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| <div class="section" id="development-schedule"> |
| <h1>Development Schedule<a class="headerlink" href="#development-schedule" title="Permalink to this headline">¶</a></h1> |
| <table border="1" class="docutils"> |
| <colgroup> |
| <col width="33%" /> |
| <col width="33%" /> |
| <col width="33%" /> |
| </colgroup> |
| <thead valign="bottom"> |
| <tr class="row-odd"><th class="head">Release</th> |
| <th class="head">Module</th> |
| <th class="head">Feature</th> |
| </tr> |
| </thead> |
| <tbody valign="top"> |
| <tr class="row-even"><td>v0.1 Sep 2015</td> |
| <td>Neural Network</td> |
| <td>Feed forward neural network, including CNN, MLP</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td> </td> |
| <td>RBM-like model, including RBM</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Recurrent neural network, including standard RNN</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td>Architecture</td> |
| <td>One worker group on single node (with data partition)</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Multi worker groups on single node using <a class="reference external" href="http://www.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf">Hogwild</a></td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td> </td> |
| <td>Distributed Hogwild</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Multi groups across nodes, like <a class="reference external" href="http://papers.nips.cc/paper/4687-large-scale-distributed-deep-networks">Downpour</a></td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td> </td> |
| <td>All-Reduce training architecture like <a class="reference external" href="http://arxiv.org/abs/1501.02876">DeepImage</a></td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Load-balance among servers</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td>Failure recovery</td> |
| <td>Checkpoint and restore</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td>Tools</td> |
| <td>Installation with GNU auto Tools</td> |
| </tr> |
| <tr class="row-odd"><td>v0.2 Jan 2016</td> |
| <td>Neural Network</td> |
| <td>Feed forward neural network, including AlexNet, cuDNN layers,Tools</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Recurrent neural network, including GRULayer and BPTT</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td> </td> |
| <td>Model partition and hybrid partition</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td>Tools</td> |
| <td>Integration with Mesos for resource management</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td> </td> |
| <td>Prepare Docker images for deployment</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Visualization of neural net and debug information</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td>Binding</td> |
| <td>Python binding for major components</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td>GPU</td> |
| <td>Single node with multiple GPUs</td> |
| </tr> |
| <tr class="row-odd"><td>v0.3 April 2016</td> |
| <td>GPU</td> |
| <td>Multiple nodes, each with multiple GPUs</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Heterogeneous training using both GPU and CPU <a class="reference external" href="http://arxiv.org/abs/1504.04343">CcT</a></td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td> </td> |
| <td>Support cuDNN v4</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td>Installation</td> |
| <td>Remove dependency on ZeroMQ, CZMQ, Zookeeper for single node training</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td>Updater</td> |
| <td>Add new SGD updaters including Adam, AdamMax and AdaDelta</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td>Binding</td> |
| <td>Enhance Python binding for training</td> |
| </tr> |
| <tr class="row-odd"><td>v1.0 Sep 2016</td> |
| <td>Programming abstraction</td> |
| <td>Tensor with linear algebra, neural net and random operations</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Updater for distributed parameter updating</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td>Hardware</td> |
| <td>Use Cuda and Cudnn for Nvidia GPU</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Use OpenCL for AMD GPU or other devices</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td>Cross-platform</td> |
| <td>To extend from Linux to MacOS</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Large image models, e.g., <a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">VGG</a> and <a class="reference external" href="http://arxiv.org/abs/1512.03385">Residual Net</a></td> |
| </tr> |
| <tr class="row-odd"><td>v1.1 Jan 2017</td> |
| <td>Model Zoo</td> |
| <td>GoogleNet; Health-care models</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td>Caffe converter</td> |
| <td>Use SINGA to train models configured in caffe proto files</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td>Model components</td> |
| <td>Add concat and slice layers; accept multiple inputs to the net</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td>Compilation and installation</td> |
| <td>Windows suppport</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td> </td> |
| <td>Simplify the installation by compiling protobuf and openblas together with SINGA</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td> </td> |
| <td>Build python wheel automatically using Jenkins</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td> </td> |
| <td>Install SINGA from Debian packages</td> |
| </tr> |
| <tr class="row-even"><td>v1.2 June 2018</td> |
| <td>AutoGrad</td> |
| <td>AutoGrad for BP</td> |
| </tr> |
| <tr class="row-odd"><td> </td> |
| <td>Python 3</td> |
| <td>Support Python 3 for PySinga</td> |
| </tr> |
| <tr class="row-even"><td> </td> |
| <td>Models</td> |
| <td>Add popular models, including VGG, ResNet, DenseNet, InceptionNet</td> |
| </tr> |
| </tbody> |
| </table> |
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