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https://github.com/mikeroyal/CUDA-Guide

CUDA Guide
https://github.com/mikeroyal/CUDA-Guide

List: CUDA-Guide

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CUDA Guide

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#### A guide covering CUDA including the applications and tools that will make you a better and more efficient CUDA developer.



**Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).**



 

 # Table of Contents

 

 1. [CUDA Learning Resources](https://github.com/mikeroyal/CUDA-Guide#cuda-learning-resources)

 

 2. [CUDA Tools](https://github.com/mikeroyal/CUDA-Guide#cuda-tools)



 3. [C/C++ Development](https://github.com/mikeroyal/CUDA-Guide#cc-development)

 

 4. [Python Development](https://github.com/mikeroyal/CUDA-Guide#python-development)





 

  





**CUDA Toolkit. Source: [NVIDIA Developer CUDA](https://developer.nvidia.com/cuda-zone)**



## CUDA Learning Resources



[Back to the Top](https://github.com/mikeroyal/CUDA-Guide#table-of-contents)



[CUDA](https://developer.nvidia.com/cuda-zone) is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.



[CUDA Toolkit Documentation](https://docs.nvidia.com/cuda/index.html)



[CUDA Quick Start Guide](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html)



[CUDA on WSL](https://docs.nvidia.com/cuda/wsl-user-guide/index.html)



[CUDA GPU support for TensorFlow](https://www.tensorflow.org/install/gpu)



[NVIDIA Deep Learning cuDNN Documentation](https://docs.nvidia.com/deeplearning/cudnn/api/index.html)



[NVIDIA GPU Cloud Documentation](https://docs.nvidia.com/ngc/ngc-introduction/index.html)



[NVIDIA NGC](https://ngc.nvidia.com/) is a hub for GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.



[NVIDIA NGC Containers](https://www.nvidia.com/en-us/gpu-cloud/containers/) is a registry that provides researchers, data scientists, and developers with simple access to a comprehensive catalog of GPU-accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.



## CUDA Tools



[Back to the Top](https://github.com/mikeroyal/CUDA-Guide#table-of-contents)



[CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) is a collection of tools & libraries that provide a development environment for creating high performance GPU-accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.



[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).



[CUDA-X HPC](https://www.nvidia.com/en-us/technologies/cuda-x/) is a collection of libraries, tools, compilers and APIs that help developers solve the world's most challenging problems. CUDA-X HPC includes highly tuned kernels essential for high-performance computing (HPC). 



[NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-docker) is a collection of tools & libraries that allows users to build and run GPU accelerated Docker containers. The toolkit includes a container runtime [library](https://github.com/NVIDIA/libnvidia-container) and utilities to automatically configure containers to leverage NVIDIA GPUs.



[Minkowski Engine](https://nvidia.github.io/MinkowskiEngine) is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors. 



[CUTLASS](https://github.com/NVIDIA/cutlass) is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS. 



[CUB](https://github.com/NVIDIA/cub) is a cooperative primitives for CUDA C++ kernel authors.



[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.



[Numba](https://github.com/numba/numba) is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.



[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.



[CuPy](https://cupy.dev/) is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.



[CatBoost](https://catboost.ai/) is a fast, scalable, high performance [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU. 



[cuDF](https://rapids.ai/) is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.



[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.



[ArrayFire](https://arrayfire.com/) is a general-purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.



[Thrust](https://github.com/NVIDIA/thrust) is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.



[AresDB](https://eng.uber.com/aresdb/) is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management. 



[Arraymancer](https://mratsim.github.io/Arraymancer/) is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.



[Kintinuous](https://github.com/mp3guy/Kintinuous) is a real-time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.



[GraphVite](https://graphvite.io/) is a general graph embedding engine, dedicated to high-speed and large-scale embedding learning in various applications.



