This minicourse covers ways to speed up your code using GPUs. Since many of us do not have a reasonable (NVidia) GPU on our laptops, the course is designed to be run on our local teaching cluster. You will need to be on the Princeton network, and will need to be able to access Adroit (registration beforehand required).

Log into our OnDemand site, https://myadroit.princeton.edu. You will want to select "Clusters -> Shell" on the header bar.

Now, you'll want to type:

git clone https://github.com/henryiii/pygpu-minicourse

This will get the course materials. Press CTRL+D to quit.

We will be working with a small number of shared GPUs, so you'll want to work in a CPU only instance, and only submit a notebook to the GPU 1-at-a-time (so you don't block them for others).

Back on the header bar on the original page, click "Interactive Apps" or "My Interactive sessions", then select "Jupyter". You should see a page that looks like this:

Make sure you have checked the JupyterLab checkbox, that you have enough time (at least 2 hours), and that you have entered our reservation (pygpu). Leave the extra slurm options blank.

After you click launch, you should soon see a button that looks like this:

Click it to enter JupyterLab!

If you have a GPU, you can install the environment provided in environment.yml with Conda. You'll probably have to choose a kernel when you launch it (and you may need the conda_nb_kernel package).

Load the ExampleRunner.ipynb notebook. You can enter the name of a GPU notebook (without the extension) at the top of the provided cell, and run that to submit the notebook as a job.

Link: See chat.