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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 ...
Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface. [ 14 ] A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot , [ 15 ] Uber 's Pyro, [ 16 ] Hugging Face 's Transformers, [ 17 ] PyTorch Lightning , [ 18 ] [ 19 ] and Catalyst.
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]
PyTorch Lightning is an open-source Python library that provides a high-level interface for PyTorch, a popular deep learning framework. [1] It is a lightweight and high-performance framework that organizes PyTorch code to decouple research from engineering, thus making deep learning experiments easier to read and reproduce.
While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). [18] TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [citation needed]
CUDA is designed to work with programming languages such as C, C++, Fortran and Python. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL , which require advanced skills in graphics programming. [ 7 ]
Contrastive Language-Image Pre-training (CLIP) is a technique for training a pair of neural network models, one for image understanding and one for text understanding, using a contrastive objective. [1]
To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in 2011 with NumPy version 1.5.0. [15] In 2011, PyPy started development on an implementation of the NumPy API for PyPy. [16] As of 2023, it is not yet fully compatible with NumPy. [17]