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Note: this tutorial is using for using in a AWS ec2 instance (recommended)

Install

sudo -s
apt update

Docker

curl -fsSL https://get.docker.com -o get-docker.sh
sh get-docker.sh

Docker Compose

apt install -y docker-compose

Clone the repo

apt install git
git clone https://github.com/thanhhau097/ml_project_template.git

Change your custom path

cd ml_project_template

api/app.py

Change your bucket in AWS S3 (it is optional, when you need to upload user data to s3 bucket)

async_data = {
    'data': {'image': image_utils.encode(image), 'result': result},
    'bucket': 'your-bucket',
    'object_name': 'file-path-in-bucket/{}.pkl'.format(file_name)
}

nginx/nginx.conf

Change your domain name (optional, it's useful when you need a domain name instead of public IP address)

Comment out the SSL config in this file if you don't have domain name. (4-6, 29-40)

init-letsencrypt.sh

Note: if you don't have a domain name, please comment the certbot service in docker-compose.yml file.

Change your email and domain name (Optional, it is needed when you want your domain can handle HTTPS requests)

Write your code

There are 3 tasks that you need to do for your project:

  1. Write prediction for you model in model/predictor.py and update your weights in model/weights folder
  2. Write your API in api/app.py using Flask framework (you can use the template that was written for image)
  3. Write your web app using ReactJS (you can use the demo template that I wrote in web/)

How to run the service

You should have an AWS EC2 instance instead of building all the things in local machine, because it require a good network for building docker file

./init-letsencrypt.sh # (use when you have a domain name)
docker-compose build 
docker-compose up

Others

  • You can use serverless for your Flask app using api/serverless.yml (tutorial will be update later)