Spell – simple, remote GPU execution
We'd like to introduce HN to Spell, which is a tool for easily running ML/DL jobs remotely. As Deep Learning has grown we see engineers and researchers struggle to incorporate running on GPUs into their workflow. So we built Spell to be the easiest way to get code running elsewhere - like the bash '&' operator but for remote machines. Sign up for an account at https://web.spell.run/waitlist, which includes $300 in credits for GPU time. There's a waitlist, but we'll be approving accounts as they come in. Here are some of the features we really wanted and built into Spell:…
What it does
In the maker’s words, at launch
We'd like to introduce HN to Spell, which is a tool for easily running ML/DL jobs remotely. As Deep Learning has grown we see engineers and researchers struggle to incorporate running on GPUs into their workflow. So we built Spell to be the easiest way to get code running elsewhere - like the bash '&' operator but for remote machines. Sign up for an account at https://web.spell.run/waitlist, which includes $300 in credits for GPU time. There's a waitlist, but we'll be approving accounts as they come in. Here are some of the features we really wanted and built into Spell: 1) We don't want you to have to modify your code for it to run remotely. Particularly so it's easy to clone something off github and run it with one command 2) Waiting for data to upload/move is one of the biggest time costs. So we built a single remote filesystem so you only need to upload data once. It's also a simple interface that everyone already understands 3) Tools should be highly efficient and reliable. Machine learning needs more tools that are sturdy enough to use everyday and build whole pipelines around. So we spent a lot of time making sure our infrastructure is solid There's plenty of documentation (https://www.spell.run/docs), including a 30 second quick start and guides for running some common models. We also wrote a series of medium posts (https://medium.com/@spellrun) where we reproduce as much of the work presented at ICLR as possible with a few commands. Thanks for reading. Questions/feedback welcome!
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