Data pipeline framework for machine learning
Fuel provides your machine learning models with the data they need to learn.
- Interfaces to common datasets such as MNIST, CIFAR-10 (image datasets), Google’s One Billion Words (text), and many more
- The ability to iterate over your data in a variety of ways, such as in minibatches with shuffled/sequential examples
- A pipeline of preprocessors that allow you to edit your data on-the-fly, for example by adding noise, extracting n-grams from sentences, extracting patches from images, etc.
- Ensure that the entire pipeline is serializable with pickle; this is a requirement for being able to checkpoint and resume long-running experiments. For this, we rely heavily on the picklable_itertools library.
Fuel is developed primarily for use by Blocks, a Theano toolkit that helps you train neural networks.
If you have questions, don’t hesitate to write to the mailing list.
- Citing Fuel
If you use Blocks or Fuel in your work, we’d really appreciate it if you could cite the following paper:
Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, and Yoshua Bengio, “Blocks and Fuel: Frameworks for deep learning,” arXiv preprint arXiv:1506.00619 [cs.LG], 2015.
- Please see the documentation for more information.
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