dstack VS salty

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.

Was there any thought to perhaps do a version with an agent? I really like how fast Saltstack can be as compared to Ansible.

I've been using my own homegrown project that does just this - Python roles, server/client, Mako templates: https://github.com/mattbillenstein/salty

It's very very fast to do deploys on long-lived infrastructure, but it hasn't been optimized for large clusters yet; I expect the server process will be a bottleneck with many clients, but still probably faster than Ansible for most setups.

And running my own deployment system on this one: https://github.com/mattbillenstein/salty

Slack chatops on top of Saltstack (although I experrimenting with rolling my own https://github.com/mattbillenstein/salty)

I have a couple of comments and a question:

* please "set -e" and ideally "-euo pipefail" in shell scripts, because encountering an error and defaulting to "this is fine" is usually not going to produce good outcomes in a server automation suite: https://github.com/mattbillenstein/salty/blob/master/example...

* if this had an "else: return 1" equivalent, it wouldn't cause the salty.py to exit(0) on a misunderstood mode: https://github.com/mattbillenstein/salty/blob/master/salty.p...

* maybe this is my ignorance of saltstack, but is there no mechanism through which a sane python editor could help one know what verbs and params are available here? https://github.com/mattbillenstein/salty/blob/master/example...

For example, if those were "from salty import copy" a sane python editor could help in all kinds of ways, but with them just being implicit, the editor doesn't know and now I have to maintain a whole api in my head