What is the best and right way to open-source packages from a company monorepo?

栏目: IT技术 · 发布时间: 3年前

内容简介:There are a few tools to split commits from sub-dirs to a branch which you can then push to a public repo/monorepo.E.g. `git subtree`, https://github.com/facebook/fbshipit, https://github.com/splitsh/lite, https://github.com/ingydotnet/git-subrepo.A lot of

There are a few tools to split commits from sub-dirs to a branch which you can then push to a public repo/monorepo.

E.g. `git subtree`, https://github.com/facebook/fbshipit, https://github.com/splitsh/lite, https://github.com/ingydotnet/git-subrepo.

A lot of these approaches though rely on the source-of-truth being the internal company monorepo. PRs are synced internally, merged, and then pushed out. It means that someone outside the organization cannot be a maintainer, and the speed of PR merges is dictated by the available resources inside the company. So I'd argue this is not the right OSS way of doing things.

Even if there are two public monorepos out in the open you can have similar problems trying to collaborate, because to modify one line of a package, you may need to pull a huge monorepo and its tooling down.

Does anyone have a solution or an example of an OSS-friendly approach to monorepo open-sourcing?


以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网

查看所有标签

猜你喜欢:

本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们

Python机器学习基础教程

Python机器学习基础教程

[德]安德里亚斯·穆勒、[美]莎拉·吉多 / 张亮 / 人民邮电出版社 / 2018-1 / 79.00元

本书是机器学习入门书,以Python语言介绍。主要内容包括:机器学习的基本概念及其应用;实践中最常用的机器学习算法以及这些算法的优缺点;在机器学习中待处理数据的呈现方式的重要性,以及应重点关注数据的哪些方面;模型评估和调参的高级方法,重点讲解交叉验证和网格搜索;管道的概念;如何将前面各章的方法应用到文本数据上,还介绍了一些文本特有的处理方法。一起来看看 《Python机器学习基础教程》 这本书的介绍吧!

HTML 压缩/解压工具
HTML 压缩/解压工具

在线压缩/解压 HTML 代码

Base64 编码/解码
Base64 编码/解码

Base64 编码/解码

URL 编码/解码
URL 编码/解码

URL 编码/解码