This article will guide you to run the Python script using Slurm script on the LANTA HPC system. An overview of the content can be found in the table of contents below for immediate visualization of the interesting parts.
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Slurm script example for running the Python script
Running on Compute node
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#!/bin/bash
#SBATCH -p compute # Specify partition [Compute/Memory/GPU]
#SBATCH -N 1 -c 128 # Specify number of nodes and processors per task
#SBATCH --ntasks-per-node=1 # Specify tasks per node
#SBATCH -t 120:00:00 # Specify maximum time limit (hour: minute: second)
#SBATCH -A ltxxxxxx # Specify project name
#SBATCH -J JOBNAME # Specify job name
module purge load Apptainer/1.1.6 # Unload all modules module load Miniconda3/22.11.1-1 # Load the Miniconda3Apptainer module
condaapptainer activateexec tensorflow-2.6.0 # Activate your environment
-B $PWD:$PWD file.sif python3 file.py # Run your program or executable code |
| Info |
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Full node: -c 128, Half node: -c 64, ΒΌ node: -c 32 |
...
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#!/bin/bash
#SBATCH -p gpu # Specify partition [Compute/Memory/GPU]
#SBATCH -N 1 -c 16 # Specify number of nodes and processors per task
#SBATCH --gpus-per-task=1 # Specify number of GPU per task
#SBATCH --ntasks-per-node=4 # Specify tasks per node
#SBATCH -t 120:00:00 # Specify maximum time limit (hour: minute: second)
#SBATCH -A ltxxxxxx # Specify project name
#SBATCH -J JOBNAME # Specify job name
module purge load Apptainer/1.1.6 # Unload all modules module load Miniconda3/22.11.1-1 # Load the Miniconda3Apptainer module
condaapptainer activateexec tensorflow-2.6.0 # Activate your environment
--nv -B $PWD:$PWD file.sif python3 file.py # Run your program or executable code |
| Info |
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1 GPU card: --ntasks-per-node=1, 2 GPU cards: --ntasks-per-node=2, 4 GPU cards: --ntasks-per-node=4 |
Submit a job
Use the sbatch script.sh command to submit your job to the Slurm system.
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username@lanta:~> sbatch script.sh |
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