Popular GitHub Repositories for Ndn Hydra

PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ???

Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning and Hydra.

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

Open-Source Toolkit for End-to-End Korean Automatic Speech Recognition leveraging PyTorch and Hydra.

Flexible components pairing ? Transformers with :zap: Pytorch Lightning

A library for ready-made reinforcement learning agents and reusable components for neat prototyping

Flexible and scalable template based on PyTorch Lightning + Hydra. Efficient workflow and reproducibility for rapid ML experiments.

My repo for training neural nets using pytorch-lightning and hydra

KUIELAB-MDX-Net got the 2nd place on the Leaderboard A and the 3rd place on the Leaderboard B in the MDX-Challenge ISMIR 2021

Deep Learning project template for PyTorch (multi-gpu training is supported)

? Modular VAE disentanglement framework for python built with PyTorch Lightning ▸ Including metrics and datasets ▸ With strongly supervised, weakly supervised and unsupervised methods ▸ Easily configured and run with Hydra config ▸ Inspired by disentanglement_lib

Анализ трафика на круговом движении с использованием компьютерного зрения

? Human head semantic segmentation

Generative models (GAN, VAE, Diffusion Models, Autoregressive Models) implemented with Pytorch, Pytorch_lightning and hydra.

PyTorch-Lightning Library for Neural News Recommendation

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

:dart: Deep Learning Framework for Image Classification & Regression in Pytorch for Fast Experiments

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

Modular and extensible speech recognition library leveraging pytorch-lightning and hydra.

A cloud-agnostic ML Platform that will enable Data Scientists to run multiple experiments, perform hyper parameter optimization, evaluate results and serve models (batch/realtime) while still maintaining a uniform development UX across cloud environments