Repro: An Open-Source Library for Improving the Reproducibility and Usability of Publicly Available Research Code

• URL: http://arxiv.org/abs/2204.13848v1
• Date: Fri, 29 Apr 2022 01:54:54 GMT
• Title: Repro: An Open-Source Library for Improving the Reproducibility and Usability of Publicly Available Research Code
• Authors: Daniel Deutsch and Dan Roth
• Abstract summary: Repro is an open-source library which aims at improving the usability of research code. It provides a lightweight Python API for running software released by researchers within Docker containers.
• Score: 74.28810048824519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce Repro, an open-source library which aims at improving the reproducibility and usability of research code. The library provides a lightweight Python API for running software released by researchers within Docker containers which contain the exact required runtime configuration and dependencies for the code. Because the environment setup for each package is handled by Docker, users do not have to do any configuration themselves. Once Repro is installed, users can run the code for the 30+ papers currently supported by the library. We hope researchers see the value provided to others by including their research code in Repro and consider adding support for their own research code.

Related papers

• Lighthouse: A User-Friendly Library for Reproducible Video Moment Retrieval and Highlight Detection [14.227865973426843]
  We propose Lighthouse, a user-friendly library for reproducible video moment retrieval and highlight detection (MR-HD) The first is a lack of comprehensive and reproducible experiments across various methods, datasets, and video-text features. This is because no unified training and evaluation covers multiple settings. Most works release only the training codes, requiring users to implement the whole inference process of MR-HD. Lighthouse addresses these issues by implementing a unified reproducible that includes six models, three features, and five datasets.
  arXiv  Detail & Related papers  (2024-08-06T02:15:12Z)
• Contributing Back to the Ecosystem: A User Survey of NPM Developers [10.154686574810501]
  Survey of 49 developers from the NPM ecosystem. We find that developers are more likely to maintain their own packages rather than contribute to the ecosystem. Our results open up new avenues into tool support and research into how to sustain these ecosystems.
  arXiv  Detail & Related papers  (2024-07-01T00:15:55Z)
• PufferLib: Making Reinforcement Learning Libraries and Environments Play Nice [0.8702432681310401]
  PufferLib provides one-line environment wrappers that eliminate common compatibility problems. With PufferLib, you can use familiar libraries like CleanRL and SB3 to scale from classic benchmarks like Atari and Procgen to complex simulators like NetHack and Neural MMO. All of our code is free and open-source software under the MIT license.
  arXiv  Detail & Related papers  (2024-06-11T21:13:34Z)
• Analyzing the Accessibility of GitHub Repositories for PyPI and NPM Libraries [91.97201077607862]
  Industrial applications heavily rely on open-source software (OSS) libraries, which provide various benefits. To monitor the activities of such communities, a comprehensive list of repositories for the libraries of an ecosystem must be accessible. In this study, we analyze the accessibility of GitHub repositories for PyPI and NPM libraries.
  arXiv  Detail & Related papers  (2024-04-26T13:27:04Z)
• torchgfn: A PyTorch GFlowNet library [56.071033896777784]
  torchgfn is a PyTorch library that aims to address this need. It provides users with a simple API for environments and useful abstractions for samplers and losses.
  arXiv  Detail & Related papers  (2023-05-24T00:20:59Z)
• SequeL: A Continual Learning Library in PyTorch and JAX [50.33956216274694]
  SequeL is a library for Continual Learning that supports both PyTorch and JAX frameworks. It provides a unified interface for a wide range of Continual Learning algorithms, including regularization-based approaches, replay-based approaches, and hybrid approaches. We release SequeL as an open-source library, enabling researchers and developers to easily experiment and extend the library for their own purposes.
  arXiv  Detail & Related papers  (2023-04-21T10:00:22Z)
• Code Librarian: A Software Package Recommendation System [65.05559087332347]
  We present a recommendation engine called Librarian for open source libraries. A candidate library package is recommended for a given context if: 1) it has been frequently used with the imported libraries in the program; 2) it has similar functionality to the imported libraries in the program; 3) it has similar functionality to the developer's implementation, and 4) it can be used efficiently in the context of the provided code.
  arXiv  Detail & Related papers  (2022-10-11T12:30:05Z)
• skrl: Modular and Flexible Library for Reinforcement Learning [0.0]
  skrl is an open-source modular library for reinforcement learning written in Python. It allows loading, configuring, and operating NVIDIA Isaac Gym environments.
  arXiv  Detail & Related papers  (2022-02-08T12:43:31Z)
• MushroomRL: Simplifying Reinforcement Learning Research [60.70556446270147]
  MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libraries, MushroomRL has been created with the purpose of providing a comprehensive and flexible framework to minimize the effort in implementing and testing novel RL methodologies.
  arXiv  Detail & Related papers  (2020-01-04T17:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.