Conda-Anaconda的虛擬環境和包管理功能

Anaconda是一個用於大數據和科學計算的Python工具包，其中的包管理工具爲conda，與python標準的pip包管理工具相似，可是融合了virtualenv的虛擬環境管理功能。這裏介紹conda的虛擬環境建立和管理的功能。

Managing environments

Contents

• Managing environments

• Export the environment file

• Use environment from file

• Build identical conda environments

• Build identical conda environments with URLs

• Create an environment

• Change environments (activate/deactivate)

• Create a separate environment

• List all environments

• Verify current environment

• Clone an environment

• Remove an environment

• Share an environment

• Saved environment variables

With conda, you can create, export, list, remove, and update environments that have different versions of Python and/or packages installed in them. Switching or moving between environments is called   activating the environment. You can even share an environment file with a coworker.

Anytime you wish to see the full documentation for any command, type the command followed by  --help. For example, to learn about the conda environment command:

conda env --help

The same help that is available in conda is also available online from our command reference documentation.

Create an environment

In order to manage environments, we need to create at least two so you can move or switch between them.

To create a new environment, use the conda create command, followed by any name you wish to call it:

conda create --name snowflakes biopython

When conda asks you

proceed ([y]/n)?

Type 「y」 for 「yes.」

This will create a new environment named /envs/snowflakes that contains the program Biopython. This environment will use the same version of Python that you are currently using, because you did not specify a version.

TIP:  Many frequently used options after two dashes (--) can be abbreviated with just a dash and the first letter. So --name and -n options are the same and --envs and -e are the same. See conda --help or conda -h for a list of abbreviations.

Change environments (activate/deactivate)

Linux, OS X: source activate snowflakes

Windows:  activate snowflakes

Conda prepends the path name (snowflakes) onto your system command.

TIP: Environments are installed by default into the envs directory in your conda directory. You can specify a different path, see conda create --help for details.

Deactivate the environment with the following:

Linux, OS X: source deactivate

Windows:  deactivate

Conda removes the path name (snowflakes) from your system command.

Create a separate environment

So you can try switching or moving between environments, create and name a new environment. With this second environment, you can install a different version of Python, and a couple of packages:

conda create --name bunnies python=3 astroid babel

This will create a second new environment named /envs/bunnies with Python 3 and Astroid and Babel installed.

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

TIP: You can add much more to the conda create command, type conda create --help for details.

List all environments

Now you can use conda to see which environments you have installed so far. Use the conda environment info command to find out:

conda info --envs

You will see a list of environments like the following:

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

You can also use the conda environments list command as follows:

conda env list

The list of all environments will be the same with either command.

Verify current environment

Which environment are you using right now – snowflakes or bunnies? To find out, type the command:

conda info --envs

Conda displays the list of all environments, with the current environment highlighted with an ‘*’ character.

Clone an environment

Make an exact copy of an environment by creating a clone of it. Here we will clone snowflakes to create an exact copy named flowers:

conda create --name flowers --clone snowflakes

Check to see the exact copy was made:

conda info --envs

You should now see the three environments listed:  flowers, bunnies, and snowflakes.

Remove an environment

If you didn’t really want an environment named flowers, just remove it as follows:

conda remove --name flowers --all

To verify that the flowers environment has now been removed, type the command:

conda info --envs

Flowers is no longer in your environment list, so we know it was deleted.

Share an environment

You may want to share your environment with another person, 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, you can give them a copy of your environment.yml file.

Export the environment file

To enable another person to create an exact copy of your environment, you will export the active environment file.

Activate the environment you wish to export:

Linux, OS X: source activate peppermint

Windows: activate peppermint

NOTE: Replace 「peppermint」 with the name of the environment.

NOTE: If you already have an environment.yml file in your current directory, it will be overwritten with the new file.

Export your active environment to the new file:

All users: conda env export > environment.yml

NOTE: This file handles the environment’s pip packages as well as its conda packages.

Email or copy the exported environment.yml file to the other person.

