.dockerignore

data/
.env/
__pycache__
.ipynb_checkpoints

.gitignore

Training Report.pdf
*.tar.gz
handler_venv/
*.nbconvert.*
static/
env/
*.pkl
.env
.ipynb_checkpoints
**/__pycache__

.python-version

3.8.0

README.md

Energy Efficiency Predictor

Problem

The goal of this challenge is to build a regression model and deploy it with docker. The dataset you will use for the challenge is available at https://archive.ics.uci.edu/ml/datasets/Energy+efficiency. You should be able to run the docker image and then curl the container by sending json containing the attributes of a new building and get a json response with the heating and cooling loads predicted by your trained model. The code should be written in python but you can use whichever libraries you like to train and deploy the model

Solution

I built a simple multivariate, Lasso model with Scikit-learn that is served with Flask.

You can see the notebook used to train the model at https://9whioydhmb.execute-api.us-east-1.amazonaws.com/carbonrelay.

Model can be tested by posting a dictionary of input values to an AWS endpoint, e.g.:

curl -d '{
    "relative_compactness": 0.98,
    "surface_area": 514.5,
    "wall_area": 294.0,
    "roof_area": 110.25,
    "overall_height": 7.0,
    "orientation": 2.0,
    "glazing_area": 0.0,
    "glazing_area_distribution": 0.0
}' -H 'Content-Type: application/json' https://9whioydhmb.execute-api.us-east-1.amazonaws.com/carbonrelay/predict

HTTP response is JSON containing an heating_load and cooling_load field.

Building and Testing

Prerequisites

• Running Docker client

Steps

Run $ docker-compose up to train model and open webserver on port 5000. Once this completes, you should be able to run:

curl -d '{
    "relative_compactness": 0.98,
    "surface_area": 514.5,
    "wall_area": 294.0,
    "roof_area": 110.25,
    "overall_height": 7.0,
    "orientation": 2.0,
    "glazing_area": 0.0,
    "glazing_area_distribution": 0.0
}' -H 'Content-Type: application/json' http://127.0.0.1:5000/predict

You can also view the Jupyter notebook with training information and model performance information at http://127.0.0.1:5000/.

app.py

import joblib
import pandas as pd

from flask import Flask, request

app = Flask("energy_efficiency")


@app.route("/")
def report():
    return app.send_static_file("Train.html")


@app.route("/predict", methods=["POST"])
def predict():
    data = pd.DataFrame.from_records([request.get_json()])
    pipe = joblib.load("model.pkl")
    output = pipe.predict(data)
    assert output.shape == (1, 2)
    return {
        "heating_load": output[0][0],
        "cooling_load": output[0][1],
    }

docker-compose.yml

version: '3'
services:
  web-server:
    build: .
    entrypoint: flask run -h 0.0.0.0
    ports:
      - "5000:5000"
    volumes:
      - .:/home
  train:
    build: .
    entrypoint: /bin/sh
    command: /home/train.sh
    volumes:
        - .:/home

Dockerfile

FROM fnndsc/ubuntu-python3
RUN apt -qq install --yes build-essential
RUN pip install --quiet --upgrade pip
WORKDIR /home
COPY . ./
RUN pip install -r requirements.txt
ENV FLASK_APP=app
ENV LC_ALL=C.UTF-8
ENV LANG=C.UTF-8

ENB2012_data.xlsx

requirements.txt

scikit-learn
flask
jupyter
pandas
xlrd

Train.ipynb

train.sh

#!/bin/bash

jupyter nbconvert --to html --execute Train.ipynb --ExecutePreprocessor.timeout=600
mkdir -p static
mv Train.html static

zappa_settings.json

{
    "carbonrelay": {
        "app_function": "app.app",
        "aws_region": "us-east-1",
        "project_name": "energy_efficiency",
        "runtime": "python3.8",
        "s3_bucket": "zappa-carbon-relay-takehome",
        "slim_handler": true
    }
}