hyperparameters required for training
              - name: "early_stopping_rounds"
                value: "100"
              - name: "metric_name"
                value: "mlogloss"
    trigger:
      checkPeriodSeconds: 60
      timer:
        start: 02:00
        end: 24:00
      condition:
        operator: ">"
        threshold: 500
        metric: num_of_samples
  evalSpec:
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
          - image: kubeedge/sedna-example-lifelong-learning-atcii-classifier:v0.3.0
            name:  eval-worker
            imagePullPolicy: IfNotPresent
            args: ["eval.py"]
            env:
              - name: "metrics"
                value: "precision_score"
              - name: "metric_param"
                value: "{'average': 'micro'}"
              - name: "model_threshold"  # Threshold for filtering deploy models
                value: "0.5"
  deploySpec:
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
        - image: kubeedge/sedna-example-lifelong-learning-atcii-classifier:v0.3.0
          name:  infer-worker
          imagePullPolicy: IfNotPresent
          args: ["inference.py"]
          env:
          - name: "UT_SAVED_URL"  # unseen tasks save path
            value: "/ut_saved_url"
          - name: "infer_dataset_url"  # simulation of the inference samples
            value: "/data/testData.csv"
          volumeMounts:
          - name: utdir
            mountPath: /ut_saved_url
          - name: inferdata
            mountPath: /data/
          resources:  # user defined resources
            limits:
              memory: 2Gi
        volumes:   # user defined volumes
          - name: utdir
            hostPath:
              path: /lifelong/unseen_task/
              type: DirectoryOrCreate
          - name: inferdata
            hostPath:
              path:  /data/
              type: DirectoryOrCreate
  outputDir: "/output"
EOF

Note: outputDir can be set as s3 storage url to save artifacts(model, sample, etc.) into s3, and follow this to set the credentials.

Check Lifelong Learning Job

query the service status

kubectl get lifelonglearningjob atcii-classifier-demo

In the lifelonglearningjob resource atcii-classifier-demo, the following trigger is configured:

trigger:
  checkPeriodSeconds: 60
  timer:
    start: 02:00
    end: 20:00
  condition:
    operator: ">"
    threshold: 500
    metric: num_of_samples

Unseen Tasks samples Labeling

In a real word, we need to label the hard examples in our unseen tasks which storage in UT_SAVED_URL with annotation tools and then put the examples to Dataset‘s url.

Effect Display

In this example, false and failed detections occur at stage of inference before lifelong learning. After lifelong learning, the precision of the dataset have been improved by 5.12%.