Using the Spark TensorFlow Distributor package

One of the most commonly used frameworks in deep learning is the TensorFlow library, which also supports distributed training on both CPU and GPU clusters. We can use it to train deep learning models in Azure Databricks by using Spark TensorFlow Distributor, which is a library that aims to ease the process of training TensorFlow models with complex architecture and lots of trainable parameters in distributed computing systems with large amounts of data.

Spark was limited to distributed training because of the standard execution mode, which is the Map/Reduce mode. In this mode, jobs are executed independently in each worker without any communication between them. In Spark 3.0, there is a new execution mode named barrier execution that allows us to easily train deep learning models in a distributed way by allowing communication between workers during the execution.

Spark TensorFlow Distributor is a TensorFlow-native package that makes...