This Docker build combines the Subramaniam Lab R and Python environments built in https://github.com/rasilab/python/pkgs/container/r and https://github.com/rasilab/python/pkgs/container/python. See Dockerfile for build details.

We use this build for interactive analysis inside a Singularity container started on the Fred Hutch cluster.

Use the separate R and Python containers for Snakemake workflows.

The image version corresponds to the R and Python image version numbers.

Do the remote operations below from within a tmux session so that you can detach and logout of your remote session and still keep the container running.

• Pull the Singularity container from the Subramaniam lab GitHub Packages Repo (You don't have to do this if you use Rasi's Singularity image at the location below):

module load Singularity
cd /fh/scratch/delete90/subramaniam_a/user/rasi/singularity/
singularity pull --name r_python:1.1.0.simg docker://ghcr.io/rasilab/r_python:1.1.0

• Make sure that any conda initialization is commented out in your .bashrc or .bash_profile file on the remote machine. This step is important. Otherwise, VScode will not recognize the conda environments within the Singularity container.

• Start an interactive Singularity container using the above image while mounting the cluster filesystem:

singularity exec -B /fh r_python\:1.1.0.simg /bin/bash

• Start a VScode CLI tunnel from within the container (Download the VScode CLI if necessary):

./code tunnel

• If you are doing the above the first time, you will have to login to GitHub using the displayed code and also name the tunnel.

• Install Remote Tunnels extension on your local machine.

• Use the Remote Tunnels: Connect to Tunnel command to connect to the tunnel you created and named above.

• You can open any folder on the remote machine and create a Jupyter notebook.

• You should be able to pick the Python interpreter at /opt/conda/bin/python or the Jupyter R kernel at /opt/conda/envs/R/lib/R/bin/R to run your Python or R notebook.