贡献

Contributing

新问题

New Issues

如果你遇到的问题是关于以下内容的 bug 报告或功能请求：

If your issue is a bug report or feature request for:

开发环境，Bash

Development Environment, Bash

要搭建本地开发环境以参与 conda 的开发，我们推荐如下步骤：

  1. Fork conda/conda 仓库，并克隆到本地任意目录（一个隔离的 miniconda 环境会在该目录下自动创建）， 然后设置 git remote 指向上游仓库和你的 fork。详见下文的详细操作说明。

    1a. 选择你希望放置项目的目录位置（不是已有 conda 的位置）

    CONDA_PROJECT_ROOT="$HOME/conda"

    1b. 克隆项目，设置 upstream 为主仓库。在 GitHub 页面点击 Fork 按钮，创建你自己的副本。

    GITHUB_USERNAME=kalefranz
git clone git@github.com:$GITHUB_USERNAME/conda "$CONDA_PROJECT_ROOT"
cd "$CONDA_PROJECT_ROOT"
git remote add upstream git@github.com:conda/conda

  2. 创建本地开发环境，并激活该环境：

    . dev/start

    此命令将在 ./devenv 下创建一个特定于该项目的基础环境。 如果该环境已存在，命令将直接快速激活已存在的 ./devenv 环境。

    要确保当前解释器使用的是项目目录下的 conda 代码， 可以通过 conda info --all 命令输出中的 conda location: 字段来确认。

  3. 使用 GNU make 运行 conda 的单元测试：

    make unit

    或者，也可以使用 pytest：

    py.test -m "not integration and not installed" conda tests

    如果你只想运行某个特定测试，也可以这样使用 pytest：

    py.test tests/test_create.py -k create_install_update_remove_smoketest

To set up an environment to start developing on conda code, we recommend the following steps:

  1. Fork the conda/conda repository, clone it locally anywhere you choose (an isolation miniconda will be set up within the clone directory), and set up git remote to point to upstream and fork. For detailed directions, see below.

    1a. Choose where you want the repository located (not location of existing conda)

    CONDA_PROJECT_ROOT="$HOME/conda"

    1b. Clone the project, with upstream being the main repository. Make sure to click the Fork button above so you have your own copy of this repo.

    GITHUB_USERNAME=kalefranz
git clone git@github.com:$GITHUB_USERNAME/conda "$CONDA_PROJECT_ROOT"
cd "$CONDA_PROJECT_ROOT"
git remote add upstream git@github.com:conda/conda

  2. Create a local development environment, and activate that environment

    . dev/start

    This command will create a project-specific base environment at ./devenv. If the environment already exists, this command will just quickly activate the already-created ./devenv environment.

    To be sure that the conda code being interpreted is the code in the project directory, look at the value of conda location: in the output of conda info --all.

  3. Run conda's unit tests using GNU make

    make unit

    or alternately with pytest

    py.test -m "not integration and not installed" conda tests

    or you can use pytest to focus on one specific test

    py.test tests/test_create.py -k create_install_update_remove_smoketest

开发环境，Windows cmd.exe shell

Development Environment, Windows cmd.exe shell

上述步骤假设你已安装好 git 并已加入 PATH。

  1. 选择你希望放置项目的目录位置：

    set "CONDA_PROJECT_ROOT=%HOMEPATH%\conda"

  2. 克隆项目，设置 origin 为主仓库。请确保点击页面上的 Fork 按钮，创建你自己的副本：

    set GITHUB_USERNAME=kalefranz
git clone git@github.com:conda/conda "%CONDA_PROJECT_ROOT%"
cd "%CONDA_PROJECT_ROOT%"
git remote add %GITHUB_USERNAME% git@github.com:%GITHUB_USERNAME%/conda

    要确保当前解释器使用的是项目目录下的 conda 代码， 可通过 conda info --all 输出中的 conda location: 字段确认。

  3. 创建本地开发环境并激活：

    .\dev\start

    此命令将在 .\devenv 下创建一个特定于该项目的基础环境。 如果该环境已存在，命令将直接激活已存在的 .\devenv 环境。

In these steps, we assume git is installed and available on PATH.

  1. Choose where you want the project located

    set "CONDA_PROJECT_ROOT=%HOMEPATH%\conda"

  2. Clone the project, with origin being the main repository. Make sure to click the Fork button above so you have your own copy of this repo.

    set GITHUB_USERNAME=kalefranz
git clone git@github.com:conda/conda "%CONDA_PROJECT_ROOT%"
cd "%CONDA_PROJECT_ROOT%"
git remote add %GITHUB_USERNAME% git@github.com:%GITHUB_USERNAME%/conda

    To be sure that the conda code being interpreted is the code in the project directory, look at the value of conda location: in the output of conda info --all.

  3. Create a local development environment, and activate that environment

    .\dev\start

    This command will create a project-specific base environment at .\devenv. If the environment already exists, this command will just quickly activate the already-created .\devenv environment.

Conda 贡献者许可协议

Conda Contributor License Agreement

如果你是首次接触 CLA（贡献者许可协议），这在大型开源项目中是非常常见的流程。 Django 和 Python 项目都采用了类似机制。

> CLA 协议的批准最终由人工完成，流程并非完全自动化，

> 因此你的 PR 上的 CLA 检查可能需要一些时间才能通过。

In case you're new to CLAs, this is rather standard procedure for larger projects. Django and even Python itself both use something similar.

> CLA agreements are ultimately approved by a person and are not fully automatic, so it

> may take some time for the CLA checks on your PRs to run successfully.