Data Science Model encapsulation in Python Microservice

The Flask REST Microservice deploys the Data science model into memory, which can then be used to get Recommendations through a HTTP GET Endpoint.

The service also exposes a HTTP GET through which a new trained model can be loaded into the memory.

Table of contents

• Installation
  • Setup virtual env
• Build and run
  • Using Docker
  • Using shell script
  • Logs
• Release History
• Contribute

Installation

Setup virtual env

####OS X & Linux:

# Install Virtualenv
pip3 install virtualenv

# Verify Virtualenv installation
virtualenv --version

# Create directory for virtual
mkdir -p ~/interpreter/python/3.7/

# Create virtualenv
cd ~/interpreter/python/3.7/
virtualenv -p /usr/bin/python3.7 venv3.7 #Ensure python 3.7 is located /usr/bin/python3.7, if not, then provide the path where python3.7 is installed.

# Activate virtualenv
source ~/interpreter/python/3.7/venv3.7/bin/activate

# Install the requirements in activated virtualenv
pip -r install requirements.txt

# It's recommended to freeze the env, you can do this with below:
pip freeze > requirements.txt

# Deactivate virtualenv
deactivate

Build and run

Using Docker

The service is Docker'zed. Below are the steps to build and run the Docker image.

# Below command builds the Docker image.
docker build -t data-model-service:v1 .

# Below command runs the docker image on port 5000.
# Sets the SERVICE_ENV environment variable in Docker container.
# The value "dev" is used to take Development configuration.
docker run -p 5000:5000 -e PORT=5000 -e SERVICE_ENV=dev data-model-service:v1

Using shell script

Run below commands to run the Microservice from shell script in background.

export SERVICE_ENV="dev" # Runs the application in Development configuration. Change to "qa" or "prod" based on environment.
chmod +x start.sh
nohup ./scripts/start.sh &

Logs

Local system

# Navigate to path /var/log/ml-price-recommendation-api
cd /var/log/ml-price-recommendation-api

# Gunicorn access logs path
tail -f /var/log/ml-price-recommendation-api/access.log

# Application log path
tail -f /var/log/ml-price-recommendation-api/application.log

Docker

# Find the docker CONTAINER_ID based on the Image tag: data-model-service:v1
docker ps | grep "data-model-service:v1" | cut -d" " -f1

# Access the docker shell
docker exec -it <CONTAINER_ID> /bin/sh

# Navigate to path /var/log/ml-price-recommendation-api
cd /var/log/ml-price-recommendation-api

# Gunicorn access logs path
tail -f /var/log/ml-price-recommendation-api/access.log

# Application log path
tail -f /var/log/ml-price-recommendation-api/application.log

Release History

• 1.0.0
  • Released v1 of Data Model service.

Issue List

Current Issues

Contribute

If you want to be a contributor please follow the below steps.

1. Fork it (https://github.com/saurabh-slacklife/ml-data-model-microservice/fork)
2. Create your feature branch (git checkout -b feature/add-feature-xyz)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/add-feature-xyz)
5. Create a new Pull Request