# C/C++ Development



[Back to the Top](https://github.com/mikeroyal/CUDA-Guide#table-of-contents)





 

  


  




## C/C++ Learning Resources



[C++](https://www.cplusplus.com/doc/tutorial/) is a cross-platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.



[C](https://www.iso.org/standard/74528.html) is a general-purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.



[Embedded C](https://en.wikipedia.org/wiki/Embedded_C) is a set of language extensions for the C programming language by the [C Standards Committee](https://isocpp.org/std/the-committee) to address issues that exist between C extensions for different [embedded systems](https://en.wikipedia.org/wiki/Embedded_system). The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.



[C & C++ Developer Tools from JetBrains](https://www.jetbrains.com/cpp/)



[Open source C++ libraries on cppreference.com](https://en.cppreference.com/w/cpp/links/libs)



[C++ Graphics libraries](https://cpp.libhunt.com/libs/graphics)



[C++ Libraries in MATLAB](https://www.mathworks.com/help/matlab/call-cpp-library-functions.html)



[C++ Tools and Libraries Articles](https://www.cplusplus.com/articles/tools/)



[Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html)



[Introduction C++ Education course on Google Developers](https://developers.google.com/edu/c++/)



[C++ style guide for Fuchsia](https://fuchsia.dev/fuchsia-src/development/languages/c-cpp/cpp-style)



[C and C++ Coding Style Guide by OpenTitan](https://docs.opentitan.org/doc/rm/c_cpp_coding_style/)



[Chromium C++ Style Guide](https://chromium.googlesource.com/chromium/src/+/master/styleguide/c++/c++.md)



[C++ Core Guidelines](https://github.com/isocpp/CppCoreGuidelines/blob/master/CppCoreGuidelines.md)



[C++ Style Guide for ROS](http://wiki.ros.org/CppStyleGuide)



[Learn C++](https://www.learncpp.com/)



[Learn C : An Interactive C Tutorial](https://www.learn-c.org/)



[C++ Institute](https://cppinstitute.org/free-c-and-c-courses)



[C++ Online Training Courses on LinkedIn Learning](https://www.linkedin.com/learning/topics/c-plus-plus)



[C++ Tutorials on W3Schools](https://www.w3schools.com/cpp/default.asp)



[Learn C Programming Online Courses on edX](https://www.edx.org/learn/c-programming)



[Learn C++ with Online Courses on edX](https://www.edx.org/learn/c-plus-plus)



[Learn C++ on Codecademy](https://www.codecademy.com/learn/learn-c-plus-plus)



[Coding for Everyone: C and C++ course on Coursera](https://www.coursera.org/specializations/coding-for-everyone)



[C++ For C Programmers on Coursera](https://www.coursera.org/learn/c-plus-plus-a)



[Top C Courses on Coursera](https://www.coursera.org/courses?query=c%20programming)



[C++ Online Courses on Udemy](https://www.udemy.com/topic/c-plus-plus/)



[Top C Courses on Udemy](https://www.udemy.com/topic/c-programming/)



[Basics of Embedded C Programming for Beginners on Udemy](https://www.udemy.com/course/embedded-c-programming-for-embedded-systems/)



[C++ For Programmers Course on Udacity](https://www.udacity.com/course/c-for-programmers--ud210)



[C++ Fundamentals Course on Pluralsight](https://www.pluralsight.com/courses/learn-program-cplusplus)



[Introduction to C++ on MIT Free Online Course Materials](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-introduction-to-c-january-iap-2011/)



[Introduction to C++ for Programmers | Harvard ](https://online-learning.harvard.edu/course/introduction-c-programmers)



[Online C Courses | Harvard University](https://online-learning.harvard.edu/subject/c)



## C/C++ Tools



[AWS SDK for C++](https://aws.amazon.com/sdk-for-cpp/)



[Azure SDK for C++](https://github.com/Azure/azure-sdk-for-cpp)



[Azure SDK for C](https://github.com/Azure/azure-sdk-for-c)



[C++ Client Libraries for Google Cloud Services](https://github.com/googleapis/google-cloud-cpp)



[Visual Studio](https://visualstudio.microsoft.com/) is an integrated development environment (IDE) from Microsoft; which is a feature-rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.



