omegaconf
pyaml_env
Our great sponsors
omegaconf | pyaml_env | |
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3 | 1 | |
1,761 | 79 | |
- | - | |
6.8 | 1.8 | |
8 days ago | 7 months 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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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.
pyaml_env
We haven't tracked posts mentioning pyaml_env yet.
Tracking mentions began in Dec 2020.
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]
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.
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
strictyaml - Type-safe YAML parser and validator.