A friendly guide to containerization for AI work One of the biggest headaches associated with AI workloads is wrangling all of the drivers, runtimes, libraries, and other dependencies they need to run. This is especially true for hardware-accelerated tasks where if you've got the wrong version of CUDA, ROCm, or PyTorch there's a good chance you'll be left scratching your …
A fun article. Thanks for the overview :)
> Save the headaches, ship your dependencies
But for the love of ${DEITY}, I hope people try to be slightly responsible with dependencies. Try to not drag in useless shite for something that is simpler, safer and takes 5 minutes to write. Containers conveniently delay issues with lifespan for x86_64 Linux but they don't entirely solve it for solutions that need to exist for decades.
TL;DR; Don't reinvent the wheel but also don't smear dogsh*t on the cart either.
Like any technology it has its time and place, and that's always the trick. I love Docker as a technology, incredibly simple, small footprint and easy to test different versions of apps within minutes, however it doesn't work well with very heavy workloads, you wouldn't spin up an Oracle DB in the docker container, but Postgres and Maria work very well. I had to upgrade an old Jira install a year ago, spend a month testing Jira upgrades with Docker without having to snag a whole server for myself, I was able to run the automated tests on a laptop and the upgrade was completed in half the time allocated.
LM Studio comes in! This handy app for Linux (and Windows/Mac too) makes exploring LLMs a breeze. No coding headaches here – just download pre-trained models and chat with them through a simple search bar. Plus, LM Studio lets you switch between different LLM models on the fly. And the best part? It keeps everything on your machine, so your data stays private. Think of it as a user-friendly playground for AI exploration!
The Register 
Biting the hand that feeds IT
Copyright. All rights reserved © 1998–2025
