Donny Greenberg

What if we made building and deploying FastAPI apps even faster? 🤯

“LudicrouslyFastAPI:” Deploy Python Functions as APIs on your own Infrastructure

Lean, ergonomic, multi-cluster dispatch for high-velocity AI teams

Better GPU Cluster Scheduling with Runhouse

Caroline Chen

Matt Kandler

Josh Lewittes

You deserve better than pasting API keys into environment variables

Simple, Secure Secrets Management for AI

Deploy and Run Your Code on SageMaker in 10 Minutes with Runhouse

The Quickest AWS SageMaker Deployment in Existence

Denis K

Get started with multimodal conversational models using the open-source LLaVA-1.5 model, Hugging Face Transformers, and Runhouse.

Create a Visual Chatbot on AWS EC2 with LLaVA-1.5

Machine Learning's hellish developer experience and research-to-production drag are largely due to widespread over-packaging - coupling systems rigidly together through misuse of containers and pipelines. There's finally an end in sight.

Please separate orchestration and execution (Or: Packaging hell is dragging ML)

Buckle up, this is less a release blog post and more a wild ride through the Infra API Wonka Factory (prepare to see some $*%t, Charlie). We’ll talk through the innocent motivations which turned into upending half of Runhouse’s core execution model, and the gaggle of awesome new features which fell out.

Runhouse 0.0.12: rh.Module, Streaming, and too much more

Platform overhead occupies 50-75% of ML engineers’ and researchers’ time. Runhouse is a PyTorch-like unified interface to solve that.

A PyTorch Approach to ML Infrastructure

Part 1 of a series on data pipelines for the modern ML stack

Supercharging Apache Airflow with Runhouse

Reproducible, cloud-agnostic GPU setup and remote code deployment using Runhouse, a walkthrough of Hugging Face’s Keras Dreambooth Event

Runhouse Blog