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Argonne Leadership
Computing Facility



We provide prebuilt conda environments containing GPU-supported builds of torch, tensorflow (both with horovod support for multi-node calculations), jax, and many other commonly-used Python modules.

Users can activate this environment by first loading the conda module, and then activating the base environment.

Explicitly (either from an interactive job, or inside a job script):

$ module load conda
$ conda activate base
(base) $ which python3
In one line, module load conda; conda activate. This can be performed on a compute node, as well as a login node.

As of writing, the latest conda module on Polaris is built on Miniconda3 version 4.13.0 and contains Python 3.8.13. Future modules may contain entirely different major versions of Python, PyTorch, TensorFlow, etc.; however, the existing modules will be maintained as-is as long as feasible.

While the shared Anaconda environment encapsulated in the module contains many of the most commonly used Python libraries for our users, you may still encounter a scenario in which you need to extend the functionality of the environment (i.e. install additional packages)

There are two different approaches that are currently recommended.

Virtual environments via venv

Creating your own (empty) virtual Python environment in a directory that is writable to you is simple:

python3 -m venv /path/to/new/virtual/environment
This creates a new folder that is fairly lightweight folder (<20 MB) with its own Python interpreter where you can install whatever packages you'd like. First, you must activate the virtual environment to make this Python interpreter the default interpreter in your shell session.

You activate the new environment whenever you want to start using it via running the activate script in that folder:


In many cases, you do not want an empty virtual environment, but instead want to start from the conda base environment's installed packages, only adding and/or changing a few modules.

To extend the base Anaconda environment with venv (e.g. my_env in the current directory) and inherit the base enviroment packages, one can use the --system-site-packages flag:

module load conda; conda activate
python -m venv --system-site-packages my_env
source my_env/bin/activate
# Install additional packages here...
You can always retroactively change the --system-site-packages flag state for this virtual environment by editing my_env/pyvenv.cfg and changing the value of the line include-system-site-packages = false.

To install a different version of a package that is already installed in the base environment, you can use:

pip install --ignore-installed  ... # or -I
The shared base environment is not writable, so it is impossible to remove or uninstall packages from it. The packages installed with the above pip command should shadow those installed in the base environment.

Cloning the base Anaconda environment

If you need more flexibility, you can clone the conda environment into a custom path, which would then allow for root-like installations via conda install <module> or pip install <module>. Unlike the venv approach, using a cloned Anaconda environment requires you to copy the entirety of the base environment, which can use significant storage space.

This can be performed by:

$ module load conda
$ conda activate base
(base) $ conda create --clone base --prefix /path/to/envs/base-clone
(base) $ conda activate /path/to/envs/base-clone
(base-clone) $ which python3
The cloning process can be quite slow.


In the above commands, path/to/envs/base-clone should be replaced by a suitably chosen path.