[Visual Studio Code](https://code.visualstudio.com/) is a code editor redefined and optimized for building and debugging modern web and cloud applications.



[Vcpkg](https://github.com/microsoft/vcpkg) is a C++ Library Manager for Windows, Linux, and MacOS.



[ReSharper C++](https://www.jetbrains.com/resharper-cpp/features/) is a Visual Studio Extension for C++ developers developed by JetBrains.



[AppCode](https://www.jetbrains.com/objc/) is constantly monitoring the quality of your code. It warns you of errors and smells and suggests quick-fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.



[CLion](https://www.jetbrains.com/clion/features/) is a cross-platform IDE for C and C++ developers developed by JetBrains.



[Code::Blocks](https://www.codeblocks.org/) is a free C/C++ and Fortran IDE built to meet the most demanding needs of its users. It is designed to be very extensible and fully configurable. Built around a plugin framework, Code::Blocks can be extended with plugins.



[CppSharp](https://github.com/mono/CppSharp) is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem. It consumes C/C++ header and library files and generates the necessary glue code to surface the native API as a managed API. Such an API can be used to consume an existing native library in your managed code or add managed scripting support to a native codebase.



[Conan](https://conan.io/) is an Open Source Package Manager for C++ development and dependency management into the 21st century and on par with the other development ecosystems. 



[High Performance Computing (HPC) SDK](https://developer.nvidia.com/hpc) is a comprehensive toolbox for GPU accelerating HPC modeling and simulation applications. It includes the C, C++, and Fortran compilers, libraries, and analysis tools necessary for developing HPC applications on the NVIDIA platform.



[Thrust](https://github.com/NVIDIA/thrust) is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.



[Boost](https://www.boost.org/) is an educational opportunity focused on cutting-edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.



[Automake](https://www.gnu.org/software/automake/) is a tool for automatically generating Makefile.in files compliant with the GNU Coding Standards. Automake requires the use of GNU Autoconf.



[Cmake](https://cmake.org/) is an open-source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.



[GDB](http://www.gnu.org/software/gdb/) is a debugger, that allows you to see what is going on `inside' another program while it executes or what another program was doing at the moment it crashed. 



[GCC](https://gcc.gnu.org/) is a compiler Collection that includes front ends for C, C++, Objective-C, Fortran, Ada, Go, and D, as well as libraries for these languages.



[GSL](https://www.gnu.org/software/gsl/) is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There are over 1000 functions in total with an extensive test suite.



[OpenGL Extension Wrangler Library (GLEW)](https://www.opengl.org/sdk/libs/GLEW/) is a cross-platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.



[Libtool](https://www.gnu.org/software/libtool/) is a generic library support script that hides the complexity of using shared libraries behind a consistent, portable interface. To use Libtool, add the new generic library building commands to your Makefile, Makefile.in, or Makefile.am.



[Maven](https://maven.apache.org/) is a software project management and comprehension tool. Based on the concept of a project object model (POM), Maven can manage a project's build, reporting and documentation from a central piece of information.



[TAU (Tuning And Analysis Utilities)](http://www.cs.uoregon.edu/research/tau/home.php) is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements as well as event-based sampling. All C++ language features are supported including templates and namespaces.



[Clang](https://clang.llvm.org/) is a production quality C, Objective-C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.



[OpenCV](https://opencv.org/) is a highly optimized library with focus on real-time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.



[Libcu++](https://nvidia.github.io/libcudacxx) is the NVIDIA C++ Standard Library for your entire system. It provides a heterogeneous implementation of the C++ Standard Library that can be used in and between CPU and GPU code.



