How to Submit Parallel Python SLURM Jobs with Arguments in a For Loop Using tcsh

Discover a step-by-step guide on submitting parallel Python SLURM jobs with arguments in a for loop using tcsh. Learn how to manage job dependencies and leverage SLURM effectively.
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How to Submit Parallel Python SLURM Jobs with Arguments in a For Loop Using tcsh

Managing job submissions in a high-performance computing (HPC) environment can be challenging, especially when dealing with job dependencies and executing multiple jobs in parallel. If you're using tcsh as your shell and want to run multiple versions of a Python script in SLURM, this guide will help you understand how to configure your job submissions effectively.

The Problem

You have a Python script that requires you to run multiple instances with slight variations in parameters. More specifically, you want to:

Execute four different versions of your script, each with a unique variable.

Use the output of previous jobs as input for subsequent jobs.

All of this must be accomplished via tcsh, as your Python script relies on certain environment variables that are loaded via your .cshrc configuration file.

The traditional method you've used involves submitting each job individually. However, you want to leverage SLURM's capabilities to run these jobs in parallel while managing their dependencies efficiently.

Understanding the Solution

You found that the srun command is commonly used to execute processes in parallel but remained uncertain about its integration in a tcsh script. Fortunately, the solution lies in combining tcsh with a Bash script to facilitate parallel execution of your Python job.

Finalizing the SLURM Script

After some exploration, you arrived at a workable solution using a Bash script invoked from tcsh. Here’s how you can structure your SLURM script for managing dependencies and running jobs in parallel.

Bash Script (slurm_script.sh)

[[See Video to Reveal this Text or Code Snippet]]

Key Components Explained

Job Name and Output: The -SBATCH directives at the start of your script define the job name and the output file for logging results.

Resource Allocation: Adjust the --ntasks, --cpus-per-task, and --mem to allocate enough resources based on the needs of your script.

Running Jobs in Parallel: The outer loop iterates through different values of i, while the inner loop runs your Python script with j variations. Each instance runs in the background (&), and wait ensures the outer loop waits for all inner jobs to complete before starting the next iteration.

Important Considerations

Job Output: The output files will be named based on the loop parameters, making it easy to track results for each job.

Syntax Notes: When defining variables in Bash, adhere to the syntax i=1 (no spaces around the equal sign).

Submitting Your Job

Finally, you can submit the Bash script using the sbatch command in tcsh:

[[See Video to Reveal this Text or Code Snippet]]

This command sends your job to the SLURM scheduler where it will be processed according to the defined resource allocation and job dependencies.

Conclusion

By structuring your job submission in this manner, you can efficiently manage parallel executions of your Python script, leveraging SLURM while staying within the tcsh environment. This method not only streamlines your workflow but also ensures that dependencies between jobs are correctly handled, providing a robust solution for high-performance computing tasks.

For advanced scheduling needs, you might explore further options within SLURM to customize job dependencies or manage more complex workflows. Happy coding!