In this workshop, we will train a deep learning model in a distributed manner using Databricks. We will discuss how we can leverage Delta Lake to prepare structured, semi-structured, or unstructured datasets and Petastorm for distributing datasets efficiently on a cluster. We will also cover how to use Horovod for distributed training on both CPU and GPU based hardware. This example aims to serve as a reusable template that is tailorable to meet your specific modeling needs.
A recommended Databricks ML Runtime >= 7.3LTS is suggested. Please use the repos feature to clone into your repo and access the notebook.