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Anaconda is a prepackaged conda distribution for mostly python based analytics and research purposes. WMF maintains a custom debian package of Anaconda that includes some extra packages, but also has scripts for creating 'stacked' conda user environments. These conda user environments allow users to install packages into their own conda environment without modifying the base anaconda environment.
Listing conda environments
/usr/lib/anaconda-wmf/bin/conda env list # conda environments: # 2020-08-19T16.19.37_otto /home/otto/.conda/envs/2020-08-19T16.19.37_otto 2020-08-19T16.47.40_otto /home/otto/.conda/envs/2020-08-19T16.47.40_otto 2020-08-19T16.56.54_otto /home/otto/.conda/envs/2020-08-19T16.56.54_otto 2020-08-19T16.59.40_otto /home/otto/.conda/envs/2020-08-19T16.59.40_otto 2020-12-13T19.40.09_otto /home/otto/.conda/envs/2020-12-13T19.40.09_otto base * /usr/lib/anaconda-wmf
Anaconda base environment
To use the readonly Anaconda base environment, you can simply run python or other executables directly out of
/usr/lib/anaconda-wmf/bin. If you prefer to activate the anaconda base environment, run
Creating a new conda user environment
and a new conda environment will be created for you in ~/.conda/envs. When used, this environment will automatically append the base conda environment Python load paths to its own. If the same package is installed in both environments, your user conda environment's package will take precedence.
If you prefer, you can name your conda environment
Activating a conda user environment
There are several ways to activate a conda user environment. Just running
On its own will attempt to guess at the most recent conda environment to activate. If you only have one conda environment, this will work.
You can also specify the name of the conda env to activate. Run
/usr/lib/anaconda-wmf/bin/conda info --envs to get a list of available conda environments. E.g.
source conda-activate-stacked otto_2020-08-17T20.52.02
Or, you can run the 'activate' script out if your conda environment path:
Installing packages into your user conda environment
After activating your user conda environment, you can set http proxy env vars and install conda and pip packages. E.g.
export http_proxy=http://webproxy.eqiad.wmnet:8080 export https_proxy=http://webproxy.eqiad.wmnet:8080 conda install -c conda-forge <desired_conda_package> pip install --ignore-installed <desired_pip_package>
Conda is much preferred over pip, if the package you need is available via Conda. Conda can better track packages and their install locations than pip.
--ignore-installed flag for
pip install. This is only needed if you are installing a pip package into your Conda environment that already exists in the base anaconda-wmf environment.
These packages will be installed into the currently activated Conda user environment.
Deactivating your user conda environment
Or, since the user conda env's bin dir has been added to your path, you should also be able to just run
stacked conda environments
Conda supports activating environments 'stacked' on another one. However, all this 'stacking' does by default is leave the base conda environment's bin directory on your PATH. It does not allow for python dependencies from multiple environments.
Our customization fixes this. When conda-create-stacked is run, an anaconda.pth file is created in the new conda environment's site-packages directory. This file tells Python to add the anaconda-wmf base environemnt python search paths to its own. If a package is present in both environments, the stacked conda environment's version will take precedence.
WMF's anaconda environment support was built with Python in mind. Other languages are passively supported.
R is included in the base anaconda-wmf environment, but it is not installed into the user conda environment by default. Doing so makes the size of user environments much larger, and makes distributing them to HDFS take much longer.
To install R packages into your user environment, do the following:
# Make sure you are using a conda env. This is not necessary if running in Jupyter. source conda-activate-stacked # Enable http proxy. This is not necessary if running in Jupyter export http_proxy=http://webproxy.eqiad.wmnet:8080; export https_proxy=http://webproxy.eqiad.wmnet:8080; export no_proxy=127.0.0.1,localhost,.wmnet # R is currently the base anaconda-wmf R. which R /usr/lib/anaconda-wmf/bin/R # Install the conda R package into your user conda environment. conda install R # R is now fully contained in your user conda environment. which R /home/otto/.conda/envs/2021-04-07T21.37.00_otto/bin/R
You should now be able to install R packages using R's package manager via
However, just like with Python, installing R packages with conda is preferred over using R's package manager. If a conda R package exists, you should be able to just install it like:
$ conda install r-tidyverse