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An output of pip install virtualenv. Pip's command-line interface allows the install of Python software packages by issuing a command: pip install some-package-name. Users can also remove the package by issuing a command: pip uninstall some-package-name. pip has a feature to manage full lists of packages and corresponding version numbers ...
TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [citation needed] Its flexible architecture allows for easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.
Kolab is a free and open source groupware suite. It consists of the Kolab server and a wide variety of Kolab clients, including KDE PIM-Suite Kontact, Roundcube web frontend, Mozilla Thunderbird and Mozilla Lightning with SyncKolab extension and Microsoft Outlook with proprietary Kolab-Connector PlugIns.
Some package managers, including pip, use PyPI as the default source for packages and their dependencies. [6] [7] As of 6 May 2024, more than 530,000 Python packages are available. PyPI primarily hosts Python packages in the form of source archives, called "sdists", or of "wheels" [8] that may contain binary modules from a compiled language.
Anaconda is a free and open-source system installer for Linux distributions.. Anaconda is used by Red Hat Enterprise Linux, Oracle Linux, Scientific Linux, Rocky Linux, AlmaLinux, CentOS, MIRACLE LINUX, Qubes OS, Fedora, Sabayon Linux and BLAG Linux and GNU, also in some less known and discontinued distros like Progeny Componentized Linux, Asianux, Foresight Linux, Rpath Linux and VidaLinux.
It is available for Windows, Mac OS X and Linux. The latest version of PIL is 1.1.7, was released in September 2009 and supports Python 1.5.2–2.7. [3] Development of the original project, known as PIL, was discontinued in 2011. [2] Subsequently, a successor project named Pillow forked the PIL repository and added Python 3.x support. [4]
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3]
SciPy (pronounced / ˈ s aɪ p aɪ / "sigh pie" [2]) is a free and open-source Python library used for scientific computing and technical computing. [3]SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.