管理环境#

以下步骤请在终端中执行：

1. 创建一个环境：

   conda create --name <my-env>
   

   将 <my-env> 替换为你的环境名称。

2. 当 conda 提示你是否继续时，输入 y：

   proceed ([y]/n)?
   

   这将在 /envs/ 目录中创建一个名为 myenv 的环境，尚未安装任何软件包。

3. 创建一个指定 Python 版本的环境：

   conda create -n myenv python=3.9
   

4. 创建一个包含特定软件包的环境：

   conda create -n myenv scipy
   

   或者：

   conda create -n myenv python
   conda install -n myenv scipy
   

5. 创建一个包含特定版本软件包的环境：

   conda create -n myenv scipy=0.17.3
   

   或者：

   conda create -n myenv python
   conda install -n myenv scipy=0.17.3
   

6. 创建一个包含特定 Python 版本和多个软件包的环境：

   conda create -n myenv python=3.9 scipy=0.17.3 astroid babel
   

   小技巧

   建议在创建环境时一次性安装所有需要的程序，逐个安装可能会导致依赖冲突。

若要在每次创建新环境时自动安装 pip 或其他程序，可将默认程序添加到 .condarc 配置文件的 create_default_packages 部分。默认软件包将在每次创建环境时自动安装。如果你不希望在某个环境中安装默认包，可使用 --no-default-packages 标志：

conda create --no-default-packages -n myenv python

Use the terminal for the following steps:

1. To create an environment:

   conda create --name <my-env>
   

   Replace <my-env> with the name of your environment.

2. When conda asks you to proceed, type y:

   proceed ([y]/n)?
   

   This creates the myenv environment in /envs/. No packages will be installed in this environment.

3. To create an environment with a specific version of Python:

   conda create -n myenv python=3.9
   

4. To create an environment with a specific package:

   conda create -n myenv scipy
   

   or:

   conda create -n myenv python
   conda install -n myenv scipy
   

5. To create an environment with a specific version of a package:

   conda create -n myenv scipy=0.17.3
   

   or:

   conda create -n myenv python
   conda install -n myenv scipy=0.17.3
   

6. To create an environment with a specific version of Python and multiple packages:

   conda create -n myenv python=3.9 scipy=0.17.3 astroid babel
   

   小技巧

   Install all the programs that you want in this environment at the same time. Installing one program at a time can lead to dependency conflicts.

To automatically install pip or another program every time a new environment is created, add the default programs to the create_default_packages section of your .condarc configuration file. The default packages are installed every time you create a new environment. If you do not want the default packages installed in a particular environment, use the --no-default-packages flag:

conda create --no-default-packages -n myenv python

以下步骤请在终端中执行：

1. 通过 environment.yml 文件创建环境：

   conda env create -f environment.yml
   

   yml 文件的第一行定义新环境的名称。 详情请参见 手动创建环境文件。

2. 激活新环境： conda activate myenv

3. 验证新环境是否安装成功：

   conda env list
   

   你也可以使用 conda info --envs。

Use the terminal for the following steps:

1. Create the environment from the environment.yml file:

   conda env create -f environment.yml
   

   The first line of the yml file sets the new environment's name. For details see Creating an environment file manually.

2. Activate the new environment: conda activate myenv

3. Verify that the new environment was installed correctly:

   conda env list
   

   You can also use conda info --envs.

默认情况下，conda 会为当前运行平台创建环境。你可以通过运行 conda info 并查看 platform 字段来确认当前平台。

不过在某些情况下，你可能希望为不同的目标平台或架构创建环境。为此，可以在 conda create 和 conda env create 命令中使用 --platform 选项。更多允许的取值请参见 conda create 中的 --subdir, --platform。

例如，在 Apple Silicon 平台上运行 macOS 的用户，可能希望为 Intel 处理器创建一个 python 环境，并通过 Rosetta 模拟可执行文件。命令如下：

conda create --platform osx-64 --name python-x64 python

该选项还允许指定不同操作系统（如在 macOS 上创建 Linux 环境），但我们不建议将其用于 --dry-run 以外的操作。不匹配操作系统可能会导致以下问题：

• 无法解析环境，因为缺少虚拟包。 你可以通过设置必要的 CONDA_OVERRIDE_**** 环境变量来解决。例如，在 macOS 上解析 Linux 环境时，可能需要设置 CONDA_OVERRIDE_LINUX=1 和 CONDA_OVERRIDE_GLIBC=2.17。

• 能够解析环境，但在提取和链接阶段失败，原因是文件系统限制（如大小写不敏感、不兼容路径等）。唯一的解决方案是使用 --dry-run --json 获取解决方案，并将其转换为锁文件，供目标机器使用。详情请参见 在不创建环境的情况下创建显式锁文件。

By default, conda will create environments targeting the platform it's currently running on. You can check which platform you are currently on by running conda info and checking the platform entry.

