Get writing tests and learn to design your own testing framework with Selenium WebDriver API Key Features Learn Selenium from the ground up Design your own testing framework Create reusable functionality in your framework Book DescriptionSelenium WebDriver is a platform-independent API for automating the testing of both browser and mobile applications. It is also a core technology in many other browser automation tools, APIs, and frameworks. This book will guide you through the WebDriver APIs that are used in automation tests. Chapter by chapter, we will construct the building blocks of a page object model framework as you learn about the required Java and Selenium methods and terminology. The book starts with an introduction to the same-origin policy, cross-site scripting dangers, and the Document Object Model (DOM). Moving ahead, we'll learn about XPath, which allows us to select items on a page, and how to design a customized XPath. After that, we will be creating singleton patterns and drivers. Then you will learn about synchronization and handling pop-up windows. You will see how to create a factory for browsers and understand command design patterns applicable to this area. At the end of the book, we tie all this together by creating a framework and implementing multi-browser testing with Selenium Grid. What you will learn Understand what an XPath is and how to design a customized XPath Learn how to create a Maven project and build Create a Singleton driver Get to grips with Jenkins integration Create a factory for browsers Implement multi-browser testing with Selenium Grid Create a sample pop-up window and JavaScript alert Report using Extent Reports Who this book is forThis book is for software testers or developers.

Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book. Key Features Expand your background in GPU programming-PyCUDA, scikit-cuda, and Nsight Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver Apply GPU programming to modern data science applications Book DescriptionHands-On GPU Programming with Python and CUDA hits the ground running: you'll start by learning how to apply Amdahl's Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You'll then see how to "query" the GPU's features and copy arrays of data to and from the GPU's own memory. As you make your way through the book, you'll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You'll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you'll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You'll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you'll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing. What you will learn Launch GPU code directly from Python Write effective and efficient GPU kernels and device functions Use libraries such as cuFFT, cuBLAS, and cuSolver Debug and profile your code with Nsight and Visual Profiler Apply GPU programming to datascience problems Build a GPU-based deep neuralnetwork from scratch Explore advanced GPU hardware features, such as warp shuffling Who this book is forHands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.

Discover how CUDA allows OpenCV to handle complex and rapidly growing image data processing in computer and machine vision by accessing the power of GPU Key Features Explore examples to leverage the GPU processing power with OpenCV and CUDA Enhance the performance of algorithms on embedded hardware platforms Discover C++ and Python libraries for GPU acceleration Book DescriptionComputer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications. To start with, you'll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You'll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples. Once you have got to grips with the core concepts, you'll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python. By the end of this book, you'll have enhanced computer vision applications with the help of this book's hands-on approach. What you will learn Understand how to access GPU device properties and capabilities from CUDA programs Learn how to accelerate searching and sorting algorithms Detect shapes such as lines and circles in images Explore object tracking and detection with algorithms Process videos using different video analysis techniques in Jetson TX1 Access GPU device properties from the PyCUDA program Understand how kernel execution works Who this book is forThis book is a go-to guide for you if you are a developer working with OpenCV and want to learn how to process more complex image data by exploiting GPU processing. A thorough understanding of computer vision concepts and programming languages such as C++ or Python is expected.

Get to grips with the advanced concepts of interactive computing to make the most out of IPython About This Book * Most updated book on Interactive computing with IPython 4.0; * Detailed, example-rich guide that lets you use the most advanced level interactive programming with IPython; * Get flexible interactive programming with IPython using this comprehensive guide Who This Book Is For This book is for IPython developers who want to make the most of IPython and perform advanced scientific computing with IPython utilizing the ease of interactive computing. It is ideal for users who wish to learn about the interactive and parallel computing properties of IPython 4.0, along with its integration with third-party tools and concepts such as testing and documenting results. What You Will Learn * Develop skills to use IPython for high performance computing (HPC) * Understand the IPython interactive shell * Use XeroMQ and MPI to pass messages * Integrate third-party tools like R, Julia, and JavaScript with IPython * Visualize the data * Acquire knowledge to test and document the data * Get to grips with the recent developments in the Jupyter notebook system In Detail IPython is an interactive computational environment in which you can combine code execution, rich text, mathematics, plots, and rich media. This book will get IPython developers up to date with the latest advancements in IPython and dive deep into interactive computing with IPython. This an advanced guide on interactive and parallel computing with IPython will explore advanced visualizations and high-performance computing with IPython in detail. You will quickly brush up your knowledge of IPython kernels and wrapper kernels, then we'll move to advanced concepts such as testing, Sphinx, JS events, interactive work, and the ZMQ cluster. The book will cover topics such as IPython Console Lexer, advanced configuration, and third-party tools. By the end of this book, you will be able to use IPython for interactive and parallel computing in a high-performance computing environment. Style and approach This is a comprehensive guide to IPython for interactive, exploratory and parallel computing. It will let the IPython get up to date with the latest advancements in IPython and dive deeper into interactive computing with IPython