The other person will then need to create the environment by the following command:

conda env create -f environment.yml

Use environment from file

Create a copy of another developer’s environment from their environment.yml file:

conda env create -f environment.yml

Activate the new environment:

Linux, OS X: source activate peppermint

Windows: activate peppermint

NOTE: Replace 「peppermint」 with the name of the environment.

Verify that the new environment was installed correctly:

conda list

Build identical conda environments

Explicit specification files can be used to build an identical conda environment on the same operating system platform, either on the same machine or a different machine.

The command conda list -e produces a spec list such as the following:

# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: osx-64
astropy=1.0.4=np19py27_0
ncurses=5.9=1
numpy=1.9.2=py27_0
openssl=1.0.1k=1
pandas=0.16.2=np19py27_0
pip=7.1.2=py27_0
python=2.7.10=0
python-dateutil=2.4.2=py27_0
pytz=2015.4=py27_0
readline=6.2.5=1
setuptools=18.1=py27_0
six=1.9.0=py27_0
sqlite=3.8.4.1=1
tk=8.5.18=0
wheel=0.24.0=py27_0
zlib=1.2.8=1

With the command conda list -e > spec-file.txt you can create a file containing this spec list in the current working directory. You may use the filename spec-file.txt or any other filename.

As the comment at the top of the file explains, with the command conda create --name MyEnvironment --file spec-file.txt you can use the spec file to create a matching environment on the same machine or another machine. Replace spec-file.txt with whatever file name you chose when you created the file. You may use the environment name MyEnvironment or substitute any other environment name to give your newly created environment.

NOTE: These explicit spec files are not usually cross platform, and therefore have a comment at the top such as # platform: osx-64 showing the platform where they were 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.

Build identical conda environments with URLs

If two users have their conda channels set up differently, then they may inadvertently create different environments from the same spec file because conda fetches the packages from different channels.

To prevent this, starting in version 3.18.2 conda offers an option conda list --explicit, which displays a list of the universal resource locators (URLs) of all conda packages installed in the current environment, as shown by this small example showing only three packages:

# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: linux-64
@EXPLICIT
https://repo.continuum.io/pkgs/free/linux-64/xlsxwriter-0.7.5-py27_0.tar.bz2
https://repo.continuum.io/pkgs/pro/linux-64/iopro-1.7.1-np19py27_p1.tar.bz2
https://repo.continuum.io/pkgs/free/linux-64/python-2.7.10-1.tar.bz2

The command conda list --explicit > explicit-spec-file.txt creates a file containing this spec list in the current working directory.

As the comment at the top of the file explains, the command conda create --name MyEnvironment --file explicit-spec-file.txt uses the spec file to create an identical environment on the same machine or another machine.

The command conda install --name MyEnvironment --file explicit-spec-file.txt adds these packages to an existing environment.

NOTE: Conda does not check architecture or dependencies when installing from an explicit specification file. To ensure the packages work correctly, be sure that the file was created from a working environment and that it is  used on the same architecture, operating system and platform, such as linux-64 or osx-64.

Saved environment variables

On Linux and OS X, conda environments can include saved environment variables. Suppose you want an environment ‘analytics’ to store a secret key needed to log in to a server and a path to a configuration file. Locate the directory for the conda environment, such as /home/jsmith/anaconda3/envs/analytics . Enter that directory and create these subdirectories and files:

cd /home/jsmith/anaconda3/envs/analytics
mkdir -p ./etc/conda/activate.d
mkdir -p ./etc/conda/deactivate.d
touch ./etc/conda/activate.d/env_vars.sh
touch ./etc/conda/deactivate.d/env_vars.sh

Edit the two files. ./etc/conda/activate.d/env_vars.sh should have this:

#!/bin/sh

export MY_KEY='secret-key-value'
export MY_FILE=/path/to/my/file/

And ./etc/conda/deactivate.d/env_vars.sh should have this:

#!/bin/sh

unset MY_KEY
unset MY_FILE

Now when you use source activate analytics the environment variables MY_KEY and MY_FILE will be set to the values you wrote into the file, and when you use source deactivate those variables will be erased.

Next, we’ll take a look at Managing Python.