Maximize Your Processing Power: Using Slurm sbatch with Python Multiprocessing

Master the art of using Slurm sbatch with Python's multiprocessing to optimize CPU usage effectively!
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How to Properly Use Slurm sbatch and Python Multiprocessing

When working with computational tasks on a server that utilizes the Slurm workload manager, it's essential to efficiently manage CPU resources. If you've ever found yourself confused about how to restrict the number of CPUs when using Python's multiprocessing library, you're not alone! This post will guide you through the process of leveraging Slurm's capabilities alongside Python to ensure you get the most out of your computing resources.

The Problem

You have a code that you want to run using Python's multiprocessing capabilities on a server managed by Slurm. You aim to limit the number of CPUs available so that your code creates a child process for each allocated CPU. However, despite your settings in the job submission script, you're noticing that the program runs on the maximum number of 272 CPUs regardless. Here’s a simplified version of your code and the bash script you might be using.

Example Python Code

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

Example Bash Script

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When you run the script using the command sbatch job.sh, you consistently receive the same high CPU usage statistics, indicating that the code is not adhering to your specified limits.

The Solution

To rectify this issue, you'll need to adjust your Python code to make it aware of the environment variables set by Slurm. Specifically, the SLURM_CPUS_PER_TASK variable holds the number of CPUs you've allocated for the current task, which you can use to set the number of processes correctly.

Updated Python Code

Here's how to modify your code accordingly:

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

Explanation of Changes

Importing os: This library allows you to access environment variables.

Setting the Number of CPUs: We retrieve the number of CPUs allocated for the job using os.environ['SLURM_CPUS_PER_TASK'], which corresponds to the value you've set with # SBATCH --cpus-per-task=48.

Creating the Process Pool: By passing ncpus to the Pool(), we ensure that the pool only utilizes the allocated number of CPUs, preventing any overload or waste of resources.

Conclusion

By following these steps to structure your Slurm job correctly and modifying your Python script, you can harness the full potential of your allocated CPUs without overloading the server. This approach maximizes efficiency and ensures that your computational resources are used appropriately. Whether you're running simulations, data analyses, or other CPU-bound tasks, applying these techniques will help you make the most of your computational environment. Happy coding!