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<h1>下载 SINGA<a class="headerlink" href="#singa" title="Permalink to this headline"></a></h1>
<ul>
<li><p class="first">要验证下载的tar.gz文件,请下载KEY和ASC文件,然后执行以下命令</p>
<div class="highlight-default"><div class="highlight"><pre><span></span> <span class="o">%</span> <span class="n">gpg</span> <span class="o">--</span><span class="kn">import</span> <span class="nn">KEYS</span>
<span class="o">%</span> <span class="n">gpg</span> <span class="o">--</span><span class="n">verify</span> <span class="n">downloaded_file</span><span class="o">.</span><span class="n">asc</span> <span class="n">downloaded_file</span>
</pre></div>
</div>
<p>您还可以检查SHA512或MD5值以查看下载是否已完成</p>
</li>
<li><p class="first">v1.2.0 (2018年6月6日):</p>
<ul class="simple">
<li><a class="reference external" href="http://www.apache.org/dyn/closer.cgi/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz">Apache SINGA 1.2.0</a>
<a class="reference external" href="https://www.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz.sha512">[SHA512]</a>
<a class="reference external" href="https://www.apache.org/dist/incubator/singa/1.2.0/KEYS">[KEYS]</a>
<a class="reference external" href="https://www.apache.org/dist/incubator/singa/1.2.0/apache-singa-incubating-1.2.0.tar.gz.asc">[ASC]</a></li>
<li><a class="reference external" href="releases/RELEASE_NOTES_1.2.0.html">发行说明 1.2.0</a></li>
<li>新功能和主要更新,<ul>
<li>实现 autograd (目前支持MLP模型)</li>
<li>升级 PySinga 以支持 Python 3</li>
<li>为 Tensor 类添加 stride field</li>
<li>讲 cuDNN 从 V5 升级到 V7</li>
<li>为 ImageNet 分类任务添加 VGG, Inception V4, ResNet, 及 DenseNet 模型</li>
<li>为 conda 包创建别名</li>
<li>完善中文文档</li>
<li>添加在 Windows 上运行 Singa 的说明</li>
<li>更新编译, CI</li>
<li>修复一些错误</li>
</ul>
</li>
</ul>
</li>
<li><p class="first">v1.1.0 (2017年2月12日):</p>
<ul class="simple">
<li><a class="reference external" href="http://www.apache.org/dyn/closer.cgi/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz">Apache SINGA 1.1.0</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.md5">[MD5]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/1.1.0/KEYS">[KEYS]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.asc">[ASC]</a></li>
<li><a class="reference external" href="releases/RELEASE_NOTES_1.1.0.html">发行说明 1.1.0</a></li>
<li>新功能和主要更新,<ul>
<li>创建 Docker 镜像(CPU和GPU版本)</li>
<li>为 SINGA 创建 Amazon AMI(CPU版本)</li>
<li>与 Jenkins 集成以自动生成 Wheel 和 Debian 软件包(用于安装),并更新网站.</li>
<li>增强 FeedFowardNet, 例如,用于调试的多输入和详细模式</li>
<li>添加 Concat 和 Slice 层</li>
<li>扩展 CrossEntropyLoss 以接受具有多个标签的实例</li>
<li>添加 image_tool.py 与图像增强方法</li>
<li>通过 Snapshot API 支持模型加载和保存</li>
<li>在 Windows 上编译 SINGA 源代码</li>
<li>用 SINGA 代码编译强制依赖库</li>
<li>启用 S​​INGA 的 Java 绑定(基本)</li>
<li>在检查点文件中添加版本 ID</li>
<li>添加 Rafiki 工具包以提供 RESTFul API</li>
<li>添加 Caffe 预训练的例子,包括 GoogleNet</li>
</ul>
</li>
</ul>
</li>
<li><p class="first">v1.0.0 (2016年9月8日):</p>
<ul class="simple">
<li><a class="reference external" href="https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz">Apache SINGA 1.0.0</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.md5">[MD5]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa//1.0.0/KEYS">[KEYS]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/1.0.0/apache-singa-incubating-1.0.0.tar.gz.asc">[ASC]</a></li>
<li><a class="reference external" href="releases/RELEASE_NOTES_1.0.0.html">发行说明 1.0.0</a></li>
<li>新功能和主要更新,<ul>
<li>用于支持更多机器学习模型的张量抽象.</li>
<li>设备抽象运行在不同的硬件设备上,包括CPU,(Nvidia / AMD)GPU 和 FPGA(将在更高版本中测试).</li>
<li>用 cmake 替换 GNU autotool 进行编译.</li>
<li>支持 Mac OS</li>
<li>改进Python绑定,包括安装和编程</li>
<li>更多深度学习模型, 包括 VGG 和 ResNet</li>
<li>读取/写入文件和编码/解码数据的更多 IO 类</li>
<li>直接基于 Socket 的新网络通信组件.</li>
