snorkel
pytorch-lightning
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| snorkel | pytorch-lightning | |
|---|---|---|
| 5 | 8 | |
| 5,707 | 26,883 | |
| 0.8% | 1.7% | |
| 5.5 | 9.9 | |
| about 2 months ago | 1 day ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
snorkel
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel
- [Discussion] - "data sourcing will be more important than model building in the era of foundational model fine-tuning"
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Can't use load_data from utils
Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo
- [D] A hand-picked selection of the best Python ML Libraries of 2021
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[Discussion] Methods for enhancing high-quality dataset A with low-quality dataset
Snorkel (https://github.com/snorkel-team/snorkel) might provide you exactly what you are looking for. From the docs:
pytorch-lightning
- Lightning AI Studios – A persistent GPU cloud environment
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Como empezar con inteligencia artificial?
https://see.stanford.edu/Course/CS229 https://lightning.ai/ https://www.youtube.com/watch?v=00s9ireCnCw&t=57s https://towardsdatascience.com/
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Best practice for saving logits/activation values of model in PyTorch Lightning
I've been wondering on what is the recommended method of saving logits/activations using PyTorch Lightning. I've looked at Callbacks, Loggers and ModelHooks but none of the use-cases seem to be for this kind of activity (even if I were to create my own custom variants of each utility). The ModelCheckpoint Callback in its utility makes me feel like custom Callbacks would be the way to go but I'm not quite sure. This closed GitHub issue does address my issue to some extent.
- New to ML, which is easier to learn - Tensorflow or PyTorch?
- PyTorch Lightning – DL framework to train, deploy, and ship AI fast
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We just release a complete open-source solution for accelerating Stable Diffusion pretraining and fine-tuning!
Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!
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An elegant and strong PyTorch Trainer
For lightweight use, pytorch-lightning is too heavy, and its source code will be very difficult for beginners to read, at least for me.
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[D] Mixed Precision Training: Difference between BF16 and FP16
For the A100 GPU, theoretical performance is the same for FP16/BF16 and both rely on the same number of bits, meaning memory should be the same. However since it's quite newly added to PyTorch, performance seems to still be dependent on underlying operators used (pytorch lightning debugging in progress here).
What are some alternatives?
skweak - skweak: A software toolkit for weak supervision applied to NLP tasks
lnd - Lightning Network Daemon ⚡️
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
Eclair - A scala implementation of the Lightning Network.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
mmdetection - OpenMMLab Detection Toolbox and Benchmark
weasel - Weakly Supervised End-to-End Learning (NeurIPS 2021)
composer - Supercharge Your Model Training
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
umbrel - A beautiful home server OS for self-hosting with an app store. Buy a pre-built Umbrel Home with umbrelOS, or install on a Raspberry Pi 4, Pi 5, any Ubuntu/Debian system, or a VPS.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
Keras - Deep Learning for humans