Working with Python

The tutorial assumes you have already worked through the Execute a Job Tutorial. Therefore, the instructions here are abbreviated but will follow the same format so you may easily consult the extended tutorial.

Table of Contents

📝 Note: Do not execute jobs on the login nodes; only use the login nodes to access your compute nodes. Processor-intensive, memory-intensive, or otherwise disruptive processes running on login nodes will be killed without warning.

Step 1: Access the Onyx HPC

  1. Open a Bash terminal (or MobaXterm for Windows users).

  2. Execute ssh doaneusername@onyx.doane.edu.

  3. When prompted, enter your password.

Step 2: Create an sbatch Script

Example sbatch Script

Here is an example sbatch script for running a batch job on an HPC like Onyx.

#!/bin/bash

#SBATCH -n 16
#SBATCH -o test_%A.out
#SBATCH --error test_%A.err
#SBATCH --mail-user $CHANGE_TO_YOUR_EMAIL
#SBATCH --mail-type ALL

module purge
module load gnu/5.4.0
module load spack-apps
module load openmpi-3.0.0-gcc-5.4.0-clbdgmf
module load python-3.6.3-gcc-5.4.0-ctlzpuv
module load py-mpi4py-3.0.0-gcc-5.4.0-wh6rtv7
module list
mpirun python hello_world.py

sbatch Procedure

  1. Use nano or Vim (we use Vim here) to create and edit your sbatch script.

    vim slurm_py_example.job

  2. Create your sbatch script within Vim by typing i for insert mode or paste the contents of your sbatch script into Vim.

  3. Save your file by typing :wq! and return to the Bash shell.

Step 3: Create a Python Program

MPI Hello World Code

#!/usr/bin/env python

import sys
from mpi4py import MPI

size = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
name = MPI.Get_processor_name()

print("Hello, World! I am process ",rank," of ",size," on ",name)

Python Procedure

  1. Use Vim (vim) to create your python source file within your working directory.

    vim hello_world.py

  2. Paste the hello world python code into Vi.

  3. Save your file and return to the Bash shell.

Python does not need to be compiled.

Step 4: Run the Job

  1. Before proceeding, ensure that you are still in your working directory (using pwd) and that you still have the openmpi module loaded (using module list).

    • We need to be in the same path/directory as our sbatch script and our python script. Use ls -al to confirm their presence.

  2. Use sbatch to schedule your batch job in the queue.

    sbatch slurm_py_example.job

    This command will automatically queue your job using slurm and produce a job number. You can check the status of your job at any time with the squeue command.

    squeue --job <jobnumber>

    You can also stop your job at any time with the scancel command.

    scancel --job <jobnumber>

  3. View your results. You can view the contents of these files using the less command followed by the file name. 

    less test_<jobnumber>.out

    Your output should look something like this (the output is truncated.):

    Hello, World! I am process  11  of  20  on  compute_node
Hello, World! I am process  13  of  20  on  compute_node
Hello, World! I am process  12  of  20  on  compute_node
Hello, World! I am process  0   of  20  on  compute_node
Hello, World! I am process  15  of  20  on  compute_node
Hello, World! I am process  19  of  20  on  compute_node
Hello, World! I am process  7   of  20  on  compute_node
Hello, World! I am process  16  of  20  on  compute_node
Hello, World! I am process  3   of  20  on  compute_node
Hello, World! I am process  9   of  20  on  compute_node
.
.
.

  4. Download your results (using the scp command or an SFTP client) or move them to persistent storage. See our moving data section for help.

Additional Examples

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