Well, this is Runhouse: Dispatch code to servers. The powerful AI devx we've synthesized from hundreds of companies' AI platforms, now yours in OSS. If you're an AI team that wants high-velocity and robustness out of the box, give it a try: github.com/run-house/runh…

Check out how Runhouse unbundles your cluster scheduling to give you lean, resource-efficient, and ergonomic execution across many clusters or providers: run.house/blog/better-gp…

Q: How dedicated are we Jack Whitehead to deploying and serving your random existing jank-ass Python? A: yes

📢🦙Try Llama3 on AWS EC2, right now!📢🦙 Why is it so hard to stand up a model on simple compute inside your cloud account? Everyone wants you to use their hosted compute, model endpoint, IDE, and/or DSL, but I just want regular Python on a regular box. Runhouse makes it so

Wrapped up a redesign project for the Runhouse homepage last week - run.house Thoughts? Any way we could we improve the messaging?

I'm absolutely riled up about this Runhouse code sample. This is what a year of cooking the best AI/ML DevX gets you. Distributed bayesian hyperparameter optimization on any infra, from scratch, in readable idiomatic Python. Step 1: Send your training function *as-is* to infra

Early on in 🏃‍♀️Runhouse🏠, the creator of a major ML platform told me he’s not sure if it’s possible for one DevX to support both workflow/training pipelines (long running, throughput-bound) and serving/inference pipelines (fast, latency-bound). We took that personally. We’re now

Making ML infrastructure fun is one of the biggest opportunities in enterprise SaaS. If that sentence gets you hyped up you've found your people 👇 run.house/about#hiring

Join us for our #TechWeekNYC kickoff event on June 3! Don't miss an afternoon with the hottest #generativeAI speakers like Kay, Runhouse, Articul8 AI & Squid AI. 🤖 Chat with Lux Capital Partner + AWS experts. Prove what's possible with the #AI! go.aws/4bsQ62s

You would *think* having access to a cloud account or big cluster would make programming fun. You would think that your "hyperscaler" would make parallelizing a simple operation easy - that should be the "Hello world" of having a cloud account! You'd be wrong, until now. We've

Runhouse Runhouse x SkyPilot Great post from Donny Greenberg on how Runhouse leverages SkyPilot to make scaling batch processing on any cloud easy:

💔 Ripping open the "Orchestrator" For a long time I cringed when people called Runhouse an orchestrator. It was built to solve development and execution across ML infra *without* an orchestrator, so you get the iteration, debugging, and flexibility of local Python back. I've

We've seen these same flexibility wins onboarding users to Ray, with cost savings of 50% or more being the norm. My only edit is in the final bullet: with Runhouse you don't need to tunnel in a notebook to interactively use the Ray cluster, we solve your Python<>Ray interface

If you love the interactivity and visualizations of notebooks but want the determinism and power of ML pipelines, you should be using marimo with Runhouse!

📢 Calling all Machine Learning Engineering practitioners & builders! Work-Bench & Runhouse 🏃🏡 are kicking off the Fall season 🍂🍁 with a happy hour on Tue, Sep 10th! 🍻 Join us for drinks and snacks and come nerd out with fellow ML engineers in NYC. We have folks from

Join our ML Engineering Happy Hour hosted by Priyanka Somrah Donny Greenberg & the Runhouse team 🍻 ​We're gathering our NYC technical community of practitioners across the ML Engineering ecosystem to come grab a drink, mingle & kick off the Fall 🍁🍂 lu.ma/MachineLearnin…

🚨#RaySummit speaker session alert!🚨 Join Runhouse as they reveal a new system that transforms Ray from single-player per cluster to multiplayer 🚀 Discover how they're overcoming cluster restrictions and introducing a collaborative UI, making AI resources accessible—like a