omegaconf
pytorch_tempest
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| omegaconf | pytorch_tempest | |
|---|---|---|
| 3 | 3 | |
| 1,788 | 201 | |
| - | - | |
| 6.8 | 5.0 | |
| 29 days ago | 6 months ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
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_tempest
- For what reason do you, or don't you, use PyTorch Lightning?
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If you're using Python, you should probably use a code formatter. (e.g. black)
Here you can see an example of ci in my training pipeline: https://github.com/Erlemar/pytorch_tempest/blob/master/.github/workflows/ci.yml
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Nicest and cleanest Deep Learning codebases out there
DL is all experimentation. You need a framework that makes modification easy, trackable and reproducible. The closest to achieving that these days is based on pytorch lightning with hydra. Check out this video and repo: https://www.youtube.com/watch?v=w10WrRA-6uI https://github.com/Erlemar/pytorch_tempest
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]
lightning-hydra-template - PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
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.
deepchem - Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
nn-template - Generic template to bootstrap your PyTorch project.
gybe - A simple YAML transpiler for rendering Kubernetes manifests using python type-hints.
black - The uncompromising Python code formatter
jinsi - JSON/YAML homoiconic templating language
xdgconfig - Easy access to ~/.config from python
python-decouple - Strict separation of config from code.