Machine Learning with Monsoon (NAUs Supercomputer)

This repository shows the code to explain some of the basic concepts you will need in your machine learning workflow using monsoon.

Connecting to Monsoon

• Access through the monsoon dashboard
• Access through the secure shell (ssh). On your terminal, run below and type your password:

ssh -Y <username>@monsoon.hpc.nau.edu

Interactive/Debug Work

• Request a compute node with 4GB of RAM and 1 cpu for 24 hours

srun -t 24:00:00 --mem=4GB --cpus-per-task=1 --pty bash

• Request a compute node with 8GB of RAM and 1 cpu for 1 hour

srun -t 1:00:00 --mem=8GB --cpus-per-task=1 python analysis.py

Submitting Jobs

You can also write your program and submit a job shell script for you to be placed in the queue. An example job script (jobscript.sh) is:

#!/bin/bash
#SBATCH --job-name=test
#SBATCH --output=/scratch/da2343/output.txt #SBATCH --error=/scratch/da2343/error.txt #SBATCH --time=20:00
#SBATCH --mem=1GB
#SBATCH --cpus-per-task=1
python analysis.py

Submit the job script using:

sbatch jobscript.sh

Time Graph

Algorithm Selection

Hyper-Parameter Tuning