However, in some cases you might want to create an environment for a different target platform or architecture. To do so, use the --platform flag available in the conda create and conda env create commands. See --subdir, --platform in conda create for more information about allowed values.

For example, a user running macOS on the Apple Silicon platform might want to create a python environment for Intel processors and emulate the executables with Rosetta. The command would be:

conda create --platform osx-64 --name python-x64 python

This flag also allows specifying a different OS (e.g. creating a Linux environment on macOS), but we don't recommend its usage outside of --dry-run operations. Common problems with mismatched OSes include:

• The environment cannot be solved because virtual packages are missing. You can workaround this issue by exporting the necessary CONDA_OVERRIDE_**** environment variables. For example, solving for Linux from macOS, you will probably need CONDA_OVERRIDE_LINUX=1 and CONDA_OVERRIDE_GLIBC=2.17.

• The environment can be solved, but extraction and linking fails due filesystem limitations (case insensitive systems, incompatible paths, etc). The only workaround here is to use --dry-run --json to obtain the solution and process the payload into a lockfile that can be shared with the target machine. See 在不创建环境的情况下创建显式锁文件 for more details.

你可以通过为目标目录指定路径，在该目录中创建 conda 环境。例如，以下命令将在当前工作目录的 envs 子目录中创建一个新环境：

conda create --prefix ./envs jupyterlab=3.2 matplotlib=3.5 numpy=1.21

使用前缀创建的环境可以通过相同命令激活：

conda activate ./envs

在项目目录的子目录中创建环境具有如下好处：

• 通过将环境作为子目录包含在项目中，可以轻松判断该项目是否使用隔离环境。

• 项目更加自包含，所需软件全部包含在单一目录中。

在项目子目录中创建环境的额外好处是，可以为所有环境使用相同名称。 如果你将所有环境保存在 envs 文件夹中，则必须为每个环境指定不同名称。

将 conda 环境放在默认 envs 文件夹之外时，有几点需要注意：

1. Conda 不再能通过 --name 参数找到你的环境。 通常需要使用 --prefix 参数并提供完整路径。

2. 在创建环境时指定安装路径后，命令提示符将显示该环境的绝对路径前缀，而不是环境名称。

使用前缀激活环境后，命令提示符类似于：

(/absolute/path/to/envs) $

这可能导致前缀过长：

(/Users/USER_NAME/research/data-science/PROJECT_NAME/envs) $

若要在 shell 提示符中移除该长前缀，可修改 .condarc 文件中的 env_prompt 设置：

conda config --set env_prompt '({name})'

如果你已有 .condarc 文件，该命令将编辑文件；否则将创建新文件。

之后你的命令提示符将显示活跃环境的通用名称，即环境根目录的名称：

$ cd project-directory
$ conda activate ./env
(env) project-directory $

You can control where a conda environment lives by providing a path to a target directory when creating the environment. For example, the following command will create a new environment in a subdirectory of the current working directory called envs:

conda create --prefix ./envs jupyterlab=3.2 matplotlib=3.5 numpy=1.21

You then activate an environment created with a prefix using the same command used to activate environments created by name:

conda activate ./envs

Specifying a path to a subdirectory of your project directory when creating an environment has the following benefits:

• It makes it easy to tell if your project uses an isolated environment by including the environment as a subdirectory.

• It makes your project more self-contained as everything, including the required software, is contained in a single project directory.

An additional benefit of creating your project’s environment inside a subdirectory is that you can then use the same name for all your environments. If you keep all of your environments in your envs folder, you’ll have to give each environment a different name.

There are a few things to be aware of when placing conda environments outside of the default envs folder.

1. Conda can no longer find your environment with the --name flag. You’ll generally need to pass the --prefix flag along with the environment’s full path to find the environment.

