#Reproducibility
Showing 57 of 57 repositories tagged #reproducibility, ranked by stars
🦉 Data Versioning and ML Experiments
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
PyTorch Lightning + Hydra. A very user-friendly template for ML experimentation. ⚡🔥⚡
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
Accelerated deep learning R&D
:cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI
The collaboration workspace for Machine Learning
A comparison between some VPS providers. It uses Ansible to perform a series of automated benchmark tests over the VPS servers that you specify. It allows the reproducibility of those tests by anyone that wanted to compare these results to their own. All the tests results are available in order to provide independence and transparency.
An R-focused pipeline toolkit for reproducibility and high-performance computing
Function-oriented Make-like declarative workflows for R
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
Create highly reproducible python environments
Prepare Raspberry Pi 3, 4 & 5 configurations using a virtual machine.
Declarative and reproducible Jupyter environments - powered by Nix
[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
Generic template to bootstrap your PyTorch project.
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
Create powerful Hydra applications without the yaml files and boilerplate code.
Open solution to the Home Credit Default Risk challenge :house_with_garden:
Open source production model management tool for data scientists
PLynx is a domain agnostic platform for managing reproducible experiments and data-oriented workflows.
Container-native task automation engine.
Deep Reinforcement Learning (DRL) agents applied to medical images
Relational Workflows: where database schemas define executable data pipelines.
Automation and Make
Archetypes for targets and pipelines
The Accelerator is a tool for fast and reproducible processing of large amounts of data.
Capture all information throughout your model's development in a reproducible way and tie results directly to the model code!
Lightweight, Python library for fast and reproducible experimentation :microscope:
R Package 📦 for initializing projects for various R activities :nut_and_bolt:
Data Analysis Workflows & Reproducibility Learning Resources
VisTrails is an open-source data analysis and visualization tool. It provides a comprehensive provenance infrastructure that maintains detailed history information about the steps followed and data derived in the course of an exploratory task: VisTrails maintains provenance of data products, of the computational processes that derive these products and their executions.
Execute and document benchmarks reproducibly.
Fast, reproducible, and portable software development environments
An example project for building containers with Nix and deploying them to Kubernetes
Continuous integration for R packages. 🔀 Automates testing ✅, documentation website building 📦, & containerised deployment 🐳.
Data version control for reproducible analysis pipelines in R with {targets}.
Umbrella package for SciTeX — reproducible science from raw data to manuscript
Synchronize your working directory efficiently to a remote place without committing the changes.
Get started DVC project
When the stakes are high, intelligence is only half the equation - reliability is the other ⚠️
A minimal example data analysis project with the targets R package
moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.
Create, visualize, run & benchmark DVC pipelines in Python & Jupyter notebooks.
Example workflows for the drake R package
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.
The user manual for the drake R package
Open solution to the Google AI Object Detection Challenge :maple_leaf:
Reproducible Science: what, why, how
User manual of the targets R package
Import packages in Python, even if they aren't installed!
PyTorch research stack for ML multi-factor trading: 213 factors, bias correction, portfolio optimization, and vectorized backtesting.
A framework for rapid development of robust data pipelines following a simple design pattern
Extract a lightweight subset of your relational production database for development and testing purpose.
Restrict the scope of functions for reproducible code execution and peace of mind.
Reproduce Jupyter Notebooks inside Docker Containers.