[ANTLR (ANother Tool for Language Recognition)](https://www.antlr.org/) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It's widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface that makes it easy to respond to the recognition of phrases of interest.



[Oat++](https://oatpp.io/) is a light and powerful C++ web framework for highly scalable and resource-efficient web application. It's zero-dependency and easy-portable.



[JavaCPP](https://github.com/bytedeco/javacpp) is a program that provides efficient access to native C++ inside Java, not unlike the way some C/C++ compilers interact with assembly language. 



[Cython](https://cython.org/) is a language that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations such as calling C functions and declaring C types on variables and class attributes.



[Spdlog](https://github.com/gabime/spdlog) is a very fast, header-only/compiled, C++ logging library. 



[Infer](https://fbinfer.com/) is a static analysis tool for Java, C++, Objective-C, and C. Infer is written in [OCaml](https://ocaml.org/).



# Python Development



[Back to the Top](https://github.com/mikeroyal/CUDA-Guide#table-of-contents)





 

  


  




## Python Learning Resources



[Python](https://www.python.org) is an interpreted, high-level programming language. Python is used heavily in the fields of Data Science and Machine Learning. 



[Python Developer’s Guide](https://devguide.python.org) is a comprehensive resource for contributing to Python – for both new and experienced contributors. It is maintained by the same community that maintains Python. 



[Azure Functions Python developer guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference-python) is an introduction to developing Azure Functions using Python. The content below assumes that you've already read the [Azure Functions developers guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference).



[CheckiO](https://checkio.org/) is a programming learning platform and a gamified website that teaches Python through solving code challenges and competing for the most elegant and creative solutions.



[Python Institute](https://pythoninstitute.org)



[PCEP – Certified Entry-Level Python Programmer certification](https://pythoninstitute.org/pcep-certification-entry-level/)



[PCAP – Certified Associate in Python Programming certification](https://pythoninstitute.org/pcap-certification-associate/)



[PCPP – Certified Professional in Python Programming 1 certification](https://pythoninstitute.org/pcpp-certification-professional/)



[PCPP – Certified Professional in Python Programming 2](https://pythoninstitute.org/pcpp-certification-professional/)



[MTA: Introduction to Programming Using Python Certification](https://docs.microsoft.com/en-us/learn/certifications/mta-introduction-to-programming-using-python)



[Getting Started with Python in Visual Studio Code](https://code.visualstudio.com/docs/python/python-tutorial)



[Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html)



[Google's Python Education Class](https://developers.google.com/edu/python/)



[Real Python](https://realpython.com)



[The Python Open Source Computer Science Degree by Forrest Knight](https://github.com/ForrestKnight/open-source-cs-python)



[Intro to Python for Data Science](https://www.datacamp.com/courses/intro-to-python-for-data-science)



[Intro to Python by W3schools](https://www.w3schools.com/python/python_intro.asp)



[Codecademy's Python 3 course](https://www.codecademy.com/learn/learn-python-3)



[Learn Python with Online Courses and Classes from edX](https://www.edx.org/learn/python)



[Python Courses Online from Coursera](https://www.coursera.org/courses?query=python)



## Python Frameworks and Tools



[Python Package Index (PyPI)](https://pypi.org/) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community. 



[PyCharm](https://www.jetbrains.com/pycharm/) is the best IDE I've ever used. With PyCharm, you can access the command line, connect to a database, create a virtual environment, and manage your version control system all in one place, saving time by avoiding constantly switching between windows.



[Python Tools for Visual Studio(PTVS)](https://microsoft.github.io/PTVS/) is a free, open source plugin that turns Visual Studio into a Python IDE. It supports editing, browsing, IntelliSense, mixed Python/C++ debugging, remote Linux/MacOS debugging, profiling, IPython, and web development with Django and other frameworks.



[Pylance](https://github.com/microsoft/pylance-release) is an extension that works alongside Python in Visual Studio Code to provide performant language support. Under the hood, Pylance is powered by Pyright, Microsoft's static type checking tool.



