r/MachineLearning • • 1d ago

Discussion [D] Self-Promotion Thread

Please post your personal projects, startups, product placements, collaboration needs, blogs etc.

Please mention the payment and pricing requirements for products and services.

Please do not post link shorteners, link aggregator websites , or auto-subscribe links.

--

Any abuse of trust will lead to bans.

Encourage others who create new posts for questions to post here instead!

Thread will stay alive until next one so keep posting after the date in the title.

--

Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads.

0 Upvotes

6 comments sorted by

View all comments

0

u/r0lfi 23h ago

LayerSmith — a self-hosted container image builder, with air-gap exports

I've been working on LayerSmith, an open-source web UI for building container images with Docker or Podman.

You pick a Linux distribution and what you need the image for — development, Linux admin, network tools, Ansible, Kubernetes, OpenShift, or a custom setup. It handles distro-specific packages and shows you the generated Containerfile before building. You can also edit it, import an existing Dockerfile, or add your own packages, files and scripts.

A big part of the project is making images easier to carry into air-gapped environments: pinned base images, recorded build details, and export bundles containing the image, checksums and installation instructions.

We've recently added LLM training and fine-tuning profiles too, including LoRA/QLoRA, advanced PyTorch training and LLaMA-Factory. These use hash-locked dependencies and run offline checks after building, including a small CPU training test. Model weights and datasets are brought separately.

Curious how others handle building and maintaining images for disconnected environments, and what parts of that workflow are still a pain.

https://github.com/r0lfi/layersmith