Open source Torchnet helps researchers and developers build rapid and reusable prototypes of learning systems in Torch.
Building rapid and clean prototypes for deep machine-learning operations can now take a big step forward with Torchnet, a new software toolkit that fosters rapid and collaborative development of deep learning experiments by the Torch community.
Introduced and open-sourced this week at the International Conference on Machine Learning (ICML) in New York, Torchnet provides a collection of boilerplate code, key abstractions, and reference implementations that can be snapped together or taken apart and then later reused, substantially speeding development. It encourages a modular programming approach, reducing the chance of bugs while making it easy to use asynchronous, parallel data loading and efficient multi-GPU computations.
The new toolkit builds on the success of Torch, a framework for building deep learning models by providing fast implementations of common algebraic operations on both CPU (via OpenMP/SSE) and GPU (via CUDA).
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