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Author: Holden Karau
Publisher: "O'Reilly Media, Inc."
ISBN: 1098118766
Category : Computers
Languages : en
Pages : 245
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Book Description
Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators. In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You'll explore distributed processing (the pure Python implementation of serverless) and learn how to: Implement stateful applications with Ray actors Build workflow management in Ray Use Ray as a unified system for batch and stream processing Apply advanced data processing with Ray Build microservices with Ray Implement reliable Ray applications
Author: Holden Karau
Publisher: "O'Reilly Media, Inc."
ISBN: 1098118766
Category : Computers
Languages : en
Pages : 245
Get Book Here
Book Description
Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators. In this book, experienced software architecture practitioners Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while reducing single points of failure and manual scheduling. Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. If your data processing or server application has grown beyond what a single computer can handle, this book is for you. You'll explore distributed processing (the pure Python implementation of serverless) and learn how to: Implement stateful applications with Ray actors Build workflow management in Ray Use Ray as a unified system for batch and stream processing Apply advanced data processing with Ray Build microservices with Ray Implement reliable Ray applications
Author: Holden Karau
Publisher: "O'Reilly Media, Inc."
ISBN: 1098119843
Category : Computers
Languages : en
Pages : 226
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Book Description
Modern systems contain multi-core CPUs and GPUs that have the potential for parallel computing. But many scientific Python tools were not designed to leverage this parallelism. With this short but thorough resource, data scientists and Python programmers will learn how the Dask open source library for parallel computing provides APIs that make it easy to parallelize PyData libraries including NumPy, pandas, and scikit-learn. Authors Holden Karau and Mika Kimmins show you how to use Dask computations in local systems and then scale to the cloud for heavier workloads. This practical book explains why Dask is popular among industry experts and academics and is used by organizations that include Walmart, Capital One, Harvard Medical School, and NASA. With this book, you'll learn: What Dask is, where you can use it, and how it compares with other tools How to use Dask for batch data parallel processing Key distributed system concepts for working with Dask Methods for using Dask with higher-level APIs and building blocks How to work with integrated libraries such as scikit-learn, pandas, and PyTorch How to use Dask with GPUs
Author: Max Pumperla
Publisher: "O'Reilly Media, Inc."
ISBN: 1098117182
Category : Computers
Languages : en
Pages : 277
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Book Description
Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale. Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started. Learn how to build your first distributed applications with Ray Core Conduct hyperparameter optimization with Ray Tune Use the Ray RLlib library for reinforcement learning Manage distributed training with the Ray Train library Use Ray to perform data processing with Ray Datasets Learn how work with Ray Clusters and serve models with Ray Serve Build end-to-end machine learning applications with Ray AIR
Author: Andrew P. McMahon
Publisher: Packt Publishing Ltd
ISBN: 1837634351
Category : Computers
Languages : en
Pages : 463
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Book Description
Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain Key Features This second edition delves deeper into key machine learning topics, CI/CD, and system design Explore core MLOps practices, such as model management and performance monitoring Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools Book DescriptionThe Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field. The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift. Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques. With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.What you will learn Plan and manage end-to-end ML development projects Explore deep learning, LLMs, and LLMOps to leverage generative AI Use Python to package your ML tools and scale up your solutions Get to grips with Apache Spark, Kubernetes, and Ray Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow Detect drift and build retraining mechanisms into your solutions Improve error handling with control flows and vulnerability scanning Host and build ML microservices and batch processes running on AWS Who this book is for This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you’re not a developer but want to manage or understand the product lifecycle of these systems, you’ll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.
Author: Tomasz Palczewski
Publisher: Packt Publishing Ltd
ISBN: 1803238054
Category : Computers
Languages : en
Pages : 322
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Book Description
Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud services Key Features Understand how to execute a deep learning project effectively using various tools available Learn how to develop PyTorch and TensorFlow models at scale using Amazon Web Services Explore effective solutions to various difficulties that arise from model deployment Book Description Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives. First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors' collective knowledge of deploying hundreds of AI-based services at a large scale. By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting. What you will learn Understand how to develop a deep learning model using PyTorch and TensorFlow Convert a proof-of-concept model into a production-ready application Discover how to set up a deep learning pipeline in an efficient way using AWS Explore different ways to compress a model for various deployment requirements Develop Android and iOS applications that run deep learning on mobile devices Monitor a system with a deep learning model in production Choose the right system architecture for developing and deploying a model Who this book is for Machine learning engineers, deep learning specialists, and data scientists will find this book helpful in closing the gap between the theory and application with detailed examples. Beginner-level knowledge in machine learning or software engineering will help you grasp the concepts covered in this book easily.
Author: Suneeta Mall
Publisher: "O'Reilly Media, Inc."