2. Specifying an install path when creating your conda environments makes it so that your command prompt is now prefixed with the active environment’s absolute path rather than the environment’s name.

After activating an environment using its prefix, your prompt will look similar to the following:

(/absolute/path/to/envs) $

This can result in long prefixes:

(/Users/USER_NAME/research/data-science/PROJECT_NAME/envs) $

To remove this long prefix in your shell prompt, modify the env_prompt setting in your .condarc file:

conda config --set env_prompt '({name})'

This will edit your .condarc file if you already have one or create a .condarc file if you do not.

Now your command prompt will display the active environment’s generic name, which is the name of the environment's root folder:

$ cd project-directory
$ conda activate ./env
(env) project-directory $

你可能出于多种原因需要更新环境。例如：

• 某个核心依赖项发布了新版本（依赖项版本号更新）；

• 你需要一个额外的数据分析软件包（添加新依赖项）；

• 你发现了更好的软件包，不再需要旧的软件包（添加新依赖项并移除旧依赖项）。

如果发生这些情况，只需相应地更新你的 environment.yml 文件内容，并运行以下命令：

conda env update --file environment.yml  --prune

You may need to update your environment for a variety of reasons. For example, it may be the case that:

• one of your core dependencies just released a new version (dependency version number update).

• you need an additional package for data analysis (add a new dependency).

• you have found a better package and no longer need the older package (add new dependency and remove old dependency).

If any of these occur, all you need to do is update the contents of your environment.yml file accordingly and then run the following command:

conda env update --file environment.yml  --prune

请在终端中执行以下步骤：

你可以通过克隆已有环境来创建一个环境的精确副本：

conda create --name myclone --clone myenv

要验证副本是否创建成功：

conda info --envs

在显示的环境列表中，你应能看到源环境和新复制的环境。

Use the terminal for the following steps:

You can make an exact copy of an environment by creating a clone of it:

conda create --name myclone --clone myenv

To verify that the copy was made:

conda info --envs

In the environments list that displays, you should see both the source environment and the new copy.

你可以使用显式规范文件，在相同操作系统平台上（无论是在同一台机器还是另一台机器上）构建一个完全相同的 conda 环境。

请在终端中执行以下步骤：

1. 运行 conda list --explicit 来生成如下规范列表：

   # This file may be used to create an environment using:
   # $ conda create --name <env> --file <this file>
   # platform: osx-64
   @EXPLICIT
   https://repo.anaconda.com/pkgs/free/osx-64/mkl-11.3.3-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/numpy-1.11.1-py35_0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/openssl-1.0.2h-1.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/pip-8.1.2-py35_0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/python-3.5.2-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/readline-6.2-2.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/setuptools-25.1.6-py35_0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/sqlite-3.13.0-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/tk-8.5.18-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/wheel-0.29.0-py35_0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/xz-5.2.2-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/zlib-1.2.8-3.tar.bz2
   

2. 若要在当前工作目录中将此规范列表保存为文件，请运行：

   conda list --explicit > spec-file.txt
   

   备注

   你可以使用 spec-file.txt 作为文件名，或替换为你选择的其他文件名。

   显式规范文件通常不具备跨平台兼容性，因此文件顶部会有类似 # platform: osx-64 的注释，表明该文件在哪个平台上创建，并已知可用。在其他平台上，某些软件包可能不可用，或关键依赖可能缺失。

   使用规范文件创建相同环境的命令如下：

   conda create --name myenv --file spec-file.txt
   

   若要将文件中列出的软件包安装到已有环境中：

   conda install --name myenv --file spec-file.txt
   

   Conda 在从规范文件安装软件包时不会检查架构或依赖关系。为确保软件包正确运行，请确保该文件来自可正常运行的环境，并在相同的架构、操作系统和平台（如 linux-64 或 osx-64）上使用。

You can use explicit specification files to build an identical conda environment on the same operating system platform, either on the same machine or on a different machine.

