With Moonglow, you can start and stop pre-configured remote cloud machines within VSCode, and it makes those servers appear to VSCode like normal Jupyter kernels that you can connect your notebook to.
We built this because we learned from talking to data scientists and ML researchers that scaling up experiments is hard. Most researchers like to start in a Jupyter notebook, but they rapidly hit a wall when they need to scale up to more powerful compute resources. To do so, they need to spin up a remote machine and then also start a Jupyter server on it so they can access it from their laptop. To avoid wasting compute resources, they might end up setting this up and tearing it down multiple times a day.
When Trevor used to do ML research at Stanford, he faced this exact problem: often, he needed to move between cloud providers to find GPU availability. This meant he was constantly clicking through various cloud compute UIs, as well as copying both notebook files and data across different providers over and over again.
Our goal with Moonglow is to make it easy to transfer your dev environment from your local machine to your cloud GPU. If you’ve used Google Colab, you’ve seen how easy it is to switch from a CPU to a GPU - we want to bring that experience to VSCode and Cursor.
If you’re curious, here’s some background on how it works. You can model a local Jupyter server as actually having three parts: a frontend (also known as a notebook), a server and an underlying backend kernel. The frontend is where you enter code into your notebook, the kernel is what actually executes it, and the server in the middle is responsible for spinning up and restarting kernels. Moonglow is a rewrite of this middle server part: where an ordinary Jupyter server would just start and stop kernels, we’ve added extra orchestration that provisions a machine from your cloud, starts a kernel on it, then sets up a tunnel between you and that kernel.
In the demo video (https://www.youtube.com/watch?v=Bf-xTsDT5FQ), you can see Trevor demonstrate how he uses Moonglow to train a ResNet to 94% accuracy on the CIFAR-10 classification benchmark in 1m16s of wall clock time. (In fact, it only takes 5 seconds of H100 time; the rest of it is all setup.)
On privacy: we tunnel your code and notebook output through our servers. We don’t store or log this data if you are bringing your own compute provider. However, we do monitor it if you are using compute provided by us, to make sure that what you are running doesn’t break our compute vendor’s terms of service.
We currently aren’t charging individuals for Moonglow. When we do, we plan to price individuals a reasonable amount per-seat, and we have a business plan for teams with more requirements.
Right now, we support Runpod and AWS. We’ll add support for GCP and Azure soon, too. (If you’d like to use us to connect to your own AWS account, please email me at [email protected].)
For today’s launch on HN only, you can get a free API key at https://app.moonglow.ai/hn-launch. You don’t need to sign in and you don’t need to bring your own compute; we’ll let you run it on servers we provide. This API key will give you enough credit to run Moonglow with an A40 for an hour.
If you're signed in, you won't be able to see the free credits page, but your account will have automatically had free credits added to it.
We’re still very early, and there are a lot of features we’d still like to add, but we’d love to get your feedback on this. We look forward to your comments!
by Andrey
Azimov