Important

This repository is no longer being maintained. Please use the new WAO instead.

WAO Prediction Service

Prediction service used in WAO-Scheduler and WAO-LB.

Author: kaz260

Directory Structure

• models/: The power consumption model
• clients/: Clients
• k8s/: Kubernetes manifests

Usage

1. Build the Docker image

docker build -t wao-predict-konohana-pc:1.0 .

2. Push the Docker image to your registry

REGISTRY=localhost # set your registry here
docker tag wao-predict-konohana-pc:1.0 $REGISTRY/wao-predict-konohana-pc:1.0
docker push $IMAGE

3. Run the prediction service

3.1. Run with Kubernetes

Make sure you set the correct image name in k8s/tensorflow-server-dep.yaml.

kubectl apply -f k8s

Note

You may need to expose the service with NodePort, Ingress, etc.

3.2. Run with Docker

docker run -d --rm -p 8500:8500 -p 8501:8501 --name wao-pred $IMAGE

4. Predict with clients

Usage: ./TOOL SERVER_IP CPU_USAGE AMBIENT_TEMP CPU1_TEMP CPU2_TEMP

Example: ./main.py 10.0.0.100 0.5 0.5 0.5 0.5

Note

CPU usage and temperatures are normalized to [0, 1].

4.1. Python gRPC client

Local:

cd clients/pred_pc_grpc_py

python -mvenv venv
. venv/bin/activate
pip install -r requirements.txt

./main.py -h

Docker:

cd clients/pred_pc_grpc_py

docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/tensorflow:2.9.1 /bin/bash
pip install tensorflow-serving-api==2.9.1

./main.py -h

You will get results like:

outputs {
  key: "outputs"
  value {
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: 1
      }
    }
    float_val: 150.6072540283203
  }
}
model_spec {
  name: "konohana"
  version {
    value: 1218
  }
  signature_name: "konohana"
}

4.2. Python HTTP client

cd clients/pred_pc_http_py

python -mvenv venv
. venv/bin/activate
pip install -r requirements.txt

./main.py -h

You will get results like:

{
    "outputs": [
        [
            150.607254
        ]
    ]
}

4.3. Go gRPC client

cd clients/pred_pc_grpc_go
./prepare.sh

go run main.go -h

You will get results like:

name:"konohana" version:<value:1218 > signature_name:"konohana" 
map[outputs:dtype:DT_FLOAT tensor_shape:<dim:<size:1 > dim:<size:1 > > float_val:150.60725 ]
150.607254

Note

You can rebuild the TensorFlow Serving gRPC client by running:

./prepare.sh

4.4. Go HTTP client

cd clients/pred_pc_http_go
go run main.go -h

You will get results like:

{
    "outputs": [
        [
            150.607254
        ]
    ]
}