Book Image

Distributed Data Systems with Azure Databricks

By : Alan Bernardo Palacio
Book Image

Distributed Data Systems with Azure Databricks

By: Alan Bernardo Palacio

Overview of this book

Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.
Table of Contents (17 chapters)
1
Section 1: Introducing Databricks
4
Section 2: Data Pipelines with Databricks
9
Section 3: Machine and Deep Learning with Databricks

Using Petastorm for distributed learning

Petastorm is an open source library that allows us to do single or distributed training of machine and deep learning algorithms using datasets stored as Apache Parquet files. It supports popular frameworks such as PyTorch, TensorFlow, and PySpark and can also be used for other Python applications. Petastorm provides us with a simple function to augment the functionality of the Parquet format with Petastorm-specific data to be able to be used in machine and deep learning model training. We can simply read our data by creating a reader object from Databricks File System and iterating over it. The underlying Petastorm library uses the PyArrow library to read Parquet files.

In this section, we will discuss how we can use Petastorm to further extend the performance of our machine and deep learning training pipelines in Azure Databricks.

Introducing Petastorm

As mentioned before, Petastorm is an open source library that enables a single machine...