Use the terminal for the following steps:

1. Run conda list --explicit to produce a spec list such as:

   # This file may be used to create an environment using:
   # $ conda create --name <env> --file <this file>
   # platform: osx-64
   @EXPLICIT
   https://repo.anaconda.com/pkgs/free/osx-64/mkl-11.3.3-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/numpy-1.11.1-py35_0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/openssl-1.0.2h-1.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/pip-8.1.2-py35_0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/python-3.5.2-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/readline-6.2-2.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/setuptools-25.1.6-py35_0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/sqlite-3.13.0-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/tk-8.5.18-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/wheel-0.29.0-py35_0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/xz-5.2.2-0.tar.bz2
   https://repo.anaconda.com/pkgs/free/osx-64/zlib-1.2.8-3.tar.bz2
   

2. To create this spec list as a file in the current working directory, run:

   conda list --explicit > spec-file.txt
   

   备注

   You can use spec-file.txt as the filename or replace it with a filename of your choice.

   An explicit spec file is not usually cross platform, and therefore has a comment at the top such as # platform: osx-64 showing the platform where it was created. This platform is the one where this spec file is known to work. On other platforms, the packages specified might not be available or dependencies might be missing for some of the key packages already in the spec.

   To use the spec file to create an identical environment on the same machine or another machine:

   conda create --name myenv --file spec-file.txt
   

   To use the spec file to install its listed packages into an existing environment:

   conda install --name myenv --file spec-file.txt
   

   Conda does not check architecture or dependencies when installing from a spec file. To ensure that the packages work correctly, make sure that the file was created from a working environment, and use it on the same architecture, operating system, and platform, such as linux-64 or osx-64.

激活环境是使环境中软件正常工作的关键。激活操作主要包含两个方面：将环境路径添加到 PATH 中，以及运行环境中可能包含的激活脚本。通过激活脚本，软件包可以设置它们运行所需的任意环境变量。你也可以使用 配置 API 设置环境变量。

激活会将环境路径前置添加到 PATH 中。这种更改仅在当前终端会话中有效，不具有全局作用。

激活环境： conda activate myenv

Conda 会将 myenv 的路径前置添加到系统命令中。

如果你未激活环境，可能会看到如下警告信息：

Warning:
This Python interpreter is in a conda environment, but the environment has
not been activated. Libraries may fail to load. To activate this environment
please see https://conda.io/activation.

如果出现此类警告，你需要激活环境。Windows 用户可在终端中运行： c:\Anaconda3\Scripts\activate。

Windows 对激活要求极为严格。这是因为其库加载机制不支持“知道依赖路径”的可执行文件或库（RPATH）。相反，Windows 依赖的是其自身的 动态链接库搜索顺序。

如果未激活环境，可能会导致库加载失败，并产生大量错误。例如，如果某个子环境中的 Python 无法找到所需的 OpenSSL 库，就可能出现 HTTP 或 SSL 错误。

Conda 自身做了一些特殊处理，可以自动添加必要的 PATH 条目。这使得即使未激活环境或在子环境中，也能调用 conda 命令。但通常情况下，若未激活环境直接运行其中的可执行文件，很可能会失败。

若想在已激活的环境中运行可执行程序，可以使用 conda run 命令。

如果遇到 PATH 相关错误，可参考我们的 疑难解答。

Activating environments is essential to making the software in the environments work well. Activation entails two primary functions: adding entries to PATH for the environment and running any activation scripts that the environment may contain. These activation scripts are how packages can set arbitrary environment variables that may be necessary for their operation. You can also use the config API to set environment variables.

Activation prepends to PATH. This only takes effect when you have the environment active so it is local to a terminal session, not global.

To activate an environment: conda activate myenv

Conda prepends the path name myenv onto your system command.

You may receive a warning message if you have not activated your environment:

Warning:
This Python interpreter is in a conda environment, but the environment has
not been activated. Libraries may fail to load. To activate this environment
please see https://conda.io/activation.

If you receive this warning, you need to activate your environment. To do so on Windows, run: c:\Anaconda3\Scripts\activate in a terminal window.

Windows is extremely sensitive to proper activation. This is because the Windows library loader does not support the concept of libraries and executables that know where to search for their dependencies (RPATH). Instead, Windows relies on a dynamic-link library search order.

If environments are not active, libraries won't be found and there will be lots of errors. HTTP or SSL errors are common errors when the Python in a child environment can't find the necessary OpenSSL library.

Conda itself includes some special workarounds to add its necessary PATH entries. This makes it so that it can be called without activation or with any child environment active. In general, calling any executable in an environment without first activating that environment will likely not work. For the ability to run executables in activated environments, you may be interested in the conda run command.

