Home folder for students is /student/<user>
Most users are using Python for their jobs
Setup your environment, Python virtual environment/Anaconda and install in you home folder the necessary libraries for your project
When you are ready to send your job to the slurm queue, adjust the slurm command script
Slurm will put you on the queue, if all the resources you ask for are available you will go to Run(R) state, otherwise it will be Pending(Pd)
The slurm command script : sbatch script.sh
| cmd | Description |
|---|---|
#SBATCH --account=student | Your user belongs to this group |
#SBATCH --job-name=MyJob | name of my job |
#SBATCH –ntasks=2 | Number of job tasks per worker |
#SBATCH –mem=4G | Job wants 4gb on memory |
#SBATCH –time=0 | Time limit on my job e.g. time=11::00 (11 hours), 0 means nolimit |
#SBATCH –partition=Lokaverk | Send job to run this queue |
#SBATCH --output=myBatch.log | Log file for environment & slurm |
| python3 file.py | you put your run command after the directives |
When you job need GPU add this line to you slurm cmd file
#SBATCH --gpus-per-node=1
Note : We recommend all students in this 3-week-course run their GPU job for a short time, using “–time=hh:MM” slurm directive, so all jobs get some GPU time
#SBATCH --mem-per-cpu=2G
#SBATCH --cpus-per-task=2 : 2 cores per process/task
#SBATCH --ntasks-per-node=4 : 4 procees per node/worker
Slurm sbatch
CUDA toolkit version 11,7
Intel oneAPI Math Kernel Library
sox
libsndfile1-dev
ffmpeg
python3-venv
python3-pip