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Interactive jobs

Interactive jobs are the most intuitive way to use RCC clusters, as they allow you to interact with the program running on compute node/s (e.g., execute cells in a Jupyter Notebook) in real-time. This is great for exploratory work or troubleshooting. An interactive job will persist until you disconnect from the compute node or until you reach the maximum requested time.

To request an interactive job with default parameters, run the following command while connected to a login node: 

sinteractive -A [pi-account] -p [partition] -N [# of nodes] --ntasks-per-node=[# of tasks] --time=[time]

Note

On Midway3, you always need to specify the account to be charged for the job explicitly. Slurm will use the default partition (Midway2: broadwl, Midway3: caslake) if you do not specify it.

Note

On Midway3, to use the partitions with AMD CPUs, it is recommended that you log in to the midway3-amd.rcc.uchicago.edu login node and submit jobs from this login node.

Note

By default, an interactive session times out after 2 hours. If you would like more than 2 hours, include a --time=HH:MM:SS flag to specify the necessary amount of time.

As soon as the requested resources become available, sinteractive will do the following:

  1. Log in to the compute node/s in the requested partition.
  2. Change into the directory you were working in.
  3. Set up X11 forwarding for displaying graphics.
  4. Transfer your shell environment, including any previously loaded modules.

There are many additional options for the sinteractive command, including options to select the number of nodes, the number of cores per node, the amount of memory, and so on. For example, to request exclusive use of two compute nodes on the default CPU partition for 8 hours, enter the following:

sinteractive --exclusive --partition=broadwl --nodes=2 --time=08:00:00

sinteractive --account=pi-<PI's CNETID> --exclusive --partition=caslake --nodes=2 --time=08:00:00

For more details about these and other useful parameters, check Slurm sbatch page.

Tip

All options in the sbatch command are also available for the sinteractive command.

Debug QoS

There is a debug QOS (Quality of Service) setup to help users quickly access some resources to debug or test their code before submitting their jobs to the main partition. The debug QoS will allow you to run one job and get up to four cores for 15 minutes without consuming SUs. To use the debug QoS, specify --time as 15 minutes or less. For example, to get two cores for 15 minutes, you could run:

sinteractive --qos=debug --time=00:15:00 --ntasks=2 --account=pi-<PI CNETID>
You can find out the available qos for your account with the command rcchelp

rcchelp qos

Additionally, it is important to use the --mem or --mem-per-cpu options. For example, to request 8 CPU cores and 128 GB of memory on a bigmem2 node, add the following to your .sbatch script:

#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=128G

These same options can also be used to set up a sinteractive session. For example, on Midway3 to access a bigmem node with 1 CPU and 128 GB of memory, run: 

sinteractive --partition=bigmem --ntasks=1 --cpus-per-task=8 --mem=128G --account=pi-<PI CNETID>

Once you are connected to the compute node where the interactive session is runnnig, you can load python: 

module load python

and run python

[your-cnetid@midway3-0xyz ] python
Python 3.9.19 | packaged by conda-forge | (main, Mar 20 2024, 12:50:21) 
[GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

After quitting python, you can activate the environment you created, and list the installed packages

source /project/[pi-folder]/[your-cnetid]/my-venv/bin/activate
pip list
You should see numpy, matplotlib and pandas installed in this environment.

Let us prepare a simple Python script, namely, simple.py with a text editor like nano, in the current folder, with the following content:

import numpy as np
from matplotlib import pyplot as plt 
x = np.arange(1,11) 
y = 2 * x + 5 
plt.title("Matplotlib demo") 
plt.xlabel("x axis caption") 
plt.ylabel("y axis caption") 
plt.plot(x,y)
plt.savefig("output.pdf", format="pdf")
And run it with 

python simple.py

which should produce a file output.pdf.

Change to the input data folder 

cd /project/[pi-folder]/[your-cnetid]/lammps/examples

Load the same modules you used to build LAMMPS (except cmake)

module load mpich/3.4.3+gcc-10.2.0 mkl/2023.1

Relax the locked memory limit 

ulimit -l unlimited

Run the LAMMPS binary you built above 

mpirun -np 8 /project/[pi-folder]/[your-cnetid]/lammps/build/lmp -in melt/in.melt

The run will produce the LAMMPS screen output and a file named log.lammps in the current folder.

To terminate the interactive session, run

exit

You will get back to the login node.