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You're reading from  Practical Deep Learning at Scale with MLflow

Product typeBook
Published inJul 2022
PublisherPackt
ISBN-139781803241333
Edition1st Edition
Concepts
Deep Learning
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Author (1)
Yong Liu
Yong Liu
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Yong Liu

Yong Liu has been working in big data science, machine learning, and optimization since his doctoral student years at the University of Illinois at Urbana-Champaign (UIUC) and later as a senior research scientist and principal investigator at the National Center for Supercomputing Applications (NCSA), where he led data science R&D projects funded by the National Science Foundation and Microsoft Research. He then joined Microsoft and AI/ML start-ups in the industry. He has shipped ML and DL models to production and has been a speaker at the Spark/Data+AI summit and NLP summit. He has recently published peer-reviewed papers on deep learning, linked data, and knowledge-infused learning at various ACM/IEEE conferences and journals.
Read more about Yong Liu

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To get the most out of this book

The majority of the code in this book can be implemented and executed using the open source MLflow tool, with a few exceptions where a 14-day full Databricks trial is needed (sign up at https://databricks.com/try-databricks) along with an AWS Free Tier account (sign up at https://aws.amazon.com/free/). The following lists some major software packages covered in this book:

  • MLflow 1.20.2 and above
  • Python 3.8.10
  • Lightning-flash 0.5.0
  • Transformers 4.9.2
  • SHAP 0.40.0
  • PySpark 3.2.1
  • Ray[tune] 1.9.2
  • Optuna 2.10.0

The complete package dependencies are listed in each chapter's requirements.txt file or the conda.yaml file in this book's GitHub repository. All code has been tested to run successfully in a macOS or Linux environment. If you are a Microsoft Windows user, it is recommended to install WSL2 to run the bash scripts provided in this book: https://www.windowscentral.com/how-install-wsl2-windows-10. It is a known issue that the MLflow CLI does not work properly in the Microsoft Windows command line.

Starting from Chapter 3, Tracking Models, Parameters, and Metrics of this book, you will also need to have Docker Desktop (https://www.docker.com/products/docker-desktop/) installed to set up a fully-fledged local MLflow tracking server for executing the code in this book. AWS SageMaker is needed in Chapter 8, Deploying a DL Inference Pipeline at Scale, for the cloud deployment example. VS Code version 1.60 or above (https://code.visualstudio.com/updates/v1_60) is used as the integrated development environment (IDE) in this book. Miniconda version 4.10.3 or above (https://docs.conda.io/en/latest/miniconda.html) is used throughout this book for creating and activating virtual environments.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book's GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

Finally, to get the most out of this book, you should have experience in programming in Python and have a basic understanding of popular ML and data manipulation libraries such as pandas and PySpark.

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Practical Deep Learning at Scale with MLflow
Published in: Jul 2022Publisher: PacktISBN-13: 9781803241333
© 2022 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Yong Liu

Yong Liu has been working in big data science, machine learning, and optimization since his doctoral student years at the University of Illinois at Urbana-Champaign (UIUC) and later as a senior research scientist and principal investigator at the National Center for Supercomputing Applications (NCSA), where he led data science R&D projects funded by the National Science Foundation and Microsoft Research. He then joined Microsoft and AI/ML start-ups in the industry. He has shipped ML and DL models to production and has been a speaker at the Spark/Data+AI summit and NLP summit. He has recently published peer-reviewed papers on deep learning, linked data, and knowledge-infused learning at various ACM/IEEE conferences and journals.
Read more about Yong Liu

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