Deep Learning and Elastic GPUs using Jupyter - JupyterCon in New York 2017

Jupyter is an excellent interface for executing deep learning development and training. In fact, many of the tutorials that help you get started with deep learning frameworks use Jupyter notebooks because of the ability to provide small blocks of commented, executable code with easy reproducibility, ability to display intermediate feature extraction information, and useful metrics and console output. However, one of the major challenges with using Jupyter and with deep learning more generally is the complexity of context switching between prototyping with CPUs and accelerated training with GPUs. At Bitfusion, we've developed custom kernels coupled with network-attached Elastic GPUs to make it quick and easy to switch from CPUs to GPUs and back again with only a couple clicks.