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   <div class="section" id="download-singa">
 <span id="download-singa"></span><h1>Download SINGA<a class="headerlink" href="#download-singa" title="Permalink to this headline">¶</a></h1>
 <ul class="simple">
 <li>Latest code: please clone the dev branch from <a class="reference external" href="https://github.com/apache/incubator-singa">Github</a></li>
 <li>v1.1.0 (12 February 2017):<ul>
 <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://dist.apache.org/repos/dist/release/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.md5">[MD5]</a>
 <a class="reference external" href="https://dist.apache.org/repos/dist/release/incubator/singa/1.1.0/KEYS">[KEYS]</a></li>
 <li><a class="reference external" href="releases/RELEASE_NOTES_1.1.0.html">Release Notes 1.1.0</a></li>
 <li>New features and major updates,<ul>
 <li>Create Docker images (CPU and GPU versions)</li>
 <li>Create Amazon AMI for SINGA (CPU version)</li>
 <li>Integrate with Jenkins for automatically generating Wheel and Debian packages (for installation), and updating the website.</li>
 <li>Enhance the FeedFowardNet, e.g., multiple inputs and verbose mode for debugging</li>
 <li>Add Concat and Slice layers</li>
 <li>Extend CrossEntropyLoss to accept instance with multiple labels</li>
 <li>Add image_tool.py with image augmentation methods</li>
 <li>Support model loading and saving via the Snapshot API</li>
 <li>Compile SINGA source on Windows</li>
 <li>Compile mandatory dependent libraries together with SINGA code</li>
 <li>Enable Java binding (basic) for SINGA</li>
 <li>Add version ID in checkpointing files</li>
 <li>Add Rafiki toolkit for providing RESTFul APIs</li>
 <li>Add examples pretrained from Caffe, including GoogleNet</li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 <ul class="simple">
 <li>v1.0.0 (8 September 2016):<ul>
 <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></li>
 <li><a class="reference external" href="releases/RELEASE_NOTES_1.0.0.html">Release Notes 1.0.0</a></li>
 <li>New features and major updates,<ul>
 <li>Tensor abstraction for supporting more machine learning models.</li>
 <li>Device abstraction for running on different hardware devices, including CPU, (Nvidia/AMD) GPU and FPGA (to be tested in later versions).</li>
 <li>Replace GNU autotool with cmake for compilation.</li>
 <li>Support Mac OS</li>
 <li>Improve Python binding, including installation and programming</li>
 <li>More deep learning models, including VGG and ResNet</li>
 <li>More IO classes for reading/writing files and encoding/decoding data</li>
 <li>New network communication components directly based on Socket.</li>
 <li>Cudnn V5 with Dropout and RNN layers.</li>
 <li>Replace website building tool from maven to Sphinx</li>
 <li>Integrate Travis-CI</li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 <ul class="simple">
 <li>v0.3.0 (20 April 2016):<ul>
 <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></li>
 <li><a class="reference external" href="releases/RELEASE_NOTES_0.3.0.html">Release Notes 0.3.0</a></li>
 <li>New features and major updates,<ul>
 <li><a class="reference external" href="v0.3.0/gpu.html">Training on GPU cluster</a> enables training of deep learning models over a GPU cluster.</li>
 <li><a class="reference external" href="v0.3.0/python.html">Python wrapper improvement</a> makes it easy to configure the job, including neural net and SGD algorithm.</li>
 <li><a class="reference external" href="v0.3.0/updater.html">New SGD updaters</a> are added, including Adam, AdaDelta and AdaMax.</li>
 <li><a class="reference external" href="v0.3.0/installation.html">Installation</a> has fewer dependent libraries for single node training.</li>
 <li>Heterogeneous training with CPU and GPU.</li>
 <li>Support cuDNN V4.</li>
 <li>Data prefetching.</li>
 <li>Fix some bugs.</li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 <ul class="simple">
 <li>v0.2.0 (14 January 2016):<ul>
 <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></li>
 <li><a class="reference external" href="releases/RELEASE_NOTES_0.2.0.html">Release Notes 0.2.0</a></li>
 <li>New features and major updates,<ul>
 <li><a class="reference external" href="v0.2.0/gpu.html">Training on GPU</a> enables training of complex models on a single node with multiple GPU cards.</li>
 <li><a class="reference external" href="v0.2.0/hybrid.html">Hybrid neural net partitioning</a> supports data and model parallelism at the same time.</li>
 <li><a class="reference external" href="v0.2.0/python.html">Python wrapper</a> makes it easy to configure the job, including neural net and SGD algorithm.</li>
 <li><a class="reference external" href="v0.2.0/general-rnn.html">RNN model and BPTT algorithm</a> are implemented to support applications based on RNN models, e.g., GRU.</li>
 <li><a class="reference external" href="v0.2.0/distributed-training.html">Cloud software integration</a> includes Mesos, Docker and HDFS.</li>
 <li>Visualization of neural net structure and layer information, which is helpful for debugging.</li>
 <li>Linear algebra functions and random functions against Blobs and raw data pointers.</li>
 <li>New layers, including SoftmaxLayer, ArgSortLayer, DummyLayer, RNN layers and cuDNN layers.</li>
 <li>Update Layer class to carry multiple data/grad Blobs.</li>
 <li>Extract features and test performance for new data by loading previously trained model parameters.</li>
 <li>Add Store class for IO operations.</li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 <ul class="simple">
 <li>v0.1.0 (8 October 2015):<ul>
 <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></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">Release Notes 0.1.0</a></li>
 <li>Major features include,<ul>
 <li>Installation using GNU build utility</li>
 <li>Scripts for job management with zookeeper</li>
 <li>Programming model based on NeuralNet and Layer abstractions.</li>
 <li>System architecture based on Worker, Server and Stub.</li>
 <li>Training models from three different model categories, namely, feed-forward models, energy models and RNN models.</li>
 <li>Synchronous and asynchronous distributed training frameworks using CPU</li>
 <li>Checkpoint and restore</li>
 <li>Unit test using gtest</li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
 <p><strong>Disclaimer</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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	<div class="section" id="download-singa">
	<span id="download-singa"></span><h1>Download SINGA<a class="headerlink" href="#download-singa" title="Permalink to this headline">¶</a></h1>
	<ul class="simple">
	<li>Latest code: please clone the dev branch from <a class="reference external" href="https://github.com/apache/incubator-singa">Github</a></li>