If you experience errors with PATH, review our troubleshooting.

若要停用当前环境，请输入： conda deactivate

此命令会将当前激活环境的路径从系统命令中移除。

To deactivate an environment, type: conda deactivate

Conda removes the path name for the currently active environment from your system command.

请在终端中执行以下操作。

默认情况下，当前正在使用的活跃环境会以括号（）或中括号 [] 的形式显示在命令提示符的开头:

(myenv) $

如果你没有看到这样的提示，请运行：

conda info --envs

在显示的环境列表中，当前所处的环境将以星号（*）标记。

默认情况下，命令提示符会显示当前激活环境的名称。若要关闭该行为:

conda config --set changeps1 false

若要重新启用该行为:

conda config --set changeps1 true

Use the terminal for the following steps.

By default, the active environment---the one you are currently using---is shown in parentheses () or brackets [] at the beginning of your command prompt:

(myenv) $

If you do not see this, run:

conda info --envs

In the environments list that displays, your current environment is highlighted with an asterisk (*).

By default, the command prompt is set to show the name of the active environment. To disable this option:

conda config --set changeps1 false

To re-enable this option:

conda config --set changeps1 true

若要查看你的所有环境，在终端窗口中运行：

conda info --envs

或

conda env list

将显示如下类似的列表：

conda environments:
myenv                 /home/username/miniconda/envs/myenv
snowflakes            /home/username/miniconda/envs/snowflakes
bunnies               /home/username/miniconda/envs/bunnies

如果该命令由管理员用户执行，则会显示所有用户拥有的环境列表。

To see a list of all of your environments, in your terminal window, run:

conda info --envs

OR

conda env list

A list similar to the following is displayed:

conda environments:
myenv                 /home/username/miniconda/envs/myenv
snowflakes            /home/username/miniconda/envs/snowflakes
bunnies               /home/username/miniconda/envs/bunnies

If this command is run by an administrator, a list of all environments belonging to all users will be displayed.

要查看特定环境中已安装的所有包：

• 如果环境尚未激活，请在终端中运行：

  conda list -n myenv
  

• 如果环境已激活，请在终端中运行：

  conda list
  

• 若要查看某个特定包是否已安装在某个环境中，请在终端中运行：

  conda list -n myenv scipy
  

To see a list of all packages installed in a specific environment:

• If the environment is not activated, in your terminal window, run:

  conda list -n myenv
  

• If the environment is activated, in your terminal window, run:

  conda list
  

• To see if a specific package is installed in an environment, in your terminal window, run:

  conda list -n myenv scipy
  

若要在你的环境中使用 pip，请在终端中运行：

conda install -n myenv pip
conda activate myenv
pip <pip_subcommand>

同时使用 pip 和 conda 时可能会遇到问题。推荐的做法是在一个隔离的 conda 环境中使用 pip。在使用 pip 安装剩余软件之前，应尽可能使用 conda 安装所需的软件包。如果需要对环境进行修改，建议新建一个环境，而不是在 pip 安装包之后再运行 conda。适当时，应将 conda 和 pip 的依赖需求分别保存在文本文件中。

我们推荐以下做法：

仅在 conda 之后使用 pip

• 尽可能使用 conda 安装所有依赖，再使用 pip。

• pip 应使用 --upgrade-strategy only-if-needed （默认策略）运行。

• 不要使用 pip 的 --user 参数，避免全局安装。

使用 conda 环境实现隔离

• 创建一个 conda 环境，用于隔离 pip 带来的改动。

• 由于采用硬链接，环境本身占用空间很小。

• 应避免在 root 环境中运行 pip。

如需修改环境，建议重新创建

• 一旦使用 pip 安装了软件，conda 将无法感知这些改动。

• 若需安装额外的 conda 包，建议重新创建该环境。

将 conda 和 pip 的依赖写入文本文件

• conda 可使用 --file 参数读取包依赖。

• pip 可使用 -r 或 --requirements 参数读取 Python 包列表。

• conda env 可以基于包含 conda 与 pip 依赖的文件导出或创建环境。

To use pip in your environment, in your terminal window, run:

conda install -n myenv pip
conda activate myenv
pip <pip_subcommand>

Issues may arise when using pip and conda together. When combining conda and pip, it is best to use an isolated conda environment. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software. If modifications are needed to the environment, it is best to create a new environment rather than running conda after pip. When appropriate, conda and pip requirements should be stored in text files.

