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        "author": "Albert Zeyer",
        "author_email": "albzey@gmail.com",
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            "Intended Audience :: Developers",
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            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
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        "description": "==================\nWelcome to RETURNN\n==================\n\n`GitHub repository <https://github.com/rwth-i6/returnn>`__.\n`RETURNN paper 2016 <https://arxiv.org/abs/1608.00895>`_,\n`RETURNN paper 2018 <https://arxiv.org/abs/1805.05225>`_.\n\nRETURNN - RWTH extensible training framework for universal recurrent neural networks,\nis a Theano/TensorFlow-based implementation of modern recurrent neural network architectures.\nIt is optimized for fast and reliable training of recurrent neural networks in a multi-GPU environment.\n\nFeatures include:\n\n- Mini-batch training of feed-forward neural networks\n- Sequence-chunking based batch training for recurrent neural networks\n- Long short-term memory recurrent neural networks\n  including our own fast CUDA kernel\n- Multidimensional LSTM (GPU only, there is no CPU version)\n- Memory management for large data sets\n- Work distribution across multiple devices\n- Flexible and fast architecture which allows all kinds of encoder-attention-decoder models\n\nSee `documentation <http://returnn.readthedocs.io/>`__.\nSee `basic usage <https://returnn.readthedocs.io/en/latest/basic_usage.html>`__\nand `technological overview <https://returnn.readthedocs.io/en/latest/tech_overview.html>`__.\n\n`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>`_\n(`slides <https://www-i6.informatik.rwth-aachen.de/web/Software/returnn/downloads/workshop-2019-01-29/01.returnn-overview.session1.handout.v1.pdf>`__,\n`exercise sheet <https://www-i6.informatik.rwth-aachen.de/web/Software/returnn/downloads/workshop-2019-01-29/01.exercise_sheet.pdf>`__;\nhosted by eBay).\n\nThere are `many example demos <https://github.com/rwth-i6/returnn/blob/master/demos/>`_\nwhich work on artificially generated data,\ni.e. they should work as-is.\n\nThere are `some real-world examples <https://github.com/rwth-i6/returnn-experiments>`_\nsuch as setups for speech recognition on the Switchboard or LibriSpeech corpus.\n\nSome benchmark setups against other frameworks\ncan be found `here <https://github.com/rwth-i6/returnn-benchmarks>`_.\nThe results are in the `RETURNN paper 2016 <https://arxiv.org/abs/1608.00895>`_.\nPerformance benchmarks of our LSTM kernel vs CuDNN and other TensorFlow kernels\nare in `TensorFlow LSTM benchmark <https://returnn.readthedocs.io/en/latest/tf_lstm_benchmark.html>`__.\n\nThere is also `a wiki <https://github.com/rwth-i6/returnn/wiki>`_.\nQuestions can also be asked on\n`StackOverflow using the RETURNN tag <https://stackoverflow.com/questions/tagged/returnn>`_.\n\n.. image:: https://travis-ci.org/rwth-i6/returnn.svg?branch=master\n    :target: https://travis-ci.org/rwth-i6/returnn\n",
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        "license": "RETURNN license",
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        "name": "returnn",
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