Setting up our Python environment for GPU programming

With our compilers, IDEs, and the CUDA Toolkit properly installed on our system, we now can set up an appropriate Python environment for GPU programming. There are many options here, but we explicitly recommend that you work with the Anaconda Python Distribution. Anaconda Python is a self-contained and user-friendly distribution that can be installed directly in your user directory, and which does not require any administrator or sudo level system access to install, use, or update.

Keep in mind that Anaconda Python comes in two flavors—Python 2.7, and Python 3. Since Python 3 is currently not as well-supported for some of the libraries we will be using, we will be using Python 2.7 in this book, which still has a broad mainstream usage.

You can install Anaconda Python by going to https://www.anaconda.com/download, choosing your operating system, and then by choosing to download the Python 2.7 version of the distribution. Follow the instructions given on the Anaconda site to install the distribution, which is relatively straightforward. We can now set up our local Python installation for GPU programming.

We will now set up what is arguably the most important Python package for this book: Andreas Kloeckner's PyCUDA package.