Interactive Mode

Using Conda and Slurm

Interactive jobs

All the difficult work is behind us. If we want to use our container interactively, we'll just use all the shortcuts we created.

1. Start an interactive job on the login node

$ ssh klone-login

[UWNetID@klone-login01 ~]$ salloc --account=uwit --partition=ckpt \

--cpus-per-task=1 --mem=16G \

--job-name=klone-container

salloc: Pending job allocation 1234567

salloc: job 1234567 queued and waiting for resources

salloc: job 1234567 has been allocated resources

salloc: Granted job allocation 1234567

salloc: Waiting for resource configuration

salloc: Nodes n3120 are ready for job

[UWNetID@n3120 ~]$

First, we'll request an interactive job in the checkpoint partition, with a single CPU and 16GB of memory. The most important part, if you're going to connect directly to the node, is that you need to name the job with --job-name=klone-container so that our node-finding script works properly.

2. Get into our container

[UWNetID@n3120 ~]$ ~/launch-container.sh

Apptainer>

We automated this step, too. Now we're in our container, attached to a read-write overlay filesystem.

3. Run Conda and Slurm commands

Apptainer> conda

usage: conda [-h] [-V] command ...

# Truncated the remainder of the conda output

Apptainer> squeue -u $USER

JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)

1234567 ckpt klone-co uwnetid R 10:00 1 n3120

Apptainer>

And that's all there is to it. Before we move on to non-interactive jobs, here's the background on Slurm compatibility:

What's required for Slurm?

Running Slurm in any container requires the following:

1. The same version of Slurm running on the node (which we installed from the Hyak repository).
2. The same user ID and group ID for the Slurm user as on the node (which we copied during the container build).
3. Three bind-mounts to node filesystems, all of which are included in the compute node's default Apptainer configuration:
   • /var/run/munge
   • /var/run/slurm
   • /var/spool/slurmd

Non-interactive jobs

Running non-interactive jobs is a little more complex, since we'll need to pass a script to our container. Let's say you've written a bit of code that uses one of the conda environments in your overlay: we'll call it ~/do-some-research.py. We'll start by writing a Bash script to get into the conda environment & run the script:

~/start-research.sh

#!/bin/bash

. ~/.bashrc

conda activate ResearchEnvironment

python3 ~/do-some-research.py

Don't forget to make this script executable:

[UWNetID@klone-login01 ~]$ chmod +x ~/start-research.sh

Now we'll make an SBATCH script, where we pass this script to our container:

~/research.job

#!/bin/bash

#SBATCH --job-name=research

#SBATCH --cpus-per-task=8

#SBATCH --mem=64GB

#SBATCH --account=uwit

#SBATCH --partition=compute

#SBATCH --time=8:00:00

~/launch-container-ro.sh ~/start-research.sh

This will start a job named 'research' with 8 CPUs, 64GB of RAM, and a time limit of 8 hours. Don't forget to change the account or parition. This tells our container (with our conda overlay in read-only) to run the ~/start-research.sh wrapper for our ~/do-some-research.py Python script. All that's left is to submit the job with sbatch ~/research.job and wait for the results.