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 <h1>开发时间表<a class="headerlink" href="#id1" 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">版本</th>
 <th class="head">模块</th>
 <th class="head">特性</th>
 </tr>
 </thead>
 <tbody valign="top">
 <tr class="row-even"><td>v0.1 2015 九月</td>
 <td>神经网络</td>
 <td>前向传播神经网络, 包括 CNN, MLP</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>&#160;</td>
 <td>类 RBM 模型, 包括 RBM</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>循环神经网络, 包括标准 RNN</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>架构</td>
 <td>在单节点运行一个工作组 (包括划分)</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>在单节点运行多个工作组, 用 <a class="reference external" href="http://www.eecs.berkeley.edu/~brecht/papers/hogwildTR.pdf">Hogwild</a></td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>&#160;</td>
 <td>分布式 Hogwild</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>跨多节点运行多个工作组 , 如 <a class="reference external" href="http://papers.nips.cc/paper/4687-large-scale-distritbuted-deep-networks">Downpour</a></td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>&#160;</td>
 <td>All-Reduce 训练架构如 <a class="reference external" href="http://arxiv.org/abs/1501.02876">DeepImage</a></td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>服务器间负载均衡</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>失败恢复</td>
 <td>检查点和恢复</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>工具</td>
 <td>用 GNU 自动工具安装</td>
 </tr>
 <tr class="row-odd"><td>v0.2 2016 一月</td>
 <td>神经网络</td>
 <td>前向传播神经网络, 包括 AlexNet, cuDNN 层, 工具</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>循环神经网络, 包括 GRU 层和 BPTT</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>&#160;</td>
 <td>模型划分和混合划分</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>工具</td>
 <td>融合 Mesos 资源管理</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>&#160;</td>
 <td>准备部署 Docker images</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>可视化神经网络和调试信息</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>绑定</td>
 <td>主要组件 Python 绑定</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>GPU</td>
 <td>单节点多个 GPU</td>
 </tr>
 <tr class="row-odd"><td>v0.3 2016 四月</td>
 <td>GPU</td>
 <td>多个节点, 每个包含多个 GPU</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>用 GPU 和 CPU 混合训练  <a class="reference external" href="http://arxiv.org/abs/1504.04343">CcT</a></td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>&#160;</td>
 <td>支持 cuDNN v4</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>安装</td>
 <td>删除 ZeroMQ, CZMQ 依赖, 单节点训练 zookeeper</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>优化器</td>
 <td>添加新的 SGD 优化器，包括 Adam, AdamMax 和 AdaDelta</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>绑定</td>
 <td>增强 Python 绑定训练</td>
 </tr>
 <tr class="row-odd"><td>v1.0 2016 九月</td>
 <td>模型抽象</td>
 <td>Tensor 基于线性代数, 神经网络和随机运算</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>分布式参数更新优化器</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>硬件</td>
 <td>使用 Cuda 和 Cudnn for Nvidia GPU</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>使用 OpenCL for AMD GPU 及其他设备</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>跨平台</td>
 <td>从 Linux 扩展到 MacOS</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>大型图像模型, 例如, <a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">VGG</a> 和 <a class="reference external" href="http://arxiv.org/abs/1512.03385">Residual Net</a></td>
 </tr>
 <tr class="row-odd"><td>v1.1 2017 一月</td>
 <td>模型库</td>
 <td>GoogleNet; 医疗健康模型</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>Caffe 转换器</td>
 <td>使用 SINGA 训练模型, 从 caffe proto 文件配置</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>模型组件</td>
 <td>添加 concat 和 slice 层; 接受多个输入网络</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>编译和安装</td>
 <td>Windows 支持</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>&#160;</td>
 <td>通过与 protobuf 和 openblas 一起编译 SINGA 简化安装</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>&#160;</td>
 <td>用 Jenkins 自动生成 python wheel</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>&#160;</td>
 <td>从 Debian packages 安装 SINGA</td>
 </tr>
 <tr class="row-even"><td>v1.2 2018 六月</td>
 <td>AutoGrad</td>
 <td>后向传播 AutoGrad</td>
 </tr>
 <tr class="row-odd"><td>&#160;</td>
 <td>Python 3</td>
 <td>PySinga 支持 Python 3</td>
 </tr>
 <tr class="row-even"><td>&#160;</td>
 <td>模型</td>
 <td>添加流行模型, 包括 VGG, ResNet, DenseNet, InceptionNet</td>
