Meetup At Spark Summit: Tensorflow on Apache Spark & Ask Me Anything

We will be in the Imperial Room

TensorFrames: Tensorflow on Spark DataFrames

We will be holding a meetup during Spark Summit. Agenda to follow!

We will have food and beverages available (sponsored by SAP)

6:30-7pm: Mingling

7-7:15: Welcome

Opening remarks from SAP by Christian Tinnefeld, Research Manager, SAP HANA Vora

7:15-7:45: TensorFrames: Tensorflow on Spark DataFrames

7:45-8:15: AMA Panel on Spark

8:15-8:30pm: Mingling

Since the creation of Apache Spark (http://spark.apache.org/), I/O throughput has increased at a faster pace than processing speed. In a lot of big data applications, the bottleneck is increasingly the CPU. With the release of Apache Spark 2.0 and Project Tungsten, Spark runs a number of control operations close to the metal. At the same time, there has been a surge of interest in using GPUs (the Graphics Processing Units of video cards) for general purpose applications, and a number of frameworks have been proposed to do numerical computations on GPUs.

In this talk, we will discuss how to combine Apache Spark with TensorFlow, a new framework from Google that provides building blocks for Machine Learning computations on GPUs. Through a binding between Spark and TensorFlow called TensorFrames (http://spark-packages.org/package/tjhunter/tensorframes), distributed numerical transforms on Spark DataFrames and Datasets can be expressed in a high-level language and still rely on highly optimized implementations.

The developers of the TensorFrames package will provide an overview, a live demo on Databricks and a presentation of the future plans. For experts, this talk will also include some technical details on design decisions, the current implementation, and ongoing work on speed and performance optimizations for numerical applications.

This talk will be followed by a session of Apache Spark, “Ask Me Anything,” in which the Spark committers and contributors will respond to questions from the audience.

Bio:

Tim Hunter is a software engineer at Databricks and contributes to the Apache Spark MLlib project. He has been building distributed Machine Learning systems with Spark since version 0.5, before Spark was an Apache Software Foundation project.