What is Docker in Python?

Docker is an open-source tool that automates the deployment of an application inside a software container. When you develop an application, you need to provide your code along with all possible dependencies like libraries, the web server, databases, etc. You may end up in a situation when the application is working on your computer, but won’t even start on the staging server, or the dev or QA’s machine. This challenge can be addressed by isolating the app to make it independent of the system.

How to use Docker with Python?

To build a Docker image, you need to create a Dockerfile. It is a plain text file with instructions and arguments. When Dockerfile is ready, use docker build command to build a new image. After that, you can create containers and run them (using docker run command) from the image.

What are the best practices of using Docker in Python?

Include only necessary context – use a .dockerignore file (like .gitignore in git). Avoid installing unnecessary packages – it will consume extra disk space. Use cache - add context that changes a lot at the end of Dockerfile to utilize Docker cache effectively. Be careful with volumes - because volumes are persistent, the next container will use data from the volume created by the previous container. Use environment variables (in RUN, EXPOSE, VOLUME) - it will make your Dockerfile more flexible.

How to hire a developer with Docker for Python experience?

The Django Stars engineers who contributed to this guide have extensive experience developing Python and Django applications. Contact them via the contact form on this page - it is the easiest way to hire qualified and Docker-savvy developers for your project.

What are the benefits of using Docker with Python for my product?

The short list of benefits includes a faster development process, handy application encapsulation, the same behaviour on local machine / dev / staging / production servers, easy and clear monitoring, and ease to scale.

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