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This is the code repository forDistributed Machine Learning with Python, published by Packt.

Accelerating model training and serving with distributed systems

Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude

This book covers the following exciting features:

• Deploy distributed model training and serving pipelines
• Get to grips with the advanced features in TensorFlow and PyTorch
• Mitigate system bottlenecks during in-parallel model training and serving
• Discover the latest techniques on top of classical parallelism paradigm
• Explore advanced features in Megatron-LM and Mesh-TensorFlow
• Use state-of-the-art hardware such as NVLink, NVSwitch, and GPUs

If you feel this book is for you, get yourcopy today!

All of the code is organized into folders.

The code will look like the following:

# Connect to API through subscription key and endpointsubscription_key = "<your-subscription-key>"endpoint = "https://<your-cognitive-service>.cognitiveservices.azure.com/"# Authenticatecredential = AzureKeyCredential(subscription_key)cog_client = TextAnalyticsClient(endpoint=endpoint,credential=credential)

Following is what you need for this book:This book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial.You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.

With the following software and hardware list you can run all code files present in the book (Chapter 1-12)

We assume you have Linux/Ubuntu as your operating system. We assume you useNVIDIA GPUs and have installed the proper NVIDIA driver as well. We also assume youhave basic knowledge about machine learning in general and are familiar with populardeep learning models.

We also provide a PDF file that has color images of the screenshots/diagrams used in this book.Click here to download it.

• Distributed Data Systems with Azure Databricks[Packt][Amazon]

• Machine Learning with the Elastic Stack - Second Edition[Packt][Amazon]

Guanhua Wangis a final-year computer science Ph.D. student in the RISELab at UCBerkeley, advised by Professor Ion Stoica. His research lies primarily in the machinelearning systems area, including fast collective communication, efficient in-parallel modeltraining, and real-time model serving. His research has gained lots of attention from bothacademia and industry. He was invited to give talks to top-tier universities (MIT, Stanford,CMU, Princeton) and big tech companies (Facebook/Meta, Microsoft). He received hismaster's degree from HKUST and a bachelor's degree from Southeast University in China.He has also done some cool research on wireless networks. He likes playing soccer and hasrun multiple half-marathons in the Bay Area of California.

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

https://packt.link/free-ebook/9781801815697