Example: Running Pytorch Script with GPU Support

Submitting a Python Job to the Noctua2 GPU Partition

Step 1: Setup the Python environment

Load Python Module, e.g:

Change the python package installation path to your scratch folder, if not already specified in your .bashrc (see Python for more details)

(Don’t forget to replace <PROJECT_NAME> and <USER> with your project name and username)

Install pytorch and numpy to your local enrivonment:

Step 2: Create your Python script

Create a Python Script which performs some pytorch operations on the GPU, e.g:

Save it in for example /scratch/<PROJECT_NAME>/<USER>/pytorch_test_project/test.py

Step 3: Create a jobscript and submit it to the Slurm queue

Write a jobscript in order to run your python script on a gpu compute node. Here is an example for a jobscript on Noctua2:

(Don’t forget to replace <PROJECT_NAME> and <USER> with your project name and username)

Save it in the same folder where you python script is located, e.g. /scratch/<PROJECT_NAME>/<USER>/pytorch_test_project/jobscript.sh

Submit the Jobscript by calling:

Using GPUs interactively for development/testing

If you would like to use GPUs for development/testing, it is recommended to do that within an interactive Slurm session on the DGX. More information on that can be found here Running Compute Jobs | Using GPUs for Development and Testing Purposes

Please keep in mind that you also need to setup the Python environment in an interactive session as written in step 1 (see above).