Kconfiglib
gin-config
| Kconfiglib | gin-config | |
|---|---|---|
| 1 | 3 | |
| 431 | 1,993 | |
| - | 0.6% | |
| 0.0 | 4.6 | |
| 7 months ago | 3 months ago | |
| Python | Python | |
| ISC License | Apache License 2.0 |
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Kconfiglib
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Using Kconfig in proprietary closed-source projects
Ulfalizer's python implementation, Kconfiglib has a permissive license and works brilliantly. It's what Zephyr uses. https://github.com/ulfalizer/Kconfiglib
gin-config
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hydra VS gin-config - a user suggested alternative
2 projects | 5 Jul 2022
Lightweight configuration framework for Python (created and maintained by Google) - Good combination of "easy to use" and "expressive API" - Directed support for TensorFlow and PyTorch - Maintained by Google
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Equivalent framework as Gin for PyTorch
Hi, does any of you know is there is an equivalent of this framework (https://github.com/google/gin-config) for PyTorch?
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[D] Tools to avoid writing tons of scripts
GinConfig
What are some alternatives?
kconfig - kconfig fragments for SOF development
dynaconf - Configuration Management for Python ⚙
SaltStack - Software to automate the management and configuration of any infrastructure or application at scale. Get access to the Salt software package repository here:
spock - spock is a framework that helps manage complex parameter configurations during research and development of Python applications
yadm - Yet Another Dotfiles Manager
fora - A simple infrastructure and configuration management tool
pyinfra - pyinfra automates infrastructure using Python. It’s fast and scales from one server to thousands. Great for ad-hoc command execution, service deployment, configuration management and more.
ConfZ - ConfZ is a configuration management library for Python based on pydantic.
python-dotenv - Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles.
hydra - Hydra is a framework for elegantly configuring complex applications
PyDGN - A research library for automating experiments on Deep Graph Networks