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Torchnet: An Open-Source Platform for (Deep) Learning Research

Ronan Collobert, Laurens van der Maaten, Armand Joulin
Facebook AI Research, 1 Hacker Way, Menlo Park CA 94025 / 770 Broadway, New York NY 10003, USA
33rd International Conference on Machine Learning (ICML), 2016
@article{collobert2016torchnet,

   title={Torchnet: An Open-Source Platform for (Deep) Learning Research},

   author={Collobert, Ronan and Maaten, Laurens van der and Joulin, Armand},

   year={2016}

}

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Torch 7 is a scientific computing platform that supports both CPU and GPU computation, has a light-weight wrapper in a simple scripting language, and provides fast implementations of common algebraic operations. It has become one of the main frameworks for research in (deep) machine learning. Torch does, however, not provide abstractions and boilerplate code for machine-learning experiments. As a result, researchers repeatedly re-implement experimentation logics that are not interoperable. We introduce Torchnet: an open-source framework that provides abstractions and boilerplate logic for machine learning. It encourages modular programming and code re-use, which reduces the chance of bugs, and it makes it straightforward to use asynchronous data loading and efficient multi-GPU computations. Torchnet is written in pure Lua, which makes it easy to install on any architecture with a Torch installation. We envision Torchnet to become a platform to which the community contributes via plugins.
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