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Argonne Leadership Computing Facility

Running PyTorch with Conda

Be aware that these builds use CUDA and will not work on login nodes, which do not have CUDA installed as there are no GPUs.

One can test these software packages in an interactive session: 

qsub -I -q single-gpu -n 1 -t 30 -A <project-name> --attrs filesystems=<list of filesystems>

PyTorch (master build)

Users can find the latest builds via the module avail conda command, which will list available builds such as conda/2021-06-26 which is a module that was built on 2021-06-26. Use module show conda/2021-06-26 or module help conda/2021-06-26 to get high level info on which versions of the key packages and libraries that this particular module contains.

This version can be used by: 

module load conda/2021-06-26 # loads conda into your environment, sets up appropriate CUDA libraries and environment variables
conda activate # add entries to PATH for the environment and run any activation scripts that the environment may contain

This will setup a conda environment with the "from scratch" build of PyTorch.

This package will also include builds of TensorFlow and Horovod tagged releases.

Installing Packages

With the conda environment setup, one can install common Python modules using pip install --users <module-name> which will install packages in $PYTHONUSERBASE/lib/pythonX.Y/site-packages. The $PYTHONUSERBASE environment variable is automatically set when you load the base conda module, and is typically equal to /home/$USER/.local/conda/YYYY-MM-DD or /home/$USER/.local/thetagpu/conda/YYYY-MM-DD, depending on the date of the module.

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: 

export PATH=$PYTHONUSERBASE/bin:$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.

Using Conda Environments

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>.

  1. Setup the conda environment you want to use as instructed above.
  2. Create/edit your $HOME/.condarc file to include this these lines, replacing <project-name> with your project name. <path-to-your-project> is the path to the file system your project is on (e.g. /lus/theta-fs0 or /grandor /eagle). By default, Conda will your $HOME/.conda/* area for caching files.

Note: Since home directories are limited to 100GB, this fills up quickly. This addition tells Conda to use your project space for cache storage instead.

pkgs_dirs: 
  - <path-to-your-project>/<project-name>/conda/pkgs 
envs_dirs: 
  - <path-to-your-project>/<project-name>/conda/envs
  1. Clone the environment into a local path to which you have write access 
    conda create --clone $CONDA_PREFIX -p <path/to/env>
  2. Activate that environment: 
    conda activate <path/to/env>

One should then be able to install modules natively.