jinsi
omegaconf
Our great sponsors
jinsi | omegaconf | |
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1 | 3 | |
17 | 1,794 | |
- | - | |
3.7 | 6.8 | |
12 months ago | about 1 month ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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jinsi
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?
yamlpath - YAML/JSON/EYAML/Compatible get/set/merge/validate/scan/convert/diff processors using powerful, intuitive, command-line friendly syntax.
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]
kapitan - Generic templated configuration management for Kubernetes, Terraform and other things
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.
kapitan-reference - Reference structure for Kapitan - alpha version
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
mashumaro - Fast and well tested serialization library
gybe - A simple YAML transpiler for rendering Kubernetes manifests using python type-hints.
hikaru - Move smoothly between Kubernetes YAML and Python for creating/updating/componentizing configurations.
xdgconfig - Easy access to ~/.config from python
srsly - 🦉 Modern high-performance serialization utilities for Python (JSON, MessagePack, Pickle)
pytorch_tempest - My repo for training neural nets using pytorch-lightning and hydra