An Online Training Framework for AI4Science Applications

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Hey, I've been designing a framework to improve the training of deep learning models on expensive numerical simulations. Instead of relying on limited, pre-generated dataset, the approach streams data directly from large-scale numerical simulations running simultaneously with training. Because it avoids I/O bottlenecks it makes training faster and cheaper. It is implemented as an asynchronous iterable dataset using ZMQ for data communication. I would love your thoughts on how to extend this to a full-fledged event-driven application. Although the framework target cluster environments, the link contains code you can try locally. Check it out and let me know what you think!

Visit product View on Hacker NewsOct 23, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos

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: io · Missing: https docs, excited, just released

68%68% 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 · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

21%21% predicted probability of success on TrustMRR, 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.

Incorrect prediction on native model

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