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Slurmq – GPU quota enforcement for Slurm

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Slurmq – GPU quota enforcement for Slurm

When I was a student at Princeton, we had this neat Slurm quota management tool ( https://github.com/klieret/pli-slurm-tool ) that prevented people from hogging all the GPUs. It was really specific to Princeton’s clusters, though, so I decided to make a generalized version for everyone to use: slurmq Slurm's built-in fairshare only deprioritizes heavy users. Sometimes you need a hard cap. slurmq tracks GPU-hours per user over a rolling window and kills jobs when they go over quota set by the sysadmin. Some quick commands: $ pip install slurmq $ slurmq check # check your quota $ slurmq report # admin: see who's over limit $ slurmq monitor --once --enforce # cron: warn, then cancel Docs: https://dedalus-labs.github.io/slurmq Source: https://github.com/dedalus-labs/slurmq Hoping this helps other HPC sysadmins. We're using it internally and would love to hear how others handle GPU quota enforcement.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, using · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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