Description
In this talk, we show how Python, Numba, and Dask can be used for GPU programming that easily scales from your workstation to a cluster, and can be controlled entirely from a Jupyter notebook. We will describe how the Numba JIT compiler can be used to create and compile GPU calculations entirely from the Python interpreter, and how the Dask task scheduling system can be used to farm these calculations out to a GPU cluster. Using an image processing example application, we will show how these two projects make it easy to iterate and experiment with algorithms on large data sets. Finally, we will conclude with tips and tricks for working with GPUs and distributed computing.