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Reproducible Data Science with Pachyderm

You're reading from   Reproducible Data Science with Pachyderm Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781801074483
Length 364 pages
Edition 1st Edition
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Author (1):
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Svetlana Karslioglu Svetlana Karslioglu
Author Profile Icon Svetlana Karslioglu
Svetlana Karslioglu
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Introduction to Pachyderm and Reproducible Data Science
2. Chapter 1: The Problem of Data Reproducibility FREE CHAPTER 3. Chapter 2: Pachyderm Basics 4. Chapter 3: Pachyderm Pipeline Specification 5. Section 2:Getting Started with Pachyderm
6. Chapter 4: Installing Pachyderm Locally 7. Chapter 5: Installing Pachyderm on a Cloud Platform 8. Chapter 6: Creating Your First Pipeline 9. Chapter 7: Pachyderm Operations 10. Chapter 8: Creating an End-to-End Machine Learning Workflow 11. Chapter 9: Distributed Hyperparameter Tuning with Pachyderm 12. Section 3:Pachyderm Clients and Tools
13. Chapter 10: Pachyderm Language Clients 14. Chapter 11: Using Pachyderm Notebooks 15. Other Books You May Enjoy

Chapter 3: Pachyderm Pipeline Specification

A Machine Learning (ML) pipeline is an automated workflow that enables you to execute the same code continuously against different combinations of data and parameters. A pipeline ensures that every cycle is automated and goes through the same sequence of steps. Like in many other technologies, in Pachyderm, an ML pipeline is defined by a single configuration file called the pipeline specification, or the pipeline spec.

The Pachyderm pipeline specification is the most important configuration in Pachyderm as it defines what your pipeline does, how often it runs, how the work is spread across Pachyderm workers, and where to output the result.

This chapter is intended as a pipeline specification reference and will walk you through all the parameters you can specify for your pipeline. To do this, we will cover the following topics:

  • Pipeline specification overview
  • Understanding inputs
  • Exploring informational parameters
  • ...
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