Lighting the way to deep machine learning

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).