tabby VS dstack

https://github.com/TabbyML/tabby can run self-hosted AI coding assistants. I tried it a while ago and it worked with Nvim pretty easily. There is a VS code extension too. The extension will just sort of "read" with you and provide suggestions from time to time. Anytime the suggestion is good you can press some key ( by default) to accept it. It's basically autocomplete on steroids.

NixOS just got tabbyml[1] which is built on llama-cpp. Working on systemsd services the weekend and updating latest tabbyml release which supports rocm in addition to cuda

[1] https://github.com/TabbyML/tabby

[2] https://github.com/NixOS/nixpkgs/pull/291744

Nice implementation! It should serve as a great reference for a minimal Tabby's backend API. Thank you for sharing it!

Yeah - ultimately, it won't be as performant or feature-rich compared to https://github.com/TabbyML/tabby, but it's still perfect for educational purposes!

Looks interesting! What are the main differences between this and https://github.com/TabbyML/tabby ?

TabbyML | Software Engineer (Rust) | REMOTE

Self-hosted AI coding assistant. An opensource / on-prem alternative to GitHub Copilot.

Project: https://github.com/TabbyML/tabby

Tabby is seeking a Software Engineer proficient in Rust to join our core engineering team. In this role, you will be responsible for developing the following features:

There are a bunch of VSCode extensions that make use of local models. Tabby seems to be the most friendly right now, but I admittedly haven't tried it myself: https://tabbyml.github.io/tabby/

We build a similar tool except we focus on AI workloads. Also support on-prem clusters now in addition to GPU clouds. https://github.com/dstackai/dstack

Not exactly this, but something related. At https://github.com/dstackai/dstack, we build an alternative to K8S for AI infra.

You can use https://github.com/dstackai/dstack to deploy your model to the most affordable GPU clouds. It supports auto-scaling and other features.

Disclaimer: I’m the creator of dstack.

Great job on the project! It looks fantastic. Thanks to your post, I discovered Fly's GPUs. We are currently developing a platform called https://github.com/dstackai/dstack that enables users to run any model on any cloud. I am curious if it would be possible to add support for Fly.io as well. If you are interested in collaborating on this, please let me know!

I also noticed that the creator of this app, dstack, is affiliated with Tensordock, the top results for most if not all queries. If that's the case, perhaps a direct link to the cheapest machine could be provided? I haven't used Tensordock, so I don't know if this is mechanically possible.

Here's the repository with all the important links, including documentation, examples, and more: https://github.com/dstackai/dstack

Hey everyone, I'm happy to release dstack Hub, an open-source tool that helps teams manage their ML workflows more effectively without vendor lock-in.

dstack Hub extend dstack [1] with workflow scheduling capabilities and user management. Here's how it works: run dstack Hub via Docker, use its UI to configure projects and cloud credentials, then pass the URL and personal token to the dstack CLI. Now, you can run workflows through the CLI and Hub will orchestrate them in the cloud on your behalf.

This is a beta release and we plan to continuously improve it. We'd love to hear your feedback and answer any questions!

[1] https://github.com/dstackai/dstack

To help you overcome this challenge, we have written an article to guide you through the simple steps of using both diffusers and dstack to generate images from prompts, both locally and in the cloud, using a simple example.