No intro, let's jump to the steps.
1. Write a Dockerfile 😐 (why not)
This is my directory structure -
├── app/
│ .....
├── requirements.txt
├── Dockerfile
└── gunicorn_starter.sh
This is the Dockerfile -
FROM python:3.7.3-slim
WORKDIR /user/src
COPY ./requirements.txt ./
RUN pip3 install -r ./requirements.txt
COPY ./app ./app
COPY ./gunicorn_starter.sh ./
ENTRYPOINT ["./gunicorn_starter.sh"]
Things to remember -
- Use small docker images like
-slim
or-alpine
.-alpine
is the lightest but in my case-alpine
haven't got thegcc
so I used-slim
. (I could've install gcc manually in alpine) - Import requirements and install them before importing codes. Why? If we change our code, we won't need to install requirements in every build. Simple and easy way to reduce build times 🤗
2. Gunicorn server
Why Gunicorn? 🧐 -> Django or Flask is a framework, not server. So we need a server to serve our application built with the framework. Gunicorn is a WSGI supported server that can communicate with other application that supports WSGI like Flask or Django. WSGI is a gateway interface that matches the URI defined in the python application.
Now install gunicorn and add to the requirements.txt
pip install gunicorn
pip freeze > requirements.txt
This is the gunicorn_starter.sh
file we have seen earlier -
gunicorn --chdir app wsgi:app -w 2 --threads 2 -b 0.0.0.0:8003
We show gunicorn our project directory with --chdir app
. wsgi:app
for Django apps and for Flask, we need the file name where we did app = Flask()
, like this - <file_contains_app>:app
. Change worker and thread as per your need. Too much workers actually makes a system slow, why? Because workers are processes, and they shares same CPU core. And we should know our threads right? 😅
3. Build & Deploy (Seek & Destroy ☠️)
Build the docker image -
docker build -t <your_tag> .
This image is production ready! Beleive it or not 😐
Run it 🤘 -
docker run --name <image_name> -p 8003:8003 -e DB_URI=<your_db_uri> <your_tag>
-e DB_URI=<your_db_uri>
here -e
sets the environment variable. I set my DB with URI, so I passed this environment variable DB_URI
while running my image.
This is it! 🥳 If you have any questions or face any problem, comment below 👇
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