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.
Combined with GPUs, Jupyter makes for fast development and fast execution, but it is not always easy to switch from a CPU execution context to GPUs and back. Drawing on their work at Bitfusion, Tim Gasper and Subbu Rama share best practices for doing deep learning with Jupyter and explain how to work with CPUs and GPUs more easily by using network-attached Elastic GPUs and quick-switching between custom kernels.
Tim Gasper is Director of Product for Data & Analytics at Janrain, the world’s leading independent customer identity management company. With over a decade of experience in product management, Tim has spent his entire career helping companies gain value from their data. Tim previously ran product and marketing for Bitfusion, an artificial intelligence (AI) software company, led product for Rackspace’s Hadoop and NoSQL as a Service, and managed global offerings for CSC (now known as DXC) Big Data & Analytics business unit. Prior to that he was VP of Product at Infochimps, an enterprise big data cloud services company, acquired by CSC in 2013. Tim received his B.S. from Case Western Reserve University, and writes and speaks on entrepreneurship, big data, and AI.
Subbu Rama is cofounder and CEO at Bitfusion, a company providing tools to make deep learning and AI application development and infrastructure management faster and easier. Previously, Subbu held various roles at Intel, leading engineering efforts spanning design, automation, validation, and postsilicon. He worked on Intel’s first integrated-graphics CPU, Intel’s first low-power CPU, Atom, and its SOC, high-performance microservers (Intel’s first initiative on microservers using low-power mobile phone processors), and Xeon servers. Subbu also led Dell Innovation Labs, driving innovation and skunk works, later overseeing numerous new strategic technology initiatives at the intersection of software and the cloud. There he built an engineering team from the ground up and launched Dell’s first cloud infrastructure marketplace.
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