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+# 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 PyTorch/TensorFlow-based implementation of modern neural network architectures.
+It is optimized for fast and reliable training of 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).
+
+[![CI](https://github.com/rwth-i6/returnn/workflows/CI/badge.svg)](https://github.com/rwth-i6/returnn/actions)
+
+## Dependencies
+
+pip dependencies are listed in `requirements.txt` and `requirements-dev`, although some parts of the code may require additional dependencies (e.g. `librosa`, `resampy`) on-demand.
+
+RETURNN supports Python >= 3.8. Bumps to the minimum Python version are listed in [`CHANGELOG.md`](https://github.com/rwth-i6/returnn/blob/master/CHANGELOG.md).