ML_dep

A deployed machine learning REST API using Django and Docker.

Python 3

For development

1. Clone this repo.

   git clone https://github.com/juccaicedoac03/ML_dep.git
   

2. Create a virtual environment in ML_dep folder and install requirements.txt.

   mkdir mldep_env
   python -m venv mldep_env
   source ml_env/bin/activate
   pip install requirements.txt
   

3. Make API model migrations.

   cd Project_MLdep
   python manage.py migrate
   

4. Run server.

   python manage.py runserver
   

   And in our browser (or in postman), make a request GET a http://127.0.0.1:8000/api/test

For deployment

You can create or donwload a Docker image:

Creating a Docker image

Once our project is inside a github repository, and Docker is installed, We will create a directory in to make our Dockerfile.

mkdir MLdep_docker
cd MLdep_docker

We create a Dockerfile and write the steps for creating the image that contains everything we need.

  # syntax=docker/dockerfile:1
FROM python:3
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
RUN git clone https://github.com/juccaicedoac03/ML_dep
WORKDIR "ML_dep/"
RUN pip install gdown
RUN pip install -U git+https://github.com/juccaicedoac03/ML_dep.git >/dev/null
RUN pip install -r requirements.txt
WORKDIR "Project_MLdep/"
RUN gdown 1lWfMIZLNehFV65AgeJK_UjSToylPFiBi # Google drive link of ML model
RUN python manage.py migrate 

Also, we create a docker-compose.yml file with:

version: "3.9"
   
services:    
  web:
    build: .
    command:  python manage.py runserver 0.0.0.0:8000
    container_name: ML_dep_container
    ports:
      - "8000:8000"

Finally run compose

docker-compose up  

Or using a predefined Docker image

docker pull juccaicedoac/mldep_docker_web:latest
docker-compose up  

REST API

The ML REST API methods are described below.

HOST: http://0.0.0.0:8000/

List of items

Request

GET

/api/Orders/ 

Success Reponse

{
  "message": "Success",
  "orders": [
    {
      "order_id": 15031281,
      "store_id": 900006856,
      "to_user_distance": 1.298663,
      "to_user_elevation": -19.70105,
      "total_earning": 5500,
      "created_at": "2017-09-19T15:36:49Z",
      "taken": false
    },
    {
      "order_id": 15213594,
      "store_id": 900011745,
      "to_user_distance": 0.750715,
      "to_user_elevation": -34.016602,
      "total_earning": 5700,
      "created_at": "2017-09-22T21:38:58Z",
      "taken": true
    }
  ]
}

Error Reponse

{
  "message": "No orders found",
  
}

Read Item

Request

GET

/api/Orders/order_id

URL Params

order_id=[integer]

Success Reponse

{
  "message": "Success",
  "orders": [
    {
      "order_id": 15031281,
      "store_id": 900006856,
      "to_user_distance": 1.298663,
      "to_user_elevation": -19.70105,
      "total_earning": 5500,
      "created_at": "2017-09-19T15:36:49Z",
      "taken": false
    },
  ]
}

Error Reponse

{
  "message": "No orders found",
}

Make inference

Request

POST

/api/Orders/

Request Body: one by one or in batch

{
  "orders": [
    {
      "order_id": 15031281,
      "store_id": 900006856,
      "to_user_distance": 1.298663,
      "to_user_elevation": -19.70105,
      "total_earning": 5500,
      "created_at": "2017-09-19T15:36:49Z"
    },
    {
      "order_id": 15213594,
      "store_id": 900011745,
      "to_user_distance": 0.750715,
      "to_user_elevation": -34.016602,
      "total_earning": 5700,
      "created_at": "2017-09-22T21:38:58Z"
    }
  ]
}

Success Reponse

{
  "message": "Success",
}

Error Reponse

{
  "message": "No orders found",
}

Update Item

Request

PUT

/api/Orders/order_id/

URL Params

order_id=[integer]

Request Body:

{
  "orders": [
    {
      "order_id": 15031281,
      "store_id": 900006856,
      "to_user_distance": 1.298663,
      "to_user_elevation": -19.70105,
      "total_earning": 5500,
      "created_at": "2017-09-19T15:36:49Z",
      "taken": false
    },
  ]
}

Success Reponse

{
  "message": "Success",
}

Error Reponse

{
  "message": "No orders found",
}

Delete Item

Request

DELETE

/api/Orders/order_id/

URL Params

order_id=[integer]

Success Reponse

{
  "message": "Success",
}

Error Reponse

{
  "message": "No orders found",
  
}