Facebook宣布开源用于简化深度学习、加速开发的软件Torchnet。 - 用Lua编写
- 提供样板代码、关键抽象和参考实现，可以聚合分拆重用，以模块化编程减少bug概率
- 简化异步、并行数据loading，提升多GPU效率
- 可能不会一直局限于Torch，其抽象将有Caffe、TensorFlow实现
- 方法有点类似于Theano框架的Blocks和Fuel库
- 已经应用于Facebook图像识别和NLP
Facebook工程师Laurens van der Maaten表示，Torchnet的核心不是让Torch更快（而是简化深度学习工作），举例而言它可以减少IO开销，这对大型神经网络尤其重要。 The goal of open-sourcing Torchnet is to empower the developer community, allowing it to rapidly build effective and reusable learning systems.
与Caffe、Chainer、TensorFlow和Theano等深度学习框架的一个很大的不同，是不注重深层网络中高效率的推理和梯度计算，Torchnet提供一个深度学习框架之上的框架（如torch/nn），使得rapid experimentation更容易。 Torchnet provides a collection of subpackages and implements five main types of abstractions: - Datasets — provide a size function that returns the number of samples in the data set, and a get(idx) function that returns the idx-th sample in the data set.
- Dataset Iterators — a simple for loop that runs from one to the data set size and calls the get() function with loop value as input.
- Engines — provides the boilerplate logic necessary for training and testing models.
Meter — used for performance measurements, such as the time needed to perform a training epoch or the value of the loss function averaged over all examples. Logs — for logging experiments.
The most important subpackages provide implementations of boilerplate code that is relevant to machine-learning problems. These include computer vision, natural language processing, and speech processing.
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