Setting up GPU Data Science Environments for Hackathons

In my first week working at NVIDIA, I have been spending some time with my previous colleagues at the Met Office to explore how the two organizations can collaborate.

We decided to run a hackathon to explore how GPUs could be used to accelerate existing tools and workflows within the Met Office. The attendees were 10–15 people who were seasoned Python developers but had little experience with GPUs. There was some awareness and curiosity about CUDA and GPU acceleration but little hands on experience.

To begin trying things out we needed a collaborative environment with access to GPUs. Our group was familiar with Jupyter Notebooks and we felt that asking people to SSH into a server would add unnecessary complication. There is a project called the Littlest JupyterHub (tljh) which not only provides a straight forward way to install and manage Jupyter Hub on a single server but also has great documentation. However, as we wanted to add GPU support, we had to deviate from the docs a little. This post encapsulates the additional steps we took.

The Met Office folks kindly provided access to an AWS account so that we could build our own server.

At this time tljh only supports ubuntu linux so first we will need to build a ubuntu server with GPUs available. As we had AWS access we will go through the excellent instructions from the tljh docs to set up on AWS with the following adjustments.

Next you need to ssh to the box with the ssh key you selected during setup. In order to make use of GPU enabled libraries we needed to install the NVIDIA CUDA drivers.

Now that we have drivers in place and have rebooted we can install the Littlest JupyterHub as instructed in the documentation. In step 7 we skipped entering the installation command in the user data field, so let’s run that manually now.

Select an admin username that you will remember as you will need that the first time you log in to JupyterHub.

We should now be able to return to the tljh documentation and continue from step 20 by visiting the server’s IP address in the browser.