<li>包含 Dropout 和 RNN 层的 Cudnn V5.</li>
<li>将网站制作工具从 maven 替换为 Sphinx</li>
<li>整合 Travis-CI</li>
</ul>
</li>
</ul>
</li>
<li><p class="first">v0.3.0 (2016年4月20日):</p>
<ul class="simple">
<li><a class="reference external" href="https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz">Apache SINGA 0.3.0</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.md5">[MD5]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/0.3.0/KEYS">[KEYS]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/0.3.0/apache-singa-incubating-0.3.0.tar.gz.asc">[ASC]</a></li>
<li><a class="reference external" href="releases/RELEASE_NOTES_0.3.0.html">发行说明 0.3.0</a></li>
<li>新功能和主要更新,<ul>
<li><a class="reference external" href="v0.3.0/gpu.html">在 GPU 集群上</a> 可以在 GPU 集群上训练深度学习模型.</li>
<li><a class="reference external" href="v0.3.0/python.html">Python 包装器的改进</a> 使得配置工作变得很容易, 包括神经网络和 SGD 算法.</li>
<li><a class="reference external" href="v0.3.0/updater.html">新增 SGD 更新器</a>, 包括 Adam, AdaDelta 和 AdaMax.</li>
<li><a class="reference external" href="v0.3.0/installation.html">安装</a> 具有较少的相关库, 对于单节点培训.</li>
<li>在 CPU 和 GPU 上进行异构训练.</li>
<li>支持 cuDNN V4.</li>
<li>数据预读取.</li>
<li>修复一些错误.</li>
</ul>
</li>
</ul>
</li>
<li><p class="first">v0.2.0 (2016年1月14日):</p>
<ul class="simple">
<li><a class="reference external" href="https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz">Apache SINGA 0.2.0</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.md5">[MD5]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/0.2.0/KEYS">[KEYS]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/0.2.0/apache-singa-incubating-0.2.0.tar.gz.asc">[ASC]</a></li>
<li><a class="reference external" href="releases/RELEASE_NOTES_0.2.0.html">发行说明 0.2.0</a></li>
<li>新功能和主要更新,<ul>
<li><a class="reference external" href="v0.2.0/gpu.html">在 GPU 上进行训练</a> 可以在具有多个 GPU 卡的单个节点上对复杂模型进行训练.</li>
<li><a class="reference external" href="v0.2.0/hybrid.html">混合神经网络划分</a> 同时支持数据和模型并行.</li>
<li><a class="reference external" href="v0.2.0/python.html">Python 包装器</a> 可以很容易地配置作业,包括神经网络和 SGD 算法.</li>
<li><a class="reference external" href="v0.2.0/general-rnn.html">RNN 模型和 BPTT 算法</a> 实现并支持基于 RNN 模型的应用,例如 GRU.</li>
<li><a class="reference external" href="v0.2.0/distributed-training.html">云软件集成</a> 包括 Mesos,Docker 和 HDFS.</li>
<li>可视化神经网络结构和层信息,有助于调试.</li>
<li>线性代数函数和针对 Blob 和原始数据指针的随机函数.</li>
<li>新层,包括 Softmax 层,ArgSort 层,Dummy 层,RNN 层和 cuDNN 层</li>
<li>更新 Layer 类以携带多个数据/梯度 Blob.</li>
<li>通过加载预训练模型参数来提取新数据的特征和测试性能.</li>
<li>为 IO 操作添加 Store 类.</li>
</ul>
</li>
</ul>
</li>
<li><p class="first">v0.1.0 (2015年10月8日):</p>
<ul class="simple">
<li><a class="reference external" href="https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz">Apache SINGA 0.1.0</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.md5">[MD5]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/KEYS">[KEYS]</a>
<a class="reference external" href="https://archive.apache.org/dist/incubator/singa/apache-singa-incubating-0.1.0.tar.gz.asc">[ASC]</a></li>
<li><a class="reference external" href="https://console.aws.amazon.com/ec2/v2/home?region=ap-southeast-1#LaunchInstanceWizard:ami=ami-b41001e6">Amazon EC2 image</a></li>
<li><a class="reference external" href="releases/RELEASE_NOTES_0.1.0.html">发行说明 0.1.0</a></li>
<li>主要功能包括,<ul>
<li>使用 GNU build 程序进行安装</li>
<li>用 zookeeper 进行工作管理的脚本</li>
<li>基于神经网络和层抽象的编程模型.</li>
<li>基于 Worker,Server 和 Stub 的系统体系结构.</li>
<li>训练三种不同类别的模型,即前馈模型,能量模型和 RNN 模型.</li>
<li>使用 CPU 的同步和异步分布式训练框架</li>
<li>检查点和恢复</li>
<li>使用 gtest 进行单元测试</li>
</ul>
</li>
</ul>
</li>
</ul>
<p><strong>免责声明(英文)</strong></p>
<p>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.</p>
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