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开发时间表
====================

.. csv-table::
  :header: "版本","模块","特性"

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