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Deploy Hugging Face Models to AWS Lambda

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Deploy Hugging Face Models to AWS Lambda

I've been working on Scaffoldly since 2020 to simplify AWS Lambda deployments. Recently discovered you can run Hugging Face models efficiently using EFS for caching. Here's what's interesting: - Uses EFS for model file persistence - Pre-downloads models after deployment for faster cold starts - Cold start: ~20s (model loading), warm requests: 5-20s (CPU inference) - Fully automated container builds and deployment - Works with private/gated models via HF_TOKEN Example deployment: npx scaffoldly create app --template python-huggingface cd python-huggingface && npx scaffoldly deploy Scaffoldly is Open Source and I'm excited for all feedback and contributions from the community! https://github.com/scaffoldly/scaffoldly https://github.com/scaffoldly/scaffoldly-examples/tree/pytho...

Visit productView on Hacker NewsNov 26, 2024
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, io · Missing: https docs, just released, exist
61%61% 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
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
23%23% 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
16%16% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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