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...