Effective Pycharm Learn The Pycharm Ide With A Hands On Approach
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Effective PyCharm
PyCharm is a powerful editor for Python, but it is more than just Python. It is an IDE that supports databases, web development, and data science. Effective PyCharm will walk you through how to learn the tools, and explore many of the features. You will learn the in's and out's of PyCharm and immediately improve your productivity.
Hands-On Application Development with PyCharm
A definitive guide to PyCharm to help you build business-oriented Python applications ranging from modern web development to data science Key FeaturesLearn basic to advanced PyCharm concepts to improve efficiency of your Python projectsWork through practical examples that focus on efficient application development with PyCharmExplore advanced features in PyCharm such as code automation, version control, and GUI debuggingBook Description JetBrain’s PyCharm is the most popular Integrated Development Environment (IDE) used by the Python community thanks to its numerous features that facilitate faster, more accurate, and more productive programming practices. However, the abundance of options and customizations can make PyCharm seem quite intimidating. Hands-on Application Development with PyCharm starts with PyCharm’s installation and configuration process, and systematically takes you through a number of its powerful features that can greatly improve your productivity. You’ll explore code automation, version control, graphical debugging/testing, management of virtual environments, and much more. Finally, you’ll delve into specific PyCharm features that support web development and data science, two of the fastest growing applications in Python programming. These include the integration of the Django framework as well as the extensive support for IPython and Jupyter Notebook. By the end of this PyCharm book, you will have gained extensive knowledge of the tool and be able to implement its features and make the most of its support for your projects. What you will learnExplore PyCharm functionalities and what makes it stand out from other Python IDEsSet up, configure, and customize your Python projects in PyCharmUnderstand how PyCharm integrates with Django for web developmentDiscover PyCharm's capabilities in database management and data visualizationPerform code automation, GUI testing, and version control in PyCharmIntegrate interactive Python tools such as Jupyter Notebooks for building virtual environmentsWho this book is for If you’re a beginner or an expert Python user looking to improve your productivity using one of the best Python IDEs, this book is for you. Basic knowledge of Python programming language is expected.
Hands-On GPU Programming with Python and CUDA
Author: Dr. Brian Tuomanen
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-11-27
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 FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook Description Hands-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 learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho this book is for Hands-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.