We recommend that you:

Use pip only after conda

• Install as many requirements as possible with conda then use pip.

• Pip should be run with --upgrade-strategy only-if-needed (the default).

• Do not use pip with the --user argument, avoid all users installs.

Use conda environments for isolation

• Create a conda environment to isolate any changes pip makes.

• Environments take up little space thanks to hard links.

• Care should be taken to avoid running pip in the root environment.

Recreate the environment if changes are needed

• Once pip has been used, conda will be unaware of the changes.

• To install additional conda packages, it is best to recreate the environment.

Store conda and pip requirements in text files

• Package requirements can be passed to conda via the --file argument.

• Pip accepts a list of Python packages with -r or --requirements.

• Conda env will export or create environments based on a file with conda and pip requirements.

如果你希望将环境变量与某个环境关联，推荐使用配置 API。这是一种更安全的替代方案，优于使用激活与停用脚本执行任意代码的做法。

首先，创建并激活你的环境:

conda create -n test-env
conda activate test-env

若要列出当前的环境变量，运行 conda env config vars list。

若要设置环境变量，运行 conda env config vars set my_var=value。

设置完成后，需要重新激活环境：conda activate test-env。

要检查环境变量是否已设置，可运行 echo $my_var （Windows 下为 echo %my_var%）或 conda env config vars list。

当你停用环境后，再运行上述命令，即可看到环境变量已被移除。

你可以通过 -n 或 -p 参数指定要操作的环境。其中 -n 用于指定环境名称，-p 用于指定环境路径。

若要取消某个环境变量，运行：conda env config vars unset my_var -n test-env。

当你停用环境后，可重新运行 echo my_var 或 conda env config vars list，确认变量已不再存在。

通过 conda env config vars 设置的环境变量会被包含在 conda env export 的导出结果中。此外，你也可以在 environment.yml 文件中声明环境变量，如下所示:

name: env-name
channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.7
  - codecov
variables:
  VAR1: valueA
  VAR2: valueB

If you want to associate environment variables with an environment, you can use the config API. This is recommended as an alternative to using activate and deactivate scripts since those are an execution of arbitrary code that may not be safe.

First, create your environment and activate it:

conda create -n test-env
conda activate test-env

To list any variables you may have, run conda env config vars list.

To set environment variables, run conda env config vars set my_var=value.

Once you have set an environment variable, you have to reactivate your environment: conda activate test-env.

To check if the environment variable has been set, run echo $my_var (echo %my_var% on Windows) or conda env config vars list.

When you deactivate your environment, you can use those same commands to see that the environment variable goes away.

You can specify the environment you want to affect using the -n and -p flags. The -n flag allows you to name the environment and -p allows you to specify the path to the environment.

To unset the environment variable, run conda env config vars unset my_var -n test-env.

When you deactivate your environment, you can see that environment variable goes away by rerunning echo my_var or conda env config vars list to show that the variable name is no longer present.

Environment variables set using conda env config vars will be retained in the output of conda env export. Further, you can declare environment variables in the environment.yml file as shown here:

name: env-name
channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.7
  - codecov
variables:
  VAR1: valueA
  VAR2: valueB

Conda 环境支持保存环境变量。

假设你希望为环境 "analytics" 保存一个登录服务器所需的密钥和一个配置文件路径。以下章节说明了如何分别在 Windows 与 macOS 或 Linux 上编写名为 env_vars 的脚本来实现这一目标。

这类脚本文件可以作为 conda 包的一部分。当包含该脚本的包被激活时，环境变量将自动生效。

你可以自由命名这些脚本。但由于多个包可能会创建脚本文件，因此请务必使用具有描述性的、不易与其他包冲突的名称。一种常见做法是使用类似 packagename-scriptname.sh 的命名方式，在 Windows 上则使用 packagename-scriptname.bat。

Conda environments can include saved environment variables.

Suppose you want an environment "analytics" to store both a secret key needed to log in to a server and a path to a configuration file. The sections below explain how to write a script named env_vars to do this on Windows and macOS or Linux.

This type of script file can be part of a conda package, in which case these environment variables become active when an environment containing that package is activated.

