Apache License 2.0

项目介绍

NVIDIA/framework-reproducibility 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,采用 Apache License 2.0 许可证,目前获得 431 个 Star、41 次 Fork。

仓库全名
NVIDIA/framework-reproducibility
开源许可证
Apache License 2.0
Star 数
431
Fork 数
41
未关闭 Issue
11

README 摘要

Framework Reproducibility (fwr13y) Repository Name Change The name of this GitHub repository was changed to framework-reproducibility on 2023-02-14. Prior to this, it was named framework-determinism. Before that, it was named tensorflow-determinism. "In addition to redirecting all web traffic, all git clone, git fetch, or git push operations targetting the previous location[s] will continue to function as if made to…

完整内容见 GitHub 原始 README 或本页 。

常见问题

framework-reproducibility 用什么语言开发?

主要使用 Python 编写,采用 Apache License 2.0 许可证,目前有 431 个 Star。

framework-reproducibility 可以免费商用吗?

仓库采用 Apache License 2.0 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 framework-reproducibility 的源码?

可以直接克隆仓库:git clone https://github.com/NVIDIA/framework-reproducibility.git,默认分支为 master;安装与运行步骤见仓库 README。

framework-reproducibility 最近还在维护吗?

仓库最近一次更新为 2024年5月14日,当前有 11 个未关闭 Issue;Star 数为 431,可作为社区活跃度的参考。

关于

Providing reproducibility in deep learning frameworks

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

语言占比

Jupyter Notebook
Python
Shell

Release

README

Framework Reproducibility (fwr13y)

Repository Name Change

The name of this GitHub repository was changed to framework-reproducibility on 2023-02-14. Prior to this, it was named framework-determinism. Before that, it was named tensorflow-determinism.

“In addition to redirecting all web traffic, all git clone, git fetch, or git push operations targetting the previous location[s] will continue to function as if made to the new location. However, to reduce confusion, we strongly recommend updating any existing local clones to point to the new repository URL.” – GitHub documentation

Repository Intention

This repository is intended to:

  • provide documentation, status, patches, and tools related to determinism (bit-accurate, run-to-run reproducibility) in deep learning frameworks, with a focus on determinism when running on GPUs, and
  • provide a tool, and related guidelines, for reducing variance (Seeder) in deep learning frameworks.

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