dstack

A nice project—I hadn’t seen it before. I’m working on a similar concept but as an open-source project [1]. We see it as an alternative to K8s/Slurm and would be interested in hearing thoughts from other ML engineers. [1] https://github.com/dstackai/dstack

Hey HN, founder of dstack here. We've been working on this over three months and pretty excited about this release. Basically, the main point is that dstack is an open-source AI-native alternative to Kubernetes, designed to be more lightweight, and focusing just on AI workloads on both cloud and data-centers. With this release we are adding the critical feature that allows to run containers concurrently on same host slicing its resources incl. GPU for a more cost-efficient utilization. Another new thing is the simplified way to run things on private clouds where clusters are often behind a login node. There are many more cool things on our roadmap to ensure dstack is a streamlined alternative to both K8S and Slurm. Our roadmap can be found in [1] Super excited to hear any feedback. [1] https://github.com/dstackai/dstack/issues/2184

I can completely relate to anyone abandoning K8s. I'm working with dstack, an open-source alternative to K8s for AI infra [1]. We talk to many people who are frustrated with K8s, especially for GPU and AI workloads. [1] https://github.com/dstackai/dstack

That’s on our list! We’re actually planning to expand our examples page with a lot more frameworks! Contributors are very welcome to our repo: https://github.com/dstackai/dstack/tree/master/examples/

it appears you have borked all the doc links from your readme (e.g.) https://github.com/dstackai/dstack#1-define-configurations

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 https://github.com/dstackai/dstack to enable 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!

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

SEEKING CO-FOUNDER | Dev tools/MLOps | Tech co-founder | US/EU/Worldwide | Seed We aim at simplifying the MLOps stack. The first OSS version is out: https://github.com/dstackai/dstack. Plan to raise a seed round shortly. Drop me a message at andrey at dstack.ai, if you’re interested.

Just curious, what do you use for hosting the blog or landing page itself? For example, we use GitHub Pages for hosting the landing page. So for hosting images, we use GitHub itself and it mostly works. For the blog, we currently use Substack, but thinking of maybe moving it to GitHub Pages too. FYI, here is our repo: https://github.com/dstackai/dstack

Hi. :) I’m Andrey, the creator of dstack. I started this project while I was working at JetBrains where I helped the PyCharm team to improve support for Jupyter notebooks. As I was in close contact with many ML devs (who used PyCharm) I was able to see their struggle with running ML workflows. Unlike traditional dev workflows, ML workflows are difficult to run on a local machine (due to the lack of memory, more CPUs/GPUs, etc). This is why people often have to use remote machines (e.g. via SSH), or adopt one of the end-to-end MLOps platforms. Using remote machines is not difficult but it is tedious and requires a lot of manual actions. Using MLOps platforms on the other hand automates the manual work but requires the use of an opinionated interface, which often kills developer productivity. Imagine, if you could run your ML workflows the

Hi everyone, I'm the creator of dstack, a tool that makes it easier to train models in the cloud. Our tool allows extending it with custom providers to support different languages, frameworks, etc. All the built-in providers are also open-source: https://github.com/dstackai/dstack Today, we've released a new update that extends the capabilities of dstack beyond training models, and now also allows users to quickly build and share apps with Streamlit, Gradio, and FastAPI in the cloud – in just a few clicks. Also, similar to apps, it's possible to run dev environments with the required hardware and data access also in one command from the Terminal. All you have to do is to link your own AWS account to run commands. Currently, we support VS Code, and JupyterLab but plan to support more. Invite everyone to give it a try and share their though