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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 ...
In addition, the package solves Linear programming (LP), Quadratic programming (QP), Quadratically constrained quadratic program (QCQP), Nonlinear programming (NLP), Mixed integer programming (MIP), and Mixed integer linear programming (MILP). GEKKO is available in Python and installed with pip from PyPI of the Python Software Foundation.
The problem is then solved in Python by first installing the APMonitor package with pip install APMonitor or from the following Python code. # Install APMonitor import pip pip . main ([ "install" , "APMonitor" ])
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.
The {{#invoke: params}} module is designed to be adopted by those templates that want to have a deep control of their parameters. It is particularly useful to variadic templates, to which it offers the possibility to count, list, map and propagate the parameters received without knowing their number in advance.
A package manager or package-management system is a collection of software tools that automates the process of installing, upgrading, configuring, and removing computer programs for a computer in a consistent manner. [1] A package manager deals with packages, distributions of software and data in archive files.
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It includes the Zero Redundancy Optimizer (ZeRO) for training models with 1 trillion or more parameters. [4] Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5]