In this lab, we'll teach you how to do GPU-accelerated numerical computing from Python using the Numba compiler. Numba is an open source compiler that can translate Python functions for execution on the GPU, all without having to write any C or C++ code. Numba's just-in-time compilation ability makes it easy to interactively experiment with GPU computing in the Jupyter notebook. We'll teach you techniques for both automatically parallelizing certain kinds of array functions, as well as how to create and launch CUDA kernels entirely from Python. At the end of the lab, we'll demonstrate how Numba can be combined with Dask for distributed computing on a GPU cluster. Prerequisites: Familiarity with CUDA, Python and NumPy This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.