Sp

Spell – simple, remote GPU execution

Hacker News

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: 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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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: filesystem, ide, pipe · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, efficient · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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