sedna/examples/incremental_learning/dog_croissants_classification

• Dog-Croissants-classification Demo
• • Prepare Model
  • Prepare for inference worker
  • build docker file
  • Create Incremental Job
  • trigger
  • delete

Dog-Croissants-classification Demo

Prepare Model

auto-download

Prepare for inference worker

mkdir -p /incremental_learning/infer/
mkdir -p /incremental_learning/he/
mkdir -p /data/dog_croissants/
mkdir /output

TODO:download dataset. I have no idea where I should put dataset

download checkpoint

# need ckpt file under both two dir
mkdir -p /models/base_model
mkdir -p /models/deploy_model
cd /models/base_model
curl https://download.mindspore.cn/vision/classification/mobilenet_v2_1.0_224.ckpt -o base_model.ckpt
cd ../deploy_model
curl https://download.mindspore.cn/vision/classification/mobilenet_v2_1.0_224.ckpt -o deploy_model.ckpt

build docker file

$  docker build -f incremental-learning-dog-croissants-classification.Dockerfile -t test/dog:v0.1 .

Create Incremental Job

WORKER_NODE="edge-node" 

Create Dataset

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Dataset
metadata:
  name: incremental-dataset
spec:
  url: "/data/dog_croissants/train_data.txt"
  format: "txt"
  nodeName: $WORKER_NODE
EOF

Create initial Model to simulate the inital model in incremental learning scenoario

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Model
metadata:
  name: initial-model
spec:
  url : "/models/base_model/base_model.ckpt"
  format: "ckpt"
EOF

Create Deploy Model

kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: Model
metadata:
  name: deploy-model
spec:
  url : "/models/deploy_model/deploy_model.ckpt"
  format: "ckpt"
EOF

create the job

IMAGE=lj1ang/dog:v0.40
kubectl create -f - <<EOF
apiVersion: sedna.io/v1alpha1
kind: IncrementalLearningJob
metadata:
  name: dog-croissants-classification-demo
spec:
  initialModel:
    name: "initial-model"
  dataset:
    name: "incremental-dataset"
    trainProb: 0.8
  trainSpec:
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
          - image: $IMAGE
            name: train-worker
            imagePullPolicy: IfNotPresent
            args: [ "train.py" ]
            env:
              - name: "batch_size"
                value: "2"
              - name: "epochs"
                value: "2"
              - name: "input_shape"
                value: "224"
              - name: "class_names"
                value: "Croissants, Dog"
              - name: "num_parallel_workers"
                value: "2"
    trigger:
      checkPeriodSeconds: 60
      timer:
        start: 02:00
        end: 20:00
      condition:
        operator: ">"
        threshold: 50
        metric: num_of_samples
  evalSpec:
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
          - image: $IMAGE
            name: eval-worker
            imagePullPolicy: IfNotPresent
            args: [ "eval.py" ]
            env:
              - name: "input_shape"
                value: "224"
              - name: "batch_size"
                value: "2"
              - name: "num_parallel_workers"
                value: "2"              
              - name: "class_names"
                value: "Croissants, Dog"
  deploySpec:
    model:
      name: "deploy-model"
      hotUpdateEnabled: true
      pollPeriodSeconds: 60
    trigger:
      condition:
        operator: ">"
        threshold: 0.1
        metric: precision_delta
    hardExampleMining:
      name: "Random"
      parameters:
        - key: "random_ratio"
          value: "0.3"
    template:
      spec:
        nodeName: $WORKER_NODE
        containers:
          - image: $IMAGE
            name: infer-worker
            imagePullPolicy: IfNotPresent
            args: [ "inference.py" ]
            env:
              - name: "input_shape"
                value: "224"
              - name: "infer_url"
                value: "/infer"
              - name: "HE_SAVED_URL"
                value: "/he_saved_url"
            volumeMounts:
              - name: localinferdir
                mountPath: /infer
              - name: hedir
                mountPath: /he_saved_url
            resources: # user defined resources
              limits:
                memory: 3Gi
        volumes: # user defined volumes
          - name: localinferdir
            hostPath:
              path: /incremental_learning/infer/
              type: DirectoryOrCreate
          - name: hedir
            hostPath:
              path: /incremental_learning/he/
              type: DirectoryOrCreate
  outputDir: "/output"
EOF

trigger

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

delete

kubectl delete dataset incremental-dataset
kubectl delete model initial-model
kubectl delete model deploy-model
kubectl delete IncrementalLearningJob dog-croissants-classification-demo