Revert "convert readme to markdown, add note about min python versions"

This reverts commit c00d1f5.

Author: NeoLegends

README.md

@@ -0,0 +1,74 @@
+==================
+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.
+
+The high-level features and goals of RETURNN are:
+
+* **Simplicity**
+
+  * Writing config / code is simple & straight-forward (setting up experiment, defining model)
+  * Debugging in case of problems is simple
+  * Reading config / code is simple (defined model, training, decoding all becomes clear)
+
+* **Flexibility**
+
+  * Allow for many different kinds of experiments / models
+
+* **Efficiency**
+
+  * Training speed
+  * Decoding speed
+
+All items are important for research, decoding speed is esp. important for production.
+
+See our `Interspeech 2020 tutorial "Efficient and Flexible Implementation of Machine Learning for ASR and MT" video <https://www.youtube.com/watch?v=wPKdYqSOlAY>`__
+(`slides <https://www-i6.informatik.rwth-aachen.de/publications/download/1154/Zeyer--2020.pdf>`__)
+with an introduction of the core concepts.
+
+More specific 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 <https://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

README.rst

@@ -87,8 +87,8 @@ def main():
         author_email="[email protected]",
         url="https://github.com/rwth-i6/returnn/",
         license="RETURNN license",
-        long_description=open("README.md").read(),
-        long_description_content_type="text/markdown",
+        long_description=open("README.rst").read(),
+        long_description_content_type="text/x-rst",
         # https://pypi.python.org/pypi?%3Aaction=list_classifiers
         classifiers=[
             "Development Status :: 5 - Production/Stable",