pyaml_env
omegaconf
| pyaml_env | omegaconf | |
|---|---|---|
| 1 | 3 | |
| 80 | 1,812 | |
| - | - | |
| 1.8 | 6.8 | |
| 9 months ago | about 2 months ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
pyaml_env
-
Python YAML configuration with environment variables parsing
You can find the repo here: Python YAML configuration with environment variables parsing *A very small library that parses a yaml configuration file and it resolves the environment variables, so that no…*mariakaranasou.com mkaranasou/pyaml_env *A very small library that parses a yaml configuration file and it resolves the environment variables, so that no…*github.com
omegaconf
-
OmegaConf module not found: Deforum_Stable_Diffusion.ipynb
!pip install -e git+https://github.com/omry/omegaconf.git#egg=omegaconf
-
What's the point of config files in another format?
OmegaConf is really easy to work with.
-
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?
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.
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]
strictyaml - Type-safe YAML parser and validator.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
gybe - A simple YAML transpiler for rendering Kubernetes manifests using python type-hints.
jinsi - JSON/YAML homoiconic templating language
xdgconfig - Easy access to ~/.config from python
pytorch_tempest - My repo for training neural nets using pytorch-lightning and hydra
python-decouple - Strict separation of config from code.
pytorch-lightning - Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
stable-diffusion - A latent text-to-image diffusion model