omegaconf
xdgconfig
Our great sponsors
| omegaconf | xdgconfig | |
|---|---|---|
| 3 | 1 | |
| 1,794 | 3 | |
| - | - | |
| 6.8 | 1.8 | |
| about 1 month ago | about 2 years ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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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.
xdgconfig
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]
dynaconf - Configuration Management for Python ⚙
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.
confuse - painless YAML config files for Python
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
cmdkit - A library for developing command-line applications in Python.
gybe - A simple YAML transpiler for rendering Kubernetes manifests using python type-hints.
python-decouple - Strict separation of config from code.
jinsi - JSON/YAML homoiconic templating language
pytorch_tempest - My repo for training neural nets using pytorch-lightning and hydra