Summary and Setup

This is the GPU programming lesson.

Do you want to teach this lesson?

Do you want to teach GPU programming? This material is open-source and freely available. Are you planning on using our material in your teaching? Send us an email at training@esciencecenter.nl. We would love to help you prepare to teach the lesson and receive feedback on how it could be further improved, based on your experience in the workshop.

Programming environment

The GPU programming lesson can be taught using Jupyter Notebook, a programming environment that runs in a web browser. For this to work you we need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported (some older browsers, including Internet Explorer version 9 and below, are not).

In case you do not have any GPU available on your laptop, a good alternative is to use Google Colab.

Local setup

To setup locally, depending on how you installed Python, there are two alternatives: - use pip if you installed Python normally using your OS’s package manager or app store, - use conda or mamba if you installed the conda distribution of Python.

In case you don’t have Python installed, we recommend you start with a variant of the conda distribution: mambaforge. mambaforge by default sets the conda-forge channel as the default, and provides the alternative package manager mamba. mamba is a lot more performant compared to conda, making the user experience significantly smoother.

Whichever case it is for you, the first step is to create an isolated environment for the workshop, this way you won’t interfere with your existing setup. You can install all the dependencies for the workshop within this environment. In the Python ecosystem, this kind of isolated environments are known as virtual environments.

Using pip

To create a virtual environment using pip, you need to install the virtualenv package using your OS’s package manager (it may have alternate names like python-virtualenv or python3-virtualenv). After you have done this, you can follow the steps below:

BASH

cd /path/to/workshop/dir
python -m virtualenv --prompt gpu-workshop venv
source venv/bin/activate
pip install -U pip  # it is good to update pip to the latest version
pip install cupy-cuda11x numba jupyterlab matplotlib scipy astropy

Callout

We are installing the precompiled cupy libraries compiled against the latest version of CUDA. This is always faster to install, but if you want to use a custom CUDA installation, you can pip install cupy instead. Also note, if you also want the cuda compiler nvcc, you have to install the CUDA toolkit manually. However, this is not required to follow the workshop. More information can be found in the cupy documentation.

Using conda or mamba

conda or mamba have support for virtual environments built-in. You can create a new virtual environment with

BASH

mamba create -n gpu-workshop
mamba activate gpu-workshop
mamba install cupy numba jupyterlab matplotlib scipy astropy

If you are using conda, you can simply replace mamba with conda in the commands above.

Starting a Jupyter server

Now you can start your Jupyter server as shown below, which will open a tab with Jupyter in your default browser:

BASH

jupyter-lab

If you do not want Jupyter to open a tab in your browser automatically, you can use the alternative below:

BASH

jupyter-lab --no-browser

This will print out a url in your terminal, which you can then open in the browser of your choice.