Python
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):
This will load and activate the base environment.
Tip
We encourage users to use the pre-installed conda environment. Any custom environments are supported on a best-effort basis only.
For Python issues or questions, please see the Contacting Support page.
Virtual environments via venv
To install additional packages that are missing from the base environment, we can build a venv on top of it.
Conda base environment + venv
If you need a package that is not already installed in the base environment, this is generally the recommended approach.
We can create a venv on top of the base Anaconda environment (with --system-site-packages to inherit the base packages):
module use /soft/modulefiles; module load conda; conda activate base
CONDA_NAME=$(echo ${CONDA_PREFIX} | tr '\/' '\t' | sed -E 's/mconda3|\/base//g' | awk '{print $NF}')
VENV_DIR="$(pwd)/venvs/${CONDA_NAME}"
mkdir -p "${VENV_DIR}"
python -m venv "${VENV_DIR}" --system-site-packages
source "${VENV_DIR}/bin/activate"
You can always retroactively change the --system-site-packages flag state for this virtual environment by editing ${VENV_DIR}/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:
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
Warning
This approach is generally not recommended as it can be quite slow and can use significant storage space.
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.
To clone the base environment:
module load conda; conda activate base
conda create --clone base --prefix /path/to/envs/base-clone
conda activate /path/to/envs/base-clone
where /path/to/envs/base-clone should be replaced by a suitable path. The cloning process can be quite slow.
Using pip install --user
Danger
This is typically not recommended.
With the conda environment setup, one can install common Python modules using python3 -m pip install --user '<module-name>', which will install packages in $PYTHONUSERBASE/lib/pythonX.Y/site-packages.
The $PYTHONUSERBASE environment variable is automatically set when you load the base conda module and is equal to /home/$USER/.local/polaris/conda/YYYY-MM-DD.
Note, Python modules installed this way that contain command line binaries will not have those binaries automatically added to the shell's $PATH. To manually add the path:
Be sure to remove this location from $PATH if you deactivate the base Anaconda environment or unload the module.
Cloning the Anaconda environment or using venv are both more flexible and transparent when compared to --user installs.
Existing issue and solution
There is an issue with the current conda environment. One may encounter the following error message:
To address this, please add the following line at the very beginning of your Python script.
Creating a Jupyter Kernel
If you need to use your Python venv on JupyterHub, you will need to create a custom Jupyter kernel for it.