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	<h1>开发时间表<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h1>
	<table border="1" class="docutils">
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	<col width="33%" />
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	<th class="head">模块</th>
	<th class="head">特性</th>
	</tr>
	</thead>
	<tbody valign="top">
	<tr class="row-even"><td>v0.1 2015 九月</td>
	<td>神经网络</td>
	<td>前向传播神经网络, 包括 CNN, MLP</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td> </td>
	<td>类 RBM 模型, 包括 RBM</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>循环神经网络, 包括标准 RNN</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td>架构</td>
	<td>在单节点运行一个工作组 (包括划分)</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>在单节点运行多个工作组, 用 <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>分布式 Hogwild</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>跨多节点运行多个工作组 , 如 <a class="reference external" href="http://papers.nips.cc/paper/4687-large-scale-distritbuted-deep-networks">Downpour</a></td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td> </td>
	<td>All-Reduce 训练架构如 <a class="reference external" href="http://arxiv.org/abs/1501.02876">DeepImage</a></td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>服务器间负载均衡</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td>失败恢复</td>
	<td>检查点和恢复</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td>工具</td>
	<td>用 GNU 自动工具安装</td>
	</tr>
	<tr class="row-odd"><td>v0.2 2016 一月</td>
	<td>神经网络</td>
	<td>前向传播神经网络, 包括 AlexNet, cuDNN 层, 工具</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>循环神经网络, 包括 GRU 层和 BPTT</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td> </td>
	<td>模型划分和混合划分</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td>工具</td>
	<td>融合 Mesos 资源管理</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td> </td>
	<td>准备部署 Docker images</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>可视化神经网络和调试信息</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td>绑定</td>
	<td>主要组件 Python 绑定</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td>GPU</td>
	<td>单节点多个 GPU</td>
	</tr>
	<tr class="row-odd"><td>v0.3 2016 四月</td>
	<td>GPU</td>
	<td>多个节点, 每个包含多个 GPU</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>用 GPU 和 CPU 混合训练 <a class="reference external" href="http://arxiv.org/abs/1504.04343">CcT</a></td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td> </td>
	<td>支持 cuDNN v4</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td>安装</td>
	<td>删除 ZeroMQ, CZMQ 依赖, 单节点训练 zookeeper</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td>优化器</td>
	<td>添加新的 SGD 优化器，包括 Adam, AdamMax 和 AdaDelta</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td>绑定</td>
	<td>增强 Python 绑定训练</td>
	</tr>
	<tr class="row-odd"><td>v1.0 2016 九月</td>
	<td>模型抽象</td>
	<td>Tensor 基于线性代数, 神经网络和随机运算</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>分布式参数更新优化器</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td>硬件</td>
	<td>使用 Cuda 和 Cudnn for Nvidia GPU</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>使用 OpenCL for AMD GPU 及其他设备</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td>跨平台</td>
	<td>从 Linux 扩展到 MacOS</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>大型图像模型, 例如, <a class="reference external" href="https://arxiv.org/pdf/1409.1556.pdf">VGG</a> 和 <a class="reference external" href="http://arxiv.org/abs/1512.03385">Residual Net</a></td>
	</tr>
	<tr class="row-odd"><td>v1.1 2017 一月</td>
	<td>模型库</td>
	<td>GoogleNet; 医疗健康模型</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td>Caffe 转换器</td>
	<td>使用 SINGA 训练模型, 从 caffe proto 文件配置</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td>模型组件</td>
	<td>添加 concat 和 slice 层; 接受多个输入网络</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td>编译和安装</td>
	<td>Windows 支持</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td> </td>
	<td>通过与 protobuf 和 openblas 一起编译 SINGA 简化安装</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td> </td>
	<td>用 Jenkins 自动生成 python wheel</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td> </td>
	<td>从 Debian packages 安装 SINGA</td>
	</tr>
	<tr class="row-even"><td>v1.2 2018 六月</td>
	<td>AutoGrad</td>
	<td>后向传播 AutoGrad</td>
	</tr>
	<tr class="row-odd"><td> </td>
	<td>Python 3</td>
	<td>PySinga 支持 Python 3</td>
	</tr>
	<tr class="row-even"><td> </td>
	<td>模型</td>
	<td>添加流行模型, 包括 VGG, ResNet, DenseNet, InceptionNet</td>
	</tr>
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