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The official Jupyter Conference
August 22-23, 2017: Training
August 23-25, 2017: Tutorials & Conference
New York, NY

Schedule: Kernels sessions

Use and development of kernels that use the Jupyter architecture and clients for different programming languages.

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1:50pm–2:30pm Thursday, August 24, 2017
Location: Murray Hill Level: Intermediate
Tim Gasper (Janrain), Subbu Rama (Bitfusion)
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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. 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 Elastic GPUs and quick-switching between custom kernels. Read more.
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11:55am–12:35pm Friday, August 25, 2017
Location: Nassau Level: Intermediate
Alexandre Archambault explores why an official Scala kernel for Jupyter has yet to emerge. Part of the answer lies in the fact that there is no user-friendly, easy-to-use Scala shell in the console (i.e., no IPython for Scala). But there's a new contender, Ammonite—although it still has to overcome a few challenges, not least being supporting by big data frameworks like Spark, Scio, and Scalding. Read more.
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5:00pm–5:40pm Friday, August 25, 2017
Location: Murray Hill Level: Intermediate
Sylvain Corlay (QuantStack), Johan Mabille (QuantStack)
Xeus takes on the burden of implementing the Jupyter kernel protocol so that kernel authors can focus on more easily implementing the language-specific part of the kernel and support features, such as autocomplete or interactive widgets. Sylvain Corlay and Johan Mabille showcase a new C++ kernel based on the Cling interpreter built with xeus. Read more.