Hydra Python Packages

hya

A library of custom OmegaConf resolvers

airflow-config

A Configuration System for Airflow

ccflow

ccflow is a collection of tools for workflow configuration, orchestration, and dependency injection

hydra-callbacks

A collection of usefull hydra callbacks

hyfi

HyFI: Hydra Fast Interface (Hydra and Pydantic based interface framework)

hydraflow

Integrate Hydra and MLflow to manage and track machine learning experiments

rl4co

A PyTorch library for all things Reinforcement Learning (RL) for Combinatorial Optimization (CO)

mushin-py

Boilerplate-free, reproducible ML experiment workflows: run a sweep, get a labeled xarray dataset back.

pytorch-segmentation-models-trainer

Framework to train semantic segmentation models on Pytorch using yaml config files

quadra

Quadra: Effortless and reproducible deep learning workflows with configuration files.

phijax

A JAX framework for reproducible physics-informed neural network training

heracless

Type-safe YAML config manager for Python with auto-generated stub files and IDE autocompletion support

moai-mdk

moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.

textconf

Jinja2-based text generation from configuration objects

librair

retrieve data from library catalogs

hydra-structlog

An opinionated hydra plugin to configure logging with structlog

omegaconf-1password

A 1Password secret reference resolver for OmegaConf and Hydra

lightning-transformers

Flexible components pairing 🤗 Transformers with :zap: Pytorch Lightning

wordlist-kit

Wordlist Kit — all-in-one wordlist toolkit (crunch + CeWL + CUPP + rule templates) with recon, OSINT and attack-command assembly. Authorised testing only.

dlhpcstarter

pip install the deep learning & HPC starter pack to begin your project.

ndn-hydra

ndn-hydra: A Python-coded NDN distributed repository with five focused attributes: resiliency, scalability, usability, efficiency, and security.

anomalib-orobix

Quadra: Effortless and reproducible deep learning workflows with configuration files.

pyteia

A config-as-graph framework for deep learning: any pipeline, in any modality or domain, assembled from swappable components.

fseval

Benchmarking framework for Feature Selection and Feature Ranking algorithms 🚀