Reverse Dependencies of pytorch-ignite

The following projects have a declared dependency on pytorch-ignite:

• active-transformers — Active Learning for Transformer with focus on Sequence Tagging tasks
• atombench — atombench
• atomvision — atomvision
• autoembedder — PyTorch autoencoder with additional embeddings layer for categorical data.
• azureml-automl-dnn-vision — AutoML DNN Vision Models
• azureml-contrib-automl-dnn-vision — AutoML DNN Vision Models
• blackhc.project — Notebook setup code
• catbird — Paraphrase generation Toolbox and Benchmark
• chady — A package for ML libraries
• chatting-chatbots — A library for running experiments of multiple chatbots having conversations with each other.
• decofre — Neural coreference resolution
• deepchecks — Package for validating your machine learning model and data
• democlassi — Collection of my python functions for training pytorch models to classify emotion, age, race, gender
• draupnir — Ancestral sequence reconstruction using a tree structured Ornstein Uhlenbeck variational autoencoder
• eir-dl — no summary
• EMCqMRI — A distribution of a general tool for training and inference of QMRI models
• fastai — fastai simplifies training fast and accurate neural nets using modern best practices
• fastAIcourse — fastAIcourse
• fedbiomed-cli — no summary
• Fireworks-ml — A batch-processing framework for data analysis and machine learning using PyTorch.
• geyser-lava — Compose and execute python objects
• gleipnir-tcr — Gleipnir
• gravitorch — A library to train ML models with PyTorch
• ignite-simple — Easily train pytorch models with automatic LR and BS tuning
• igniter — no summary
• libcrap — Crappy functions Crabman uses. Some helpers for pytorch and ignite.
• melmac — A package for neural multilabel and multiclass classification.
• ml-workflow — Pytorch project template and required tools
• monai — AI Toolkit for Healthcare Imaging
• monai-weekly — AI Toolkit for Healthcare Imaging
• mpdd-alignn — A version of the NIST-JARVIS ALIGNN optimized in terms of model performance and to some extent reliability, for large-scale deployments over the MPDD infrastructure by Phases Research Lab.
• neural-semigroups — Neural networks powered research of semigroups
• newAI — newAi
• nfflr — neural force field learning toolkit
• niiv — Self-Supervised Neural Implicit Isotropic Volume Reconstruction
• nurphy — A deep learning based natural language understanding module
• nussl — A flexible sound source separation library.
• optuna-integration — Integration libraries of Optuna.
• pipelinex — PipelineX: Python package to build ML pipelines for experimentation with Kedro, MLflow, and more
• pyklopp — no summary
• pysiml — SiML - a Simulation ML library
• pytorch-adapt — Domain adaptation made easy. Fully featured, modular, and customizable.
• pytorch-flame — A library based on Ignite to help you train and evaluate PyTorch neural networks more easily.
• pytorch-hrvvi-ext — HrvvI's extension to PyTorch
• pytorch-igniter — Simplify running pytorch training with fully-configured pytorch-ignite
• pytorch-light — Light.
• pytorch-tao — A toolbox for a specific Machine Learning training project
• quartic-sdk — QuarticSDK is the SDK package which exposes the APIs to the user
• runml-checks — Package for validating your machine learning model and data
• sccross — Single cell multi-omics cross modal generation, multi-omics simulation and perturbation
• scglue — Graph-linked unified embedding for unpaired single-cell multi-omics data integration
• stVAE — Style transfer variational autoencoder
• texi — Text processing toolbox.
• thermostat-datasets — Collection of NLP model explanations and accompanying analysis tools
• TimeSeriesML — TimeSeriesML
• torch-optim — PyTorch models optimization using neural network pruning
• traintool — Machine learning in one line of code
• upside-down-rl — no summary
• uwnet — PyTorch training code for climate modeling
• vegvisir — Vegvisir
• waterch-tasker — A scalable and extendable experiment task scheduler framework.
• yaib — Yet Another ICU Benchmark is a holistic framework for the automation of the development of clinical prediction models on ICU data. Users can create custom datasets, cohorts, prediction tasks, endpoints, and models.
• yonlu — A deep learning based natural language understanding module