https://github.com/engineerdanny/ml_with_monsoon

Last synced: 6 months ago
JSON representation

Host: GitHub
URL: https://github.com/engineerdanny/ml_with_monsoon
Owner: EngineerDanny
Created: 2023-11-18T07:57:34.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-03-02T23:21:56.000Z (about 2 years ago)
Last Synced: 2025-09-02T22:41:27.592Z (8 months ago)
Language: Jupyter Notebook
Size: 3.59 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# 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](https://ondemand.hpc.nau.edu/pun/sys/dashboard/)
- Access through the secure shell (ssh). On your terminal, run below and type your password:
```bash
ssh -Y @monsoon.hpc.nau.edu
```
## Interactive/Debug Work
- Request a compute node with 4GB of RAM and 1 cpu for 24 hours
```bash
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
```bash
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:
```bash
#!/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:
```bash
sbatch jobscript.sh
```

## [Time Graph](https://github.com/EngineerDanny/ml_with_monsoon/tree/main/code/job_arrays_intermediate)
time_graph