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Werkzeug (German for "tool") is a utility library for the Python programming language for Web Server Gateway Interface (WSGI) applications. Werkzeug can instantiate objects for request, response, and utility functions. It can be used as the basis for a custom software framework and supports Python 2.7 and 3.5 and later. [20] [21]
Boost Software License: Yes Cross-platform: C++: C++, Python: Yes No Yes No Yes Yes No Yes Yes Yes Yes Flux: Mike Innes 2017 MIT license: Yes Linux, MacOS, Windows (Cross-platform) Julia: Julia: Yes No Yes Yes [13] Yes Yes No Yes Yes Intel Data Analytics Acceleration Library: Intel 2015 Apache License 2.0: Yes Linux, macOS, Windows on Intel CPU ...
This is a comparison of notable web frameworks, software used to build ... Base One Foundation Component Library (BFC) 7.51 ... Form validation framework(s) Python 3
This is a list of notable library packages implementing a graphical user interface (GUI) platform-independent GUI library (PIGUI). These can be used to develop software that can be ported to multiple computing platforms with no change to its source code.
For example, many frameworks provide libraries for database access, template processing and session management, and often promote code reuse. Though they often target development of dynamic websites they are also applicable to static websites. This category lists web frameworks written in and/or designed for use with Python.
Software frameworks may include support programs, compilers, code libraries, toolsets, and application programming interfaces (APIs) that bring together all the different components to enable development of a project or system. Frameworks have key distinguishing features that separate them from normal libraries:
In computer science, a library is a collection of resources that is leveraged during software development to implement a computer program. Historically, a library consisted of subroutines (generally called functions today).
Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It aims to be scalable, allows fast model training, and supports a flexible programming model and multiple programming languages (including C++, Python, Java, Julia, MATLAB, JavaScript, Go, R, Scala, Perl, and Wolfram Language).