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Introduction
Let’s address a common dilemma faced by many in the tech community: the unwelcome reality of bulky Docker images. It’s not uncommon for Docker containers, especially those housing ML models, to balloon to over 2GB. At times, mine even surpassed the 3GB mark until recently.
Despite being well-versed in best practices, the allure of simplicity often leads us to opt for a straightforward FROM pytorch/pytorch declaration when rushing a model to production. This article is a practical guide shedding light on the nuances of Docker image optimization, uncovering the often-overlooked trade-offs that come into play.