Anaconda

Python is a high level programming language that is widely used in many branches of science. The scientific python ecosystem is available to researchers as Anaconda modules on Cheaha. Modules for both python 2 and python 3 are installed. In order to see the different versions of each, use:

module spider Anaconda

Loading Anaconda

When planning a project, you should have an idea of which python version you need to use. Python 3 is the current standard and is used by the Anaconda3 modules. After loading one of the modules, use python --version to check the version number.

Anaconda modules and their corresponding python versions can be seen in the table below:

Python Versions

Module	Python Version
Anaconda2/4.0.0	2.7.11
Anaconda2/4.2.0	2.7.12
Anaconda3/4.4.0	3.6.1
Anaconda3/5.0.1	3.6.3
Anaconda3/5.1.0	3.6.4
Anaconda3/5.2.0	3.6.5
Anaconda3/5.3.0	3.7.2
Anaconda3/5.3.1	3.7.4
Anaconda3/2019.10	3.7.4
Anaconda3/2020.02	3.7.6
Anaconda3/2020.07	3.8.3
Anaconda3/2020.11	3.8.5

If a necessary version is not required, choose the most recent version, Anaconda3/2020.11. Alternatively, the necessary python version can specified when creating a virtual environment and will be downloaded and installed regardless of if it is currently installed on the cluster.

Libraries and Virtual Environments

Anaconda virtual environments are self-contained environments with necessary packages for specific projects. It is recommended to have a separate environment for each project you have. This solves cases where different projects have dependencies on different versions of the same package.

New virtual environments include a few very common libraries such as scikit-learn, pandas, numpy, and scipy by default. However, most projects will need to install some external libraries as well using pip or conda.

Here, we will go through instructions for creating and managing Anaconda environments including installing new libraries. More complete information on this process can be found at the Anaconda documentation.

Create an Environment

In order to create a basic environment with the default packages, use the conda create command:

# create a base environment. Replace <env> with an environment name
conda create -n <env>

If you are trying to replicate a pipeline or analysis from another person, you can also recreate an environment using a YAML file, if they have provided one. To replicate an environment using a YAML file, use:

# replicate an environment from a YAML file named env.yml
conda create -n <env> -f <path/to/env.yml>

By default, all of your conda environments are stored in /home/<user>/.conda/envs.

Activate an Environment

From here, you can activate the environment using either source or conda:

# activate the virtual environment using source
source activate <env>

# or using conda
conda activate <env>

To know your environment has loaded, the command line should look like:

(<env>) [blazerid@c0XXX ~]$

Once the environment is activated, you are allowed to install whichever python libraries you need for your analysis.

Install Libraries

The base package manager for python is pip. The basic way to use pip is (replace <package> with the package name, omitting <>):

# install most recent version of a package
pip install <package>

# install a specific version
pip install <package>==version

# install a list of pacakges from a text file
pip install -r packages.txt

pip searches various package indexes like PyPi or local project directories. If the package you need isn’t found there, it may be available in an online Anaconda channel (same as index). To install from there, use the conda install command.

# install most recent version of a package
conda install <package>

# install a specific version
conda install <package>=version

# install from a specific conda channel
conda install -c <channel> <package>

Generally, if a package needs to be downloaded from a specific conda channel, it will mention that in its installation instructions.

Running Command-Line Python

Python code can be run an individual commands from the command line. In order to access a python terminal, use the python or python3 command in the terminal window. The prompt will be replaced with >>>. Execute any commands here. exit() will return you to the normal command line.

Executing scripts is the more common use case than executing individual commands interactively. To execute a script from the command line:

python <script.py>

Any optional inputs the script has can be listed after the name of the script.

Note

When Anaconda3 is loaded in your environment, the python and python3 commands both refer to Python version 3.X.X (whatever minor version is loaded). However, when Anaconda3 is not loaded, python will refer to the base Python 2.7.5 instead. Be sure to load Anaconda3 before running python, or always use python3 for disambiguation.

Deactivating an Environment

An environment can be deactivated using either source or conda:

# Using source
source deactivate

# Using conda
conda deactivate

Anaconda may say that using source deactivate is deprecated, but environment will still be deactivated.

Closing the terminal will also close out the environment.

Exporting an Environment

To easily share environments with other researchers or replicate it on a new machine, it is useful to create an environment YAML file. You can do this using:

# activate the environment if it is not active already
conda activate <env>

# export the environment to a YAML file
conda env export > env.yml