Reverse Dependencies of Lightning

The following projects have a declared dependency on Lightning:

• molflux — A foundational package for molecular predictive modelling
• molpipeline — Integration of rdkit functionality into sklearn pipelines.
• molsetrep — A library for molecular representation learning.
• morpheus-spatial — no summary
• mqtts-lightning — Add a short description here
• multicom-ligand — Comprehensive ensembling of protein-ligand structure and affinity prediction methods
• multilearn — Multi-task learning with Pytorch.
• naifu — naifu is designed for training generative models with various configurations and features.
• namable-classify — no summary
• napari-brainways — Brainways
• napatrackmater — Import Trackmate XML files for Track Visualization and analysis in Napari.
• nemo-aligner — NeMo-Aligner - a toolkit for model alignment
• nemo-toolkit — NeMo - a toolkit for Conversational AI
• NeuralTSNE — Implementation of neural t-SNE in PyTorch with CUDA support
• neuromancer — Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularization
• neurosis — A neural network trainer (for weebs)
• NeuroVisKit — NeuroVisKit
• nf2 — Neural Network Force Free magnetic field extrapolation
• nlhappy — 自然语言处理(NLP)
• nlu-trainer — 普强信息语言理解模型训练框架
• nn-template-core — Utility library for nn-template.
• nn-zoo — A collection of PyTorch utilities
• nougat-ocr — Nougat: Neural Optical Understanding for Academic Documents
• novae — Graph-based foundation model for spatial transcriptomics data
• nshtrainer — no summary
• oh-my-bloom — 中文BLOOM语言模型
• ohara — A collection of implementations of LLM, papers, and other models.
• ontime — Your library to work with time series
• open-magvit2 — Packaging of Open-source replication of Google's MAGVIT-v2 tokenizer
• openbb-chat — Deep learning package to add chat capabilities to OpenBB
• openelm-pytorch — openelm-pytorch
• opengeokube-mlkit — MLKit - A quick way to start with machine and deep learning
• openretina — Open source retina model architectures and training setups
• opensoundscape — Open source, scalable acoustic classification for ecology and conservation
• optima-ml — Distributed hyperparameter optimization and input variable selection for artificial neural networks.
• optuna-integration — Integration libraries of Optuna.
• osc-llm — 轻量级大模型推理工具,专注于模型推理延迟,注重框架易用性和可拓展性。
• otx — OpenVINO™ Training Extensions: Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
• p2pfl — A p2p federated learning framework
• PACBB — PAC Bayes Bound toolset
• packmetric — PackMetric
• pansharpening — Deep learning for pansharpening in remote sensing
• personalitylinmult — PersonalityLinMulT: Transformer-based Big Five Automatic Personality Perception.
• perturbers — Perturber models for neural data augmentation.
• physioex — A python package for explainable sleep staging via deep learning
• pina-mathlab — Physic Informed Neural networks for Advance modeling.
• pl-crossvalidate — Cross validation made easy in Lightning
• pl-extension — extension for pytorch-lightning
• plants-sm — PlantsSM
• posebench — Comprehensive benchmarking of protein-ligand structure prediction methods
• propulate — Massively parallel genetic optimization through asynchronous propagation of populations
• proteinworkshop — no summary
• prototorch-models — Pre-packaged prototype-based machine learning models using ProtoTorch and PyTorch-Lightning.
• provision-ai — AI experiment provisioner
• ptlflow — PyTorch Lightning Optical Flow
• ptn-set-transformer — Training and inference on protein sets (genomes)
• PVNet — PVNet
• PVNet-summation — PVNet_summation
• pyannote.audio — Neural building blocks for speaker diarization
• pykoopman — Python package for data-driven approximations to the Koopman operator.
• pymetalearning — A python package for metalearning.
• pyniche — An AI Library for Niche Squad
• pyoneai — OneAIOps SDK
• pyoneai-ops — OneAIOps Services
• pyrelational — Python tool box for quickly implementing active learning strategies
• pyrfd — no summary
• pyro-ppl — A Python library for probabilistic modeling and inference
• pyrovelocity — A multivariate RNA Velocity model to estimate future cell states with uncertainty using probabilistic modeling with pyro.
• pytorch-bsf — PyTorch implementation of Bezier simplex fitting
• pytorch-cortex — A modular architecture for deep learning systems.
• pytorch-eo — Deep Learning for Earth Observation
• pytorch-forecasting — Forecasting timeseries with PyTorch - dataloaders, normalizers, metrics and models
• pytorchcocotools — Unofficial APIs for the MS-COCO dataset using PyTorch
• PyTorchLab — Realize code in AI field with PyTorch and Lightning.
• quanda — Toolkit for quantitative evaluation of data attribution methods in PyTorch.
• reax — REAX: A simple training framework for JAX-based projects
• relik — Fast and Accurate Entity Linking and Relation Extraction on an Academic Budget
• replay-rec — RecSys Library
• retrvtme — retrvTME: Bulk RNA-seq Deconvolution with Tumor MicroEnvironment Retrieval retrvTME is a deep learning-powered tool designed to decode bulk RNA-seq data using large-scale single-cell atlases. The tool precisely reconstructs cell-type proportions and cell-type-specific gene expression profiles, with specialized capabilities for resolving intricate tumor microenvironment (TME) biology from bulk transcriptomes.
• rewards — Start learning about RL and make model and envs in minutes in just few lines of code
• rhizonet — Segmentation pipeline for EcoFAB images
• rl4co — RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
• rldurham — Python package for the Reinforcement Learning course at Durham University
• rlsvision — no summary
• rslearn — A library for developing remote sensing datasets and models
• salt-ml — Multimodal and Multiclass Machine Learning for High Energy Physics
• scclip — no summary
• scdiffeq — scDiffEq: modeling single-cell dynamics using neural differential equations.
• scMIDAS — A torch-based integration method for single-cell multi-omic data.
• scprint — scPRINT is a Large Cell Model for Gene Network Inference, Denoising and more from scRNAseq data
• scsims — Scalable, Interpretable Deep Learning for Single-Cell RNA-seq Classification
• scUNAGI — A Python package for UNAGI
• scvi-tools — Deep probabilistic analysis of single-cell omics data.
• seq2squiggle — End-to-end simulation of nanopore sequencing signals with feed-forward transformers
• shapeaxi — Shape Analysis Exploration and Interpretability
• sheeprl — High-quality, single file and distributed implementation of Deep Reinforcement Learning algorithms with production-friendly features
• sihl — Simple Image Heads and Layers
• sispca — Supervised independent subspace principal component analysis
• slg-nimrod — minimal deep learning framework
• snp-transformer — SNP Transformer