Popular GitHub Repositories for Reproducibility

? Data Versioning and ML Experiments

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

A toolkit for reproducible reinforcement learning research.

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

:cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI

The collaboration workspace for Machine Learning

High-fidelity performance metrics for generative models in PyTorch

[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

Providing reproducibility in deep learning frameworks

Automatic Ontology and Knowledge Graph construction with LLM

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

ForML - A development framework and MLOps platform for the lifecycle management of data science projects

A template for starting reproducible Python machine-learning projects with hardware acceleration. Find an example at https://github.com/CLAIRE-Labo/no-representation-no-trust

My entry for ICLR 2018 Reproducibility Challenge for paper Synthesizing robust adversarial examples https://openreview.net/pdf?id=BJDH5M-AW

Automated machine learning protocols that start from CSV databases of descriptors or SMILES and produce publication-quality results in Chemistry studies with only one command line.

End-to-end-Sequence-Labeling-via-Bi-directional-LSTM-CNNs-CRF-Tutorial

Python’s delightfully modular experiment tracker for MLOps

SnapperML is a framework for machine learning. It has many functionalities including scalability through docker instances and reproducibility.

A collection of portable workflows, automation recipes and components for MLOps in a unified CK format. Note that this repository is outdated - please check the 2nd generation of the CK workflow automation meta-framework with portable MLOps and DevOps components here: