While our Python installations come with many popular packages installed, you may come upon a case in which you need an additional package that is not installed. If the specific package you are looking for is available from anaconda.org (formerly binstar.org), you can easily install it and required dependencies by using the conda package manager.

Procedure

The following steps are an example of how to set up a Python environment and install packages to a local directory using conda. We use the name  local for the environment, but you may use any other name.

Load proper Python module

We have python and Miniconda3 modules. python and miniconda3 module is based on Conda package manager. python modules are typically recommended when you use Python in a standard environment that we provide. However, if you want to create your own python environment, we recommend using miniconda3 module, since you can start with minimal configurations.

module load miniconda3/24.1.2-py310

Configure Conda (first time use)

The first time you use conda, it is recommend to configure it to use the desired channels and options.  A number of channels exist with different packages and licensing requirements.  While academic users are generally unrestricted, commercial users may be subject to terms of service requiring license purchasing.  Commercial users are encouraged to check with their organization regarding licensing.  Please see Anaconda, Inc. Terms of Service for details.

To avoid using proprietary packages from the defaults channel, users can remove it:

conda config --remove channels defaults

and add the alternative conda-forge channel instead:

conda config --add channels conda-forge

OSC recommends setting strict channel priority:

conda config --set channel_priority strict

If strict channel priority makes required dependencies unavailable, it can be disabled:

conda config --set channel_priority flexible

Create Python installation to local directory

Three alternative create commands are listed. These cover the most common cases.

CREATE NEW ENVIRONMENT

The following will create a minimal Python installation without any extraneous packages:

conda create -n local

CLONE BASE ENVIRONMENT

If you want to clone the full base Python environment from the system, you may use the following create command:

conda create -n local --clone base

CREATE NEW ENVIRONMENT WITH SPECIFIC PACKAGES

You can augment the command above by listing specific packages you would like installed into the environment. For example, the following will create a minimal Python installation with only the specified packages (in this case, numpy and babel):

conda create -n local numpy babel

By default, conda will install the newest versions of the packages it can find. Specific versions can be specified by adding =<version> after the package name. For example, the following will create a Python installation with Python version 2.7 and NumPy version 1.16:

conda create -n local python=2.7 numpy=1.16

CREATE NEW ENVIRONMENT WITH A SPECIFIC location

By default, conda will create the environment in your home location $HOME. To specify a location where the local environment is created, for example, in the project space /fs/ess/ProjectID, you can use the following command:

conda create --prefix /fs/ess/ProjectID/local

To activate the environment, use the command:

source activate /fs/ess/ProjectID/local

To verify that a clone has been created, use the command

conda info -e

For additional conda command documentation see https://docs.conda.io/projects/conda/en/latest/commands.html#conda-general-commands

Activate environment

For the bash shell:

source activate local

At the end of the conda create step, you may saw a message from the installer that you can use conda activate command for activating environment. But, please don't use conda activate command, because it will try to update your shell configuration file and it may cause other issues. So, please use source activate command as we suggest above.

On newer versions of Anaconda on the Pitzer cluster you may also need to perform the removal of the following packages before trying to install your specific packages:

conda remove conda-build
conda remove conda-env

Install packages

To install additional packages, use the conda install command. For example, to install the yt package:

conda install yt

By default, conda will install the newest version if the package that it can find. Specific versions can be specified by adding =<version> after the package name. For example, to install version 1.16 of the NumPy package:

conda install numpy=1.16

If you need to install packages with pip, then you can install pip in your virtual environment by

conda install pip

Then, you can install packages with pip as

pip install PACKAGE

Please make sure that you have installed pip in your environment not using one from the miniconda module. The pip from the miniconda module will give access to the packages from the module to your environment which may or may not be desired. Also set export PYTHONNOUSERSITE=True to prevent packages from user's .local path.

Test Python package

Now we will test our installed Python package by loading it in Python and checking its location to ensure we are using the correct version. For example, to test that NumPy is installed correctly, run

python -c "from __future__ import print_function; import numpy; print(numpy.__file__)"

and verify that the output generally matches

$HOME/.conda/envs/local/lib/python3.6/site-packages/numpy/__init__.py

To test installations of other packages, replace all instances of numpy with the name of the package you installed.

Remember, you will need to load the proper version of Python before you go to use your newly installed package. Packages are only installed to one version of Python.

Install your own Python packages

If the method using conda above is not working, or if you prefer, you can consider installing Python packages from the source. Please read HOWTO: install your own Python packages.

But I use virtualenv and/or pip!

See the comparison to these package management tools here:

https://docs.conda.io/projects/conda/en/latest/commands.html#conda-vs-pip-vs-virtualenv-commands

Use pip only without conda package manager

pip installations are supported:

module load python
module list                            # check which python you just loaded
pip install --user --upgrade PACKAGE   # where PACKAGE is a valid package name

Note the default installation prefix is set to the system path where OSC users cannot install the package. With the option --user, the prefix is set to $HOME/.local where lib, bin, and other top-level folders for the installed packages are placed. Finally, the option --upgrade will upgrade the existing packages to the newest available version.

The one issue with this approach is portability with multiple Python modules. If you plan to stick with a single Python module, then this should not be an issue. However, if you commonly switch between different Python versions, then be aware of the potential trouble in using the same installation location for all Python versions.

Use pip in a Python virtual environment (Python 3 only)

Typically, you can install packages with the methods shown in Install packages section above, but in some cases where the conda package installations have no source from conda channels or have dependency issues, you may consider using pip in an isolated Python virtual environment. 

To create an isolated virtual environment:

module reset
python3 -m venv --without-pip $HOME/venv/mytest --prompt "local"
source $HOME/venv/mytest/bin/activate
(local) curl https://bootstrap.pypa.io/get-pip.py |python     # get the newest version of pip
(local) deactivate

where we use the path $HOME/venv/mytest and the name local for the environment, but you may use any other path and name. 

To activate and deactivate the virtual environment:

source $HOME/venv/mytest/bin/activate
(local) deactivate 

To install packages:

source $HOME/venv/mytest/bin/activate
(local) pip install PACKAGE 

You don't need the --user option within the virtual environment.  

Further Reading

Conda Test Drive: https://conda.io/docs/test-drive.html