Skip to main content
Python Software Foundation 20th Year Anniversary Fundraiser  Donate today!

The RWTH extensible training framework for universal recurrent neural networks

Project description

GitHub repository. RETURNN paper 2016, RETURNN paper 2018.

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 slides of the Interspeech 2020 tutorial “Efficient and Flexible Implementation of Machine Learning for ASR and MT” 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. See basic usage and technological overview.

Here is the video recording of a RETURNN overview talk (slides, exercise sheet; hosted by eBay).

There are many example demos which work on artificially generated data, i.e. they should work as-is.

There are some real-world examples such as setups for speech recognition on the Switchboard or LibriSpeech corpus.

Some benchmark setups against other frameworks can be found here. The results are in the RETURNN paper 2016. Performance benchmarks of our LSTM kernel vs CuDNN and other TensorFlow kernels are in TensorFlow LSTM benchmark.

There is also a wiki. Questions can also be asked on StackOverflow using the RETURNN tag.

https://github.com/rwth-i6/returnn/workflows/CI/badge.svg

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for returnn, version 1.20210613.011437
Filename, size File type Python version Upload date Hashes
Filename, size returnn-1.20210613.011437.tar.gz (1.8 MB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page