gybe
omegaconf
Our great sponsors
| gybe | omegaconf | |
|---|---|---|
| 1 | 3 | |
| 7 | 1,788 | |
| - | - | |
| 7.1 | 6.8 | |
| 6 days ago | about 1 month ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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gybe
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.
What are some alternatives?
falconpy - The CrowdStrike Falcon SDK for Python
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]
uvicorn-gunicorn-fastapi-docker - Docker image with Uvicorn managed by Gunicorn for high-performance FastAPI web applications in Python with performance auto-tuning.
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.
awesome-pattern-matching - Pattern Matching for Python 3.7+ in a simple, yet powerful, extensible manner.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
dotify - 🐍🎶 Yet another Spotify Web API Python library
jinsi - JSON/YAML homoiconic templating language
dallecli - 💠 Provide users with the ability to generate, edit and filter images using the DALL-E 3 API provided by OpenAI, all from the command line.
xdgconfig - Easy access to ~/.config from python
ytmusic-deleter - Easily delete your YouTube Music library.
pytorch_tempest - My repo for training neural nets using pytorch-lightning and hydra