Installing Python packages¶

pip and conda are the easiest ways of installing python packages and programs as user. In both cases it is advised to use virtual environments to separate between different workflows/projects. This makes it possible to have multiple versions of the same package or application without problems of conflicting dependencies.

Virtual environments¶

Virtual environments in Python are a nice way to compartmentalize package installation. You can have many virtual environment and we recommend that you at least have one for each disparate experiment. One additional benefit of this setup is that it allows other researchers to easily replicate your setup.

pip is the main package installer for Python and included in every Python installation. It is easy to use and can be combined with virtualenv to manage independent environments. These can contain different Python versions and packages.

In some cases, packages installed with pip have problems with complex dependencies and libraries. In this case, conda is the better solution.

Setup and installation with pip¶

Users can install Python packages in a virtual Python environment. Here is how you create a virtual environment with Python:

# First load an appropriate Python module (use 'module list Python' to see all)
$ module load Python/3.8.6-GCCcore-10.2.0
# Create the virtual environment.
$ python -m venv my_new_pythonenv
# Activate the environment.
$ source my_new_pythonenv/bin/activate
# Install packages with pip. Here we install pandas.
$ python -m pip install pandas

For more information, have a look at the official pip and virtualenv documentations.

Note

When running software from your Python environment in a batch script, it is highly recommended to activate the environment only in the script (see below), while keeping the login environment clean when submitting the job, otherwise the environments can interfere with each other (even if they are the same).

Using the virtual environment in a batch script¶

In a batch script you will activate the virtual environment in the same way as above. You must just load the python module first:

# Set up job environment
set -o errexit # exit on any error
set -o nounset # treat unset variables as error

# Load modules
module load Python/3.8.6-GCCcore-10.2.0

# Set the ${PS1} (needed in the source of the virtual environment for some Python versions)
export PS1=\$

# activate the virtual environment
source my_new_pythonenv/bin/activate

# execute example script
python pdexample.py

Anaconda, Miniconda & Conda¶

You can install many python and non-python packages yourself using conda or especially for bioinformatics software bioconda.

Conda enables you to easily install complex packages and software. Creating multiple enviroments enables you to have installations of the same software in different versions or incompatible software collections at once. You can easily share a list of the installed packages with collaborators or colleagues, so they can setup the same eniviroment in a matter of minutes.

Setup¶

First you load the miniconda module which is like a python and r package manager. Conda makes it easy to have multiple environments for example one python2 and one python3 based parallel to each other without interfering.

Load conda module¶

Start by removing all preloaded modules which can complicate things. We then display all installed version and load the newest Miniconda one (4.6.14):

$ ml purge
$ ml avail conda
$ ml Miniconda3/4.6.14

Setup conda activate command¶

To use conda activate interactively you have to initialise your shell once with:

$ conda init bash

Add channels¶

To install packages we first have to add the package repository to conda (we only have to do this once). This is the place conda will download the packages from.

$ conda config --add channels defaults
$ conda config --add channels conda-forge

If you want install bioinformatics packages you should also add the bioconda channel:

$ conda config --add channels bioconda

Supress unneccessary warnings¶

To suppress the warning that a newer version of conda exists which is usually not important for most users and will be fixed by us by installing a new module:

$ conda config --set notify_outdated_conda false

Create new environment¶

New environments are initialised with the conda create. During the creation you should list all the packages and software that should be installed in this environment instead of creating an empty one and installing them one by one. This makes the installation much faster and there is less chance for conda to get stuck in a dependency loop.

$ conda create --name ENVIRONMENT python=3 SOMESOFTWARE MORESOFTWARE

If you are planning on adding many libraries to your environment, you should consider placing it in a directory other than your $HOME, due to the storage restrictions on that folder. One alternative could be to use the Project area, please check out Storage areas on HPC clusters for other alternatives. To install conda in an alternative location, use the --prefix PATH or -p PATH option when creating a new environment.

conda create -p PATH SOMEPACKAGES

This enables multiple users of a project to share the conda environment by installing it into their project folder instead of the user’s home.

Daily usage¶

Interactively¶

To load this environment you have to use the following commands either on the command line or in your job script:

$ ml purge
$ ml Miniconda3/4.6.14 # Replace with the version available on the system
$ conda activate ENVIRONMENT

Then you can use all software as usual.

To deactivate the current environment:

$ conda deactivate

If you need to install additional software or packages, we can search for it with:

$ conda search SOMESOFTWARE

and install it with:

$ conda install -n ENVIRONMENT SOMESOFTWARE

If the python package you are looking for is not available in conda you can use pip like usually from within a conda environment to install additional python packages:

$ pip install SOMEPACKAGE

To update a single package with conda:

$ conda update -n ENVIRONMENT SOMESOFTWARE

or to update all packages:

$ conda update -n ENVIRONMENT --all

In batch/job scripts¶

To be able to use this environment in a batch script (job script), you will need to include the following in your batch script, before calling the python program:

# load the Anaconda3
module load Anaconda3/2019.03

# Set the ${PS1} (needed in the source of the Anaconda environment)
export PS1=\$

# Source the conda environment setup
# The variable ${EBROOTANACONDA3} or ${EBROOTMINICONDA3}
# So use one of the following lines
# comes with the module load command
# source ${EBROOTANACONDA3}/etc/profile.d/conda.sh
source ${EBROOTMINICONDA3}/etc/profile.d/conda.sh

# Deactivate any spill-over environment from the login node
conda deactivate &>/dev/null

# Activate the environment by using the full path (not name)
# to the environment. The full path is listed if you do
# conda info --envs at the command prompt.
conda activate PATH_TO_ENVIRONMENT

# Execute the python program
python pdexample.py

Additional Conda information¶

Cheatsheet and built-in help¶

See this cheatsheet for an overview over the most important conda commands.

In case you get confused by the conda commands and command line options you can get help by adding --help to any conda command or have a look at the conda documentation.

Miniconda vs. Anaconda¶

Both Miniconda and Anaconda are distributions of the conda repository management system. But while Miniconda brings just the management system (the conda command), Anaconda comes with a lot of built-in packages.

Both are installed on Stallo but we advise the use of Miniconda. By explicitly installing packages into your own environment the chance for unwanted effects and errors due to wrong or incompatible versions is reduced. Also you can be sure that everything that happens with your setup is controlled by yourself.