Metadata-Version: 1.1
Name: returnn
Version: 1.20201126.234536
Summary: The RWTH extensible training framework for universal recurrent neural networks
Home-page: https://github.com/rwth-i6/returnn/
Author: Albert Zeyer
Author-email: albzey@gmail.com
License: RETURNN license
Description: ==================
        Welcome to RETURNN
        ==================
        
        `GitHub repository <https://github.com/rwth-i6/returnn>`__.
        `RETURNN paper 2016 <https://arxiv.org/abs/1608.00895>`_,
        `RETURNN paper 2018 <https://arxiv.org/abs/1805.05225>`_.
        
        RETURNN - RWTH extensible training framework for universal recurrent neural networks,
        is a Theano/TensorFlow-based implementation of modern recurrent neural network architectures.
        It is optimized for fast and reliable training of recurrent neural networks in a multi-GPU environment.
        
        Features include:
        
        - Mini-batch training of feed-forward neural networks
        - Sequence-chunking based batch training for recurrent neural networks
        - Long short-term memory recurrent neural networks
          including our own fast CUDA kernel
        - Multidimensional LSTM (GPU only, there is no CPU version)
        - Memory management for large data sets
        - Work distribution across multiple devices
        - Flexible and fast architecture which allows all kinds of encoder-attention-decoder models
        
        See `documentation <http://returnn.readthedocs.io/>`__.
        See `basic usage <https://returnn.readthedocs.io/en/latest/basic_usage.html>`__
        and `technological overview <https://returnn.readthedocs.io/en/latest/tech_overview.html>`__.
        
        `Here is the video recording of a RETURNN overview talk <https://www-i6.informatik.rwth-aachen.de/web/Software/returnn/downloads/workshop-2019-01-29/01.recording.cut.mp4>`_
        (`slides <https://www-i6.informatik.rwth-aachen.de/web/Software/returnn/downloads/workshop-2019-01-29/01.returnn-overview.session1.handout.v1.pdf>`__,
        `exercise sheet <https://www-i6.informatik.rwth-aachen.de/web/Software/returnn/downloads/workshop-2019-01-29/01.exercise_sheet.pdf>`__;
        hosted by eBay).
        
        There are `many example demos <https://github.com/rwth-i6/returnn/blob/master/demos/>`_
        which work on artificially generated data,
        i.e. they should work as-is.
        
        There are `some real-world examples <https://github.com/rwth-i6/returnn-experiments>`_
        such as setups for speech recognition on the Switchboard or LibriSpeech corpus.
        
        Some benchmark setups against other frameworks
        can be found `here <https://github.com/rwth-i6/returnn-benchmarks>`_.
        The results are in the `RETURNN paper 2016 <https://arxiv.org/abs/1608.00895>`_.
        Performance benchmarks of our LSTM kernel vs CuDNN and other TensorFlow kernels
        are in `TensorFlow LSTM benchmark <https://returnn.readthedocs.io/en/latest/tf_lstm_benchmark.html>`__.
        
        There is also `a wiki <https://github.com/rwth-i6/returnn/wiki>`_.
        Questions can also be asked on
        `StackOverflow using the RETURNN tag <https://stackoverflow.com/questions/tagged/returnn>`_.
        
        .. image:: https://github.com/rwth-i6/returnn/workflows/CI/badge.svg
            :target: https://github.com/rwth-i6/returnn/actions
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Environment :: GPU
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
