Cluster nodes are managed through Slurm. Accessing and running jobs on login nodes or by ssh’ing directly to a node is Strictly forbidden.
Commands | Syntax | Description |
|---|---|---|
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| Submit a batch script to SLURM for processing. |
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| Show information about your job(s) in the queue. The command when run without the |
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| Run jobs interactively on the cluster. |
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| End or cancel a queued job. |
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| Show information about current and previous jobs. |
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| To check the status of the cluster and partition, including availability, time limits, and the number of node |
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| Code Block |
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srun --partition=partition_name --time=01:00:00 -gres=gpu:1scu-cpu --mem=8G --cpus-per-task=4 --pty bash -i |
Breakdown:
--gres=gpu:1: Allocates 1 GPU to your job.--partition=partition_namescu-cpu: Specifies the partition to run the job in. Replacepartition_namewith the appropriate partition, likescu-gpu.--time=01:00:00: Requests 1 hour of runtime. Adjust the time as needed.--mem=8G: Requests 8 GB of memory.--cpus-per-task=4: Requests 4 CPU cores.--pty bash: Launches an interactive bash shell after resources are allocated.
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| Code Block |
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#!/bin/bash #SBATCH --job-name=cpu_job # Job name #SBATCH --output=output_file.txt # Output file #SBATCH --partition=cpu_partition # Partition to run the job (e.g., scu-cpu) #SBATCH --time=01:00:00 # Max runtime (1 hour) #SBATCH --mem=8G # Memory requested #SBATCH --cpus-per-task=4 # Number of CPU cores per task #SBATCH --output=job_output-%j.out # Standard output file #SBATCH --error=job_error-%j.err # Error output file # Your commands here srun python my_script.py |
Additional flags to add to that can be added to the sbatch script above:
| Code Block |
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# Request 1 GPU
#SBATCH --gres=gpu:1
# Set email notifications (optional)
#SBATCH --mail-type=BEGIN,END,FAIL
#SBATCH --mail-user=your_email@example.com |
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