ISBN: 1098145259
Category : Computers
Languages : en
Pages : 448
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Book Description
Bringing a deep-learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack deep learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack deep learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. You'll gain a thorough understanding of: How data flows through the deep-learning network and the role the computation graphs play in building your model How accelerated computing speeds up your training and how best you can utilize the resources at your disposal How to train your model using distributed training paradigms, i.e., data, model, and pipeline parallelism How to leverage PyTorch ecosystems in conjunction with NVIDIA libraries and Triton to scale your model training Debugging, monitoring, and investigating the undesirable bottlenecks that slow down your model training How to expedite the training lifecycle and streamline your feedback loop to iterate model development A set of data tricks and techniques and how to apply them to scale your training model How to select the right tools and techniques for your deep-learning project Options for managing the compute infrastructure when running at scale
Author: Scott Bateman
Publisher: Packt Publishing Ltd
ISBN: 1804610577
Category : Computers
Languages : en
Pages : 276
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Book Description
Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook. Key Features Explore the architecture and different use cases to build and manage geospatial data lakes in AWS Discover how to leverage AWS purpose-built databases to store and analyze geospatial data Learn how to recognize which anti-patterns to avoid when managing geospatial data in the cloud Book DescriptionManaging geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data. You’ll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you’ll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you’ll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning. By the end of this book, you’ll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.What you will learn Discover how to optimize the cloud to store your geospatial data Explore management strategies for your data repository using AWS Single Sign-On and IAM Create effective SQL queries against your geospatial data using Athena Validate postal addresses using Amazon Location services Process structured and unstructured geospatial data efficiently using R Use Amazon SageMaker to enable machine learning features in your application Explore the free and subscription satellite imagery data available for use in your GIS Who this book is forIf you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book.
Author: Mahesh Venkitachalam
Publisher: No Starch Press
ISBN: 1718503059
Category : Computers
Languages : en
Pages : 447
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Book Description
Put the fun back in Python programming and build your skills as you create 3D simulations and graphics, speech-recognition machine-learning systems, IoT devices, and more. The fully updated 2nd edition is here, now with 5 brand-new projects! Harness the power of Python as you turn code into tangible creations with Python Playground, a collection of 15 inventive projects that will expand your programming horizons, spark your curiosity, and elevate your coding skills. Go beyond the basics as you write programs to generate art and music, simulate real-world phenomena, and interact with hardware, all through the use of Python and common libraries such as numpy, matplotlib, and Pillow. As you work through the book’s projects, you will: Craft intricate Spirograph-like designs with parametric equations and the turtle module Generate music by synthesizing plucked string sounds Transform everyday images into ASCII art, photomosaics, and eye-popping autostereograms Design engaging cellular automata and flocking simulations Explore the realm of 3D graphics, from basic shape rendering to visualizing MRI scan data Build a Raspberry Pi–powered laser show that dances along with music New to this edition: We’ve expanded your playground with five new projects: you’ll draw fractals, bring Conway’s Game of Life into 3D space, and use a Raspberry Pi and Python to create a musical instrument, an IoT garden monitor, and even a machine learning–driven speech recognition system. Whether you’re a seasoned professional or just getting started, you’ll find Python Playground to be a great way to learn, experiment with, and master this versatile programming language. Covers Python 3.x
Author: Étienne André
Publisher: Springer Nature
ISBN: 3031453298
Category : Computers
Languages : en
Pages : 453
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Book Description
This book constitutes the refereed proceedings of the 21st International Symposium on Automated Technology for Verification and Analysis, ATVA 2023, held in Singapore, in October 2023. The symposium intends to promote research in theoretical and practical aspects of automated analysis, verification and synthesis by providing a forum for interaction between regional and international research communities and industry in related areas. The 30 regular papers presented together with 7 tool papers were carefully reviewed and selected from 150 submissions.The papers are divided into the following topical sub-headings: Temporal logics, Data structures and heuristics, Verification of programs and hardware.
Author: Amanda Bienz
Publisher: Springer Nature
ISBN: 3031408438
Category : Computers
Languages : en
Pages : 677
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Book Description
This volume constitutes the papers of several workshops which were held in conjunction with the 38th International Conference on High Performance Computing, ISC High Performance 2023, held in Hamburg, Germany, during May 21–25, 2023. The 49 revised full papers presented in this book were carefully reviewed and selected from 70 submissions. ISC High Performance 2023 presents the following workshops: 2nd International Workshop on Malleability Techniques Applications in High-Performance Computing (HPCMALL) 18th Workshop on Virtualization in High-Performance Cloud Computing (VHPC 23) HPC I/O in the Data Center (HPC IODC) Workshop on Converged Computing of Cloud, HPC, and Edge (WOCC’23) 7th International Workshop on In Situ Visualization (WOIV’23) Workshop on Monitoring and Operational Data Analytics (MODA23) 2nd Workshop on Communication, I/O, and Storage at Scale on Next-Generation Platforms: Scalable Infrastructures First International Workshop on RISC-V for HPC Second Combined Workshop on Interactive and Urgent Supercomputing (CWIUS) HPC on Heterogeneous Hardware (H3)
