Hey everyone, we’re Trevor and Leila from Moonglow!

❌ Problem: moving experiments to cloud GPUs sucks

When you’re doing machine learning research, it’s important to try out new ideas quickly. Jupyter notebooks make that easy. But what happens when your local computer isn’t enough?

Your workflow probably looks like this:

1. Go to your cluster or cloud provider
2. Pick the right configs and spin up a node
3. SSH into the node
4. Install all the required packages
5. Pull your code from GitHub

All of this is before you’ve even run a single cell in your notebook! And if you want to share your work or come back to it later, either you need to keep your GPU running (expensive) or go through this entire process again (slow).

🎉 Solution: Bring your own compute to Jupyter

Moonglow connects your local Jupyter notebooks to your cloud compute provider. With a click of a button, you can switch runtimes and scale up your experiments to the GPUs you need.

We handle all of the messy DevOps under the hood, and since your notebook lives in your local IDE, you can easily come back to it and get it running in seconds!

We currently support connecting notebooks in VS Code / Cursor to Runpod instances, and we’re expanding this to other providers soon e.g. AWS, GCP, Azure, Lambda Labs etc.

👀 Team

Trevor used to do ML research at Stanford, while Leila was a software engineer working on high-performance infrastructure at Jane Street. We started Moonglow because we’ve seen how janky and unintuitive the current tooling is for ML research, and how that is bottlenecking the pace at which researchers can validate their results at scale.

🙏 Asks

• Try out Moonglow or book a time to get set up.
• Let us know which cloud providers we should support next!
• Connect us to ML researchers you know.

We’re excited to hear from you, either at trevor@moonglow.ai or on Linkedin (Trevor, Leila).