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.
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-packaes to inherit
the base packaes):
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 suitably chosen
path.
Note: The cloning process can be quite slow.
Using pip install --user (not recommended)
Danger
This is typically not recommended.
With the conda environment setup, one can install common Python modules using
python3 pip install --users '<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 addresss this, please add the following line in the very beginning of your python script.