omegaconf
pytorch-lightning
Our great sponsors
| omegaconf | pytorch-lightning | |
|---|---|---|
| 3 | 8 | |
| 1,794 | 26,797 | |
| - | 1.7% | |
| 6.8 | 9.9 | |
| about 1 month ago | 6 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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omegaconf
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OmegaConf module not found: Deforum_Stable_Diffusion.ipynb
!pip install -e git+https://github.com/omry/omegaconf.git#egg=omegaconf
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What's the point of config files in another format?
OmegaConf is really easy to work with.
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Nicest and cleanest Deep Learning codebases out there
Thanks for sharing this, this is probably the best thing here. What makes Hydra really cool is the config system, which is done using OmegaConf (https://github.com/omry/omegaconf), and I especially enjoy the option of defining the configs using Python Data Classes.
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?
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]
lnd - Lightning Network Daemon ⚡️
parse_it - A python library for parsing multiple types of config files, envvars & command line arguments that takes the headache out of setting app configurations.
Eclair - A scala implementation of the Lightning Network.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
mmdetection - OpenMMLab Detection Toolbox and Benchmark
gybe - A simple YAML transpiler for rendering Kubernetes manifests using python type-hints.
composer - Supercharge Your Model Training
jinsi - JSON/YAML homoiconic templating language
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.
xdgconfig - Easy access to ~/.config from python
Keras - Deep Learning for humans