GP

GPU Watch – Get notified once cloud GPUs have available capacity

Hacker News

GPU Watch – Get notified once cloud GPUs have available capacity

Hi HN! GPU Watch continuously monitors a list of Cloud GPUs you'd like to use (in the background of the tab), and notifies you once there is available capacity for the Cloud GPUs you've chosen. Try it at: https://gpu-watch.vercel.app/ How does it work? 1. Select the Cloud GPUs you'd like to use 2. Click the "Start Watching" button & leave the tab open in the background 3. Once any of your selected Cloud GPUs have available capacity, you'll get a desktop notification Currently, it only supports Lambda Labs, but I will add support for other cloud providers if there's interest. I built this to help with scheduling my own ML training jobs, and thought it would be nice to ship this, in case others find it useful! :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
80%80% 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: open · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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