There are three pieces of example code here:

• a Spark program to launch TensorFlow functions inside a Spark 'mapper' function. This is useful for hyperparameter optimization (parameter sweeps)

• a TensorFlowOnSpark program that can be used with Hops Hadoop

• a Horovod program that can be used with Hops Hadoop

This has been tested on a Ubuntu 16.04 machine, but should work on other platforms. We recommend that you do the following inside a new conda environment, but you may also run it without a virtual environment.

# Download and install standalone Spark
wget http://apache.mirrors.spacedump.net/spark/spark-2.2.1/spark-2.2.1-bin-hadoop2.7.tgz
tar zxf spark-2.2.1-bin-hadoop2.7.tgz
export SPARK_HOME=$(pwd)/spark-2.2.1-bin-hadoop2.7
export PATH=$SPARK_HOME/bin:$PATH
pip install pyspark
pip install tensorflow==1.4.0
pip install jupyter
git clone https://github.com/hopshadoop/distributed-tf.git
cd distributed-tf

export PYSPARK_DRIVER_PYTHON=jupyter
export PYSPARK_DRIVER_PYTHON_OPTS='notebook'
pyspark
#The 'pyspark' command should start a Jupyter Notebook in your web browser. 
#You can then create a new 'pyspark' notebook and test it using this code

You can get more details on TensorFlowOnSpark at its github repo.

More details on Horovod at its github repo.

For details on how to install and setup Hops, so that you can run all three programs in a Tour, go to hops.io.