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Learning PySpark

You're reading from   Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

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Product type Paperback
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Understanding Spark FREE CHAPTER 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications Index

Introducing TensorFrames


At the time of writing, TensorFrames is an experimental binding for Apache Spark; it was introduced in early 2016, shortly after the release of TensorFlow. With TensorFrames, one can manipulate Spark DataFrames with TensorFlow programs. Referring to the tensor diagrams in the previous section, we have updated the figure to show how Spark DataFrames work with TensorFlow, as shown in the following diagram:

As noted in the preceding diagram, TensorFrames provides a bridge between Spark DataFrames and TensorFlow. This allows you to take your DataFrames and apply them as input into your TensorFlow computation graph. TensorFrames also allows you to take the TensorFlow computation graph output and push it back into DataFrames so you can continue your downstream Spark processing.

In terms of common usage scenarios for TensorFrames, these typically include the following:

Utilize TensorFlow with your data

The integration of TensorFlow and Apache Spark with TensorFrames allows...

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