Optimizing Docker Images: A Practical Guide to Slimming Down
Let’s address a common issue that many of us in the IT and development world face: bulky Docker images. It’s no secret that a significant number of us are guilty of shipping containers that are larger than they should be. For instance, if you’re working with ML models, chances are your Docker images exceed 2GB. Personally, I’ve encountered containers that were a hefty 3GB until recently.
Despite being well-versed in best practices, the convenience of starting with a generic base image such as FROM pytorch/pytorch often wins when under pressure to deploy models swiftly. In this article, we will delve into the practical aspects of streamlining Docker images. We’ll explore the often-overlooked compromises and complexities involved in this process.