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  • v0.2.0 (14 January 2016):
    • Apache SINGA 0.2.0 [MD5] [KEYS]
    • Release Notes 0.2.0
    • New features and major updates,
      • 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.
  • v0.1.0 (8 October 2015):
    • Apache SINGA 0.1.0 [MD5] [KEYS]
    • Amazon EC2 image
    • Release Notes 0.1.0
    • Major features include,
      • Installation using GNU build utility
      • Scripts for job management with zookeeper
      • Programming model based on NeuralNet and Layer abstractions.
      • System architecture based on Worker, Server and Stub.
      • Training models from three different model categories, namely, feed-forward models, energy models and RNN models.
      • Synchronous and asynchronous distributed training frameworks using CPU
      • Checkpoint and restore
      • Unit test using gtest


Apache SINGA is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the name of Apache Incubator PMC. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.