Apache SINGA 0.2.0 发布分布式深度学习平台

Apache SINGA 0.2.0 发布，此版本主要更新内容如下：

Training on GPU enables training of complex models on a single node with multiple GPU cards.
Hybrid neural net partitioning supports data and model parallelism at the same time.
Python wrapper makes it easy to configure the job, including neural net and SGD algorithm.
RNN model and BPTT algorithm are implemented to support applications based on RNN models, e.g., GRU.
Cloud software integration includes Mesos, Docker and HDFS.
Visualization of neural net structure and layer information, which is helpful for debugging.
Linear algebra functions and random functions against Blobs and raw data pointers.
New layers, including SoftmaxLayer, ArgSortLayer, DummyLayer, RNN layers and cuDNN layers.
Update Layer class to carry multiple data/grad Blobs.
Extract features and test performance for new data by loading previously trained model parameters.
Add Store class for IO operations.

Apache SINGA 是 Apache 在 2015 年 3 月 17 日接纳的一个孵化项目，是个分布式深度学习平台。

SINGA 是基于大型数据集训练大型深度学习模块的常规分布式学习平台。SINGA 支持各种流行的深度学习模块，其中的 feed-forward 模块包括 convolutional neural networks (CNN)，能量模块 restricted Boltzmann machine (RBM) 和 recurrent neural networks (RNN)。

SGD 流：