Docker build combining R and Python

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.

How to use the Singularity container for interactive data analysis in R and Python

Steps on the remote machine (for example, Fred Hutch rhino cluster)

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.

• Make Singularity available:

module load Singularity

• Pull the Singularity container from the Subramaniam lab GitHub Packages Repo:

(This step is not necessary if you use Rasi's Singularity image at the location below)

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:

cd /fh/scratch/delete90/subramaniam_a/user/rasi/singularity/
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.

Steps on the local machine (for example, your lab desktop computer)

• 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.