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Practical Automated Machine Learning Using H2O.ai

You're reading from   Practical Automated Machine Learning Using H2O.ai Discover the power of automated machine learning, from experimentation through to deployment to production

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Product type Paperback
Published in Sep 2022
Publisher Packt
ISBN-13 9781801074520
Length 396 pages
Edition 1st Edition
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Author (1):
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Salil Ajgaonkar Salil Ajgaonkar
Author Profile Icon Salil Ajgaonkar
Salil Ajgaonkar
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Toc

Table of Contents (19) Chapters Close

Preface 1. Part 1 H2O AutoML Basics
2. Chapter 1: Understanding H2O AutoML Basics FREE CHAPTER 3. Chapter 2: Working with H2O Flow (H2O’s Web UI) 4. Part 2 H2O AutoML Deep Dive
5. Chapter 3: Understanding Data Processing 6. Chapter 4: Understanding H2O AutoML Architecture and Training 7. Chapter 5: Understanding AutoML Algorithms 8. Chapter 6: Understanding H2O AutoML Leaderboard and Other Performance Metrics 9. Chapter 7: Working with Model Explainability 10. Part 3 H2O AutoML Advanced Implementation and Productization
11. Chapter 8: Exploring Optional Parameters for H2O AutoML 12. Chapter 9: Exploring Miscellaneous Features in H2O AutoML 13. Chapter 10: Working with Plain Old Java Objects (POJOs) 14. Chapter 11: Working with Model Object, Optimized (MOJO) 15. Chapter 12: Working with H2O AutoML and Apache Spark 16. Chapter 13: Using H2O AutoML with Other Technologies 17. Index 18. Other Books You May Enjoy

Minimum system requirements to use H2O AutoML

H2O is very easy to install, but certain minimum standard requirements need to be met for it to run smoothly and efficiently. The following are some of the minimum requirements needed by H2O in terms of hardware capabilities, along with other software support:

  • The minimum hardware required by H2O is as follows:
    • Memory: H2O runs on an in-memory architecture, so it is limited by the physical memory of the system that uses it. Thus, to be able to process huge chunks of data, the more memory the system, has the better.
    • Central Processing Unit (CPU): By default, H2O will use the maximum available CPUs of the system. However, at a minimum, it will need 4 CPUs.
    • Graphical Processing Unit (GPU): GPU support is only available for XGBoost models in AutoML if the GPUs are NVIDIA GPUs (GPU Cloud, DGX Station, DGX-1, or DGX-2) or if it is a CUDA 8 GPU.
  • The operating systems that support H2O are as follows:
    • Ubuntu 12.04
    • OS X 10.9 or later
    • Windows 7 or later
    • CentOS 6 or later
  • The programming languages that support H2O are as follows:
    • Java: Java is mandatory for H2O. H2O requires a 64-bit JDK to build H2O and a 64-bit JRE to run its binary:
      • Java versions supported: Java SE 15, 14, 13, 12, 11, 10, 9, and 8
    • Other Languages: The following languages are only required if H2O is being run in those environments:
      • Python 2.7.x, 3.5.x, or 3.6.x
      • Scala 2.10 or later
      • R version 3 or later
  • Additional requirements: The following requirements are only needed if H2O is being run in these environments:
    • Hadoop: Cloudera CDH 5.4 or later, Hortonworks HDP 2.2 or later, MapR 4.0 or later, or IBM Open Platform 4.2
    • Conda: 2.7, 3.5, or 3.6
    • Spark: Version 2.1, 2.2, or 2.3

Once we have a system that meets the minimum requirements, we need to focus on H2O’s functional dependencies on other software. H2O has only one dependency and that is Java. Let’s see why Java is important for H2O and how we can download and install the correct supported Java version.

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Practical Automated Machine Learning Using H2O.ai
Published in: Sep 2022
Publisher: Packt
ISBN-13: 9781801074520
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