You can name these scripts anything you like. However, multiple packages may create script files, so be sure to use descriptive names that are not used by other packages. One popular option is to give the script a name in the form packagename-scriptname.sh, or on Windows, packagename-scriptname.bat.

你可以将环境分享给其他人，例如让他们复现你所做的测试。为了让他们快速复现你的环境及其所有包和版本，你可以将你的 environment.yml 文件复制给他们。

You may want to share your environment with someone else---for example, so they can re-create a test that you have done. To allow them to quickly reproduce your environment, with all of its packages and versions, give them a copy of your environment.yml file.

Conda 会记录环境中的所有更改历史，你可以轻松“回滚”至旧版本。列出当前环境的修改历史：

conda list --revisions

回滚到某个历史版本：

conda install --revision=REVNUM 或 conda install --rev REVNUM

示例： 若要回滚至修订号为 8 的版本，运行 conda install --rev 8。

Conda keeps a history of all the changes made to your environment, so you can easily "roll back" to a previous version. To list the history of each change to the current environment: conda list --revisions

To restore environment to a previous revision: conda install --revision=REVNUM or conda install --rev REVNUM.

Example: If you want to restore your environment to revision 8, run conda install --rev 8.

要移除一个环境，在终端中运行：

conda remove --name myenv --all

你也可以使用：

conda env remove --name myenv

验证环境是否已移除：

conda info --envs

输出的环境列表中不应再包含该已删除环境。

To remove an environment, in your terminal window, run:

conda remove --name myenv --all

You may instead use conda env remove --name myenv.

To verify that the environment was removed, in your terminal window, run:

conda info --envs

The environments list that displays should not show the removed environment.

@EXPLICIT 锁定文件允许你无需解析依赖（不启用 solver）就能重建环境。该文件包含一个 @EXPLICIT 头部，后接一组 conda 包的 URL，可选附加其 MD5 或 SHA256 校验值。

你可以通过 conda list --explicit 从现有环境生成它，如 构建相同的 conda 环境 所示。

但如果你只想获得锁定文件，是否必须先临时创建一个环境再删除它？答案是：不需要。你可以使用 JSON 模式运行 conda，并使用 jq 工具处理输出。

Linux 和 macOS 下命令如下（将 MATCHSPECS_GO_HERE 替换为你想指定的包）：

echo "@EXPLICIT" > explicit.txt
CONDA_PKGS_DIRS=$(mktemp -d) conda create --dry-run MATCHSPECS_GO_HERE --json | jq -r '.actions.FETCH[] | .url + "#" + .md5' >> explicit.txt

Windows 下只需稍作调整：

echo "@EXPLICIT" > explicit.txt
set "CONDA_PKGS_DIRS=%TMP%\conda-%RANDOM%"
conda create --dry-run MATCHSPECS_GO_HERE --json | jq -r '.actions.FETCH[] | .url + "#" + .md5' >> explicit.txt
set "CONDA_PKGS_DIRS="

然后你就可以使用该 explicit.txt 文件创建新环境：

conda create -n new-environment --file explicit.txt

@EXPLICIT lockfiles allow you to (re)create environments without invoking the solver. They consist of an @EXPLICIT header plus a list of conda package URLs, optionally followed by their MD5 or SHA256 hash.

They can be obtained from existing environments via conda list --explicit, as seen in 构建相同的 conda 环境.

But what if you only need the lockfile? Would you need create to a temporary environment first just to delete it later? Fortunately, there's a way: you can invoke conda in JSON mode and then process the output with jq.

The command looks like this for Linux and macOS (replace MATCHSPECS_GO_HERE with the relevant packages you want):

echo "@EXPLICIT" > explicit.txt
CONDA_PKGS_DIRS=$(mktemp -d) conda create --dry-run MATCHSPECS_GO_HERE --json | jq -r '.actions.FETCH[] | .url + "#" + .md5' >> explicit.txt

The syntax in Windows only needs some small changes:

echo "@EXPLICIT" > explicit.txt
set "CONDA_PKGS_DIRS=%TMP%\conda-%RANDOM%"
conda create --dry-run MATCHSPECS_GO_HERE --json | jq -r '.actions.FETCH[] | .url + "#" + .md5' >> explicit.txt
set "CONDA_PKGS_DIRS="

The resulting explicit.txt can be used to create a new environment with:

conda create -n new-environment --file explicit.txt