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book * Design, engineer and deploy scalable machine learning solutions with the power of Python * Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework * Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn * Apply the most scalable machine learning algorithms * Work with modern state-of-the-art large-scale machine learning techniques * Increase predictive accuracy with deep learning and scalable data-handling techniques * Improve your work by combining the MapReduce framework with Spark * Build powerful ensembles at scale * Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

Over 25 hands-on recipes to create robust and highly-efficient cross-platform distributed applications with the Boost.Asio library About This Book * Build highly efficient distributed applications with ease * Enhance your cross-platform network programming skills with one of the most reputable C++ libraries * Find solutions to real-world problems related to network programming with ready-to-use recipes using this detailed and practical handbook Who This Book Is For If you want to enhance your C++ network programming skills using the Boost.Asio library and understand the theory behind development of distributed applications, this book is just what you need. The prerequisite for this book is experience with general C++11. To get the most from the book and comprehend advanced topics, you will need some background experience in multithreading. What You Will Learn * Boost your working knowledge of one of the most reputable C++ networking libraries-Boost.Asio * Familiarize yourself with the basics of TCP and UDP protocols * Create scalable and highly-efficient client and server applications * Understand the theory behind development of distributed applications * Increase the security of your distributed applications by adding SSL support * Implement a HTTP client easily * Use iostreams, scatter-gather buffers, and timers In Detail Starting with recipes demonstrating the execution of basic Boost.Asio operations, the book goes on to provide ready-to-use implementations of client and server applications from simple synchronous ones to powerful multithreaded scalable solutions. Finally, you are presented with advanced topics such as implementing a chat application, implementing an HTTP client, and adding SSL support. All the samples presented in the book are ready to be used in real projects just out of the box. As well as excellent practical examples, the book also includes extended supportive theoretical material on distributed application design and construction. Style and approach This book is a set of recipes, each containing the statement and description of a particular practical problem followed by code sample providing the solution to the problem and detailed step-by-step explanation. Recipes are grouped by topic into chapters and ordered by the level of complexity from basic to advanced.

Create robust and scalable applications along with responsive UI using concurrency and the multi-threading infrastructure in .NET and C# About This Book * Learn to combine your asynchronous operations with Task Parallel Library * Master C#'s asynchronous infrastructure and use asynchronous APIs effectively to achieve optimal responsiveness of the application * An easy-to-follow, example-based guide that helps you to build scalable applications using concurrency in C# Who This Book Is For If you are a C# developer who wants to develop modern applications in C# and wants to overcome problems by using asynchronous APIs and standard patterns, then this book is ideal for you. Reasonable development knowledge, an understanding of core elements and applications related to the .Net platform, and also the fundamentals of concurrency is assumed. What You Will Learn * Apply general multithreading concepts to your application's design * Leverage lock-free concurrency and learn about its pros and cons to achieve efficient synchronization between user threads * Combine your asynchronous operations with Task Parallel Library * Make your code easier with C#'s asynchrony support * Use common concurrent collections and programming patterns * Write scalable and robust server-side asynchronous code * Create fast and responsible client applications * Avoid common problems and troubleshoot your multi-threaded and asynchronous applications In Detail Starting with the traditional approach to concurrency, you will learn how to write multithreaded concurrent programs and compose ways that won't require locking. You will explore the concepts of parallelism granularity, and fine-grained and coarse-grained parallel tasks by choosing a concurrent program structure and parallelizing the workload optimally. You will also learn how to use task parallel library, cancellations, timeouts, and how to handle errors. You will know how to choose the appropriate data structure for a specific parallel algorithm to achieve scalability and performance. Further, you'll learn about server scalability, asynchronous I/O, and thread pools, and write responsive traditional Windows and Windows Store applications. By the end of the book, you will be able to diagnose and resolve typical problems that could happen in multithreaded applications. Style and approach An easy-to-follow, example-based guide that will walk you through the core principles of concurrency and multithreading using C#.

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IBM Cognos BI v10.2 is an advanced suite of business intelligence tools that empower a business with a wide range of capabilities to plan, design, develop, manage reports, analysis, and design dashboards to deliver effective business intelligence strategies. This book particularly focuses on understanding the administration aspects of managing a Cognos BI environment. This fast-paced guide will provide you with practical hands-on experience of all the necessary features to administer an IBM Cognos environment.

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Written as a step-by-step guide, Getting Started with Hazelcast will teach you all you need to know to make your application data scalable. This book is a great introduction for Java developers, software architects, or developers looking to enable scalable and agile data within their applications. You should have programming knowledge of Java and a general familiarity with concepts like data caching and clustering.