Working with Spark TensorFlow

As Spark offers distributed computation, it can be used to perform neural network training on large data and the model deployment could be done at scale. The distributed training cuts down the training time, improves accuracy and also speeds up the model validation over a single-node model validation. The ability to scale model selection and neural network tuning by adopting tools such as Spark and TensorFlow may be a boon for the data science and machine learning communities because of the increasing availability of cloud computing and parallel resources to a wider range of engineers.

Getting ready

To step through this recipe, you will need a running Spark cluster either in pseudo distributed mode or in one of the distributed modes, that is, standalone, YARN, or Mesos. Also, have Installing TensorFlow recipe for details on the installation.

How to do it…

1. Here is the Python code to run TensorFlow in distributed mode:

         import numpy as np 
         import...