Hi, We are pleased to announce that Apache SINGA (incubating) 0.2.0 is released.
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. SINGA supports a wide variety of popular deep learning models. The release is available at: http://singa.apache.org/downloads.html The main features of this release include * Training on GPU -- enabling training of complex models on a single node with multiple GPU cards. * Hybrid neural net partitioning -- supporting data and model parallelism at the same time. * Python wrapper -- making it easier to configure jobs, including neural net and SGD algorithm. * RNN model and BPTT algorithm -- supporting applications based on RNN models, e.g., GRU. * Cloud software integration, including Mesos, Docker and HDFS. * Visualization of neural net structure and layer information -- 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 -- for carrying multiple data/grad Blobs. * Extract features and test performance for new data by loading previously trained model parameters. * Add Store class for IO operations We look forward to hearing your feedbacks, suggestions, and contributions to the project (http://singa.apache.org/develop/schedule.html). On behalf of the SINGA team, Wei Wang
