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Development Schedule

Release Module Feature Status
0.1 Sep 2015 Neural Network Feed forward neural network, including CNN, MLP done
RBM-like model, including RBM done
Recurrent neural network, including standard RNN done
Architecture One worker group on single node (with data partition) done
Multi worker groups on single node using Hogwild done
Distributed Hogwild done
Multi groups across nodes, like Downpour done
All-Reduce training architecture like DeepImage done
Load-balance among servers done
Failure recovery Checkpoint and restore done
Tools Installation with GNU auto tools done
0.2 Jan 2016 Neural Network Feed forward neural network, including AlexNet, cuDNN layers, etc. done
Recurrent neural network, including GRULayer and BPTT done
Model partition and hybrid partition done
Tools Integration with Mesos for resource management done
Prepare Docker images for deployment done
Visualization of neural net and debug information done
Binding Python binding for major components done
GPU Single node with multiple GPUs done
0.3 April 2016 GPU Multiple nodes, each with multiple GPUs done
Heterogeneous training using both GPU and CPU CcT done
Support cuDNN v4 done
Installation Remove dependency on ZeroMQ, CZMQ, Zookeeper for single node training done
Updater Add new SGD updaters including Adam, AdamMax and AdaDelta done
Binding Enhance Python binding for training done
0.4 June 2016 Rafiki Deep learning as a service
Product search using Rafiki
1.0 July 2016 Programming abstraction Tensor with linear algebra, neural net and random operations
Updater for distributed parameter updating
Optimization Execution and memory optimization
Hardware Use Cuda and Cudnn for Nvidia GPU
Use OpenCL for AMD GPU or other devices
Cross-platform To extend from Linux to MacOS and Windows
Examples Speech recognition example
Large image models, e.g., GoogLeNet, VGG and Residual Net