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For example, TensorFlow Recommenders and TensorFlow Graphics are libraries for their respective functionalities in recommendation systems and graphics, TensorFlow Federated provides a framework for decentralized data, and TensorFlow Cloud allows users to directly interact with Google Cloud to integrate their local code to Google Cloud. [68]
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Up until version 2.3, Keras supported multiple backends, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. [7] [8] [9] As of version 2.4, only TensorFlow was supported. Starting with version 3.0 (as well as its preview version, Keras Core), however, Keras has become multi-backend again, supporting TensorFlow, JAX, and ...
It is also used for the Blender user manual [10] and Python API documentation. [11] In 2010, Eric Holscher announced [12] the creation of the Read the Docs project as part of an effort to make maintenance of software documentation easier. Read the Docs automates the process of building and uploading Sphinx documentation after every commit.
Printing system can render any document to a PDF file, thus any Linux program with print capability can produce PDF files Pdftk: GPLv2: No Yes Yes Command-line tools to merge, split, en-/decrypt, watermark/stamp and manipulate PDF document files. Front end to an older version of the iText library. poppler: GNU GPL: Yes Yes
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
reStructuredText (RST, ReST, or reST) is a file format for textual data used primarily in the Python programming language community for technical documentation.. It is part of the Docutils project of the Python Doc-SIG (Documentation Special Interest Group), aimed at creating a set of tools for Python similar to Javadoc for Java or Plain Old Documentation (POD) for Perl.
PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms. [25] [26]