Papperoni

2011

Torch7: A Matlab-Like Environment for Machine Learning

Clement Farabet

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This paper introduces Torch7, a versatile numeric computing framework and machine learning library that extends Lua, offering a flexible environment for designing and training learning machines, leveraging OpenMP/SSE and CUDA implementations for high performance.

Main Contributions

Introduces Torch7, a flexible numeric computing framework and machine learning library that extends Lua.
Achieves high performance through efficient OpenMP/SSE and CUDA implementations of low-level numeric routines.
Provides a convenient scripting language interface using Lua for designing numerical algorithms.
Demonstrates the efficiency of Torch7 through benchmarks, comparing its performance against Theano on various neural network architectures.
Offers two ways of parallelization: OpenMP and CUDA.

Abstract

Torch7 is a versatile numeric computing framework and machine learning library that extends Lua. Its goal is to provide a flexible environment to design and train learning machines. Flexibility is obtained via Lua, an extremely lightweight scripting language. High performance is obtained via efficient OpenMP/SSE and CUDA implementations of low-level numeric routines. Torch7 can easily be interfaced to third-party software thanks to Lua's light interface.

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References [3]

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[1]Theano: A CPU and GPU Math Expression Compiler

James Bergstra, O. Breuleux, F. Bastien, P. Lamblin, Razvan Pascanu, G. Desjardins, J. Turian, D. W. Farley, Yoshua Bengio - 2010

22 papers in library cite

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Very nice framework. Symbolic programming is very nice. However, I think that this had very little impact and was mostly used by Bengio's lab.

[2]Sn: A Simulator for Connectionist Models

L. Y. Bottou, Yann Lecun - 1988

5 papers in library cite

Google Scholar

[3]Lush Reference Manual

Yann Lecun, Leon Bottou - 2002

1 paper in library cites

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on July 25, 2025

Very nice paper! I am impressed that they used Lua, and the API sounds very simple. Plus, it sounds like they didn't even try that hard to get good performance (vs. Theano which was very tryhard)