Reproducibility
Showing 26 of 26 projects
🦉 Data Versioning and ML Experiments
A framework for elegantly configuring complex applications, particularly in machine learning and research.
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
A PyTorch framework for deep learning research and development, focusing on reproducibility and rapid experimentation.
An open-source machine learning platform for distributed training, hyperparameter tuning, experiment tracking, and resource management.
A toolkit and library for developing, evaluating, and reproducing reinforcement learning algorithms.
A modular framework for automated, reproducible Nix packaging across multiple programming language ecosystems.
A bioinformatics-native AI agent skill library for reproducible, local-first genomic analysis, built on OpenClaw.
An R package that creates reproducible examples from R code for sharing on GitHub, Stack Overflow, Slack, and other platforms.
A Nix-based framework for creating declarative and reproducible Jupyter environments with configurable kernels and extensions.
An open-source machine learning solution for the Home Credit Default Risk Kaggle competition, providing reproducible code and experiments.
A container-native workflow engine for defining and executing testing and automation tasks in Docker and other container runtimes.
A Python library for logging ML metrics, parameters, and models in simple file formats, compatible with DVC and Git.
A collection of examples demonstrating how to use Comet.ml for machine learning experiment tracking across various Python frameworks.
A lightweight Python library for building reproducible machine learning pipelines with minimal interface constraints.
ENIGMA HALFpipe is a user-friendly software that facilitates reproducible analysis of fMRI data
An R package that extends knitr to provide flexible control over working directories and output paths when generating dynamic reports.
A build system for data science pipelines that caches dependencies and versions outputs for performance and reproducibility.
A PyTorch framework for reinforcement learning research, focused on reproducibility and fast experimentation.
An open-source solution for the Google AI Open Images Object Detection Challenge, providing a RetinaNet-based benchmark with experiment tracking.
An open-source machine learning solution for the Santander Value Prediction Challenge on Kaggle.
A high-level linter and static analyzer for Python data science code that detects potential issues like data leakage and non-reproducibility.
A curated collection of transformers to accelerate and enhance machine learning experimentation with the Steppy library.
A multi-extract, multi-level dataset of Mozilla Bugzilla issue tracking history spanning 15 years for software engineering research.
A self-hostable, Django-based platform that unifies the scientific research lifecycle with AI-native tools and GitHub-style collaboration.
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