[Pyright](https://github.com/Microsoft/pyright) is a fast type checker meant for large Python source bases. It can run in a “watch” mode and performs fast incremental updates when files are modified.



[Django](https://www.djangoproject.com/) is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.



[Flask](https://flask.palletsprojects.com/) is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. 

 

[Web2py](http://web2py.com/) is an open-source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.



[AWS Chalice](https://github.com/aws/chalice) is a framework for writing serverless apps in python. It allows you to quickly create and deploy applications that use AWS Lambda. 



[Tornado](https://www.tornadoweb.org/) is a Python web framework and asynchronous networking library. Tornado uses a non-blocking network I/O, which can scale to tens of thousands of open connections.



[HTTPie](https://github.com/httpie/httpie) is a command line HTTP client that makes CLI interaction with web services as easy as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers. 



[Scrapy](https://scrapy.org/) is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.



[Sentry](https://sentry.io/) is a service that helps you monitor and fix crashes in realtime. The server is in Python, but it contains a full API for sending events from any language, in any application.



[Pipenv](https://github.com/pypa/pipenv) is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world.



[Python Fire](https://github.com/google/python-fire) is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.



[Bottle](https://github.com/bottlepy/bottle) is a fast, simple and lightweight [WSGI](https://www.wsgi.org/) micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the [Python Standard Library](https://docs.python.org/library/).



[CherryPy](https://cherrypy.org) is a minimalist Python object-oriented HTTP web framework.



[Sanic](https://github.com/huge-success/sanic) is a Python 3.6+ web server and web framework that's written to go fast. 



[Pyramid](https://trypyramid.com) is a small and fast open source Python web framework. It makes real-world web application development and deployment more fun and more productive.



[TurboGears](https://turbogears.org) is a hybrid web framework able to act both as a Full Stack framework or as a Microframework. 



[Falcon](https://falconframework.org/) is a reliable, high-performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.



[Neural Network Intelligence(NNI)](https://github.com/microsoft/nni) is an open source AutoML toolkit for automate machine learning lifecycle, including [Feature Engineering](https://github.com/microsoft/nni/blob/master/docs/en_US/FeatureEngineering/Overview.md), [Neural Architecture Search](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/Overview.md), [Model Compression](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/Overview.md) and [Hyperparameter Tuning](https://github.com/microsoft/nni/blob/master/docs/en_US/Tuner/BuiltinTuner.md).



[Dash](https://plotly.com/dash) is a popular Python framework for building ML & data science web apps for Python, R, Julia, and Jupyter.



[Luigi](https://github.com/spotify/luigi) is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built-in.



[Locust](https://github.com/locustio/locust) is an easy to use, scriptable and scalable performance testing tool. 



[spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython. 



[NumPy](https://www.numpy.org/) is the fundamental package needed for scientific computing with Python.



[Pillow](https://python-pillow.org/) is a friendly PIL(Python Imaging Library) fork.



[IPython](https://ipython.org/) is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history.



[GraphLab Create](https://turi.com/) is a Python library, backed by a C++ engine, for quickly building large-scale, high-performance machine learning models.



[Pandas](https://pandas.pydata.org/) is a fast, powerful, and easy to use open source data structrures, data analysis and manipulation tool, built on top of the Python programming language.



[PuLP](https://coin-or.github.io/pulp/) is an Linear Programming modeler written in python. PuLP can generate LP files and call on use highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI, to solve these linear problems.



[Matplotlib](https://matplotlib.org/) is a 2D plotting library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.



[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.



## Contribute



- [x] If would you like to contribute to this guide simply make a [Pull Request](https://github.com/mikeroyal/CUDA-Guide/pulls).



## License

[Back to the Top](https://github.com/mikeroyal/CUDA-Guide#table-of-contents)



Distributed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) Public License](https://creativecommons.org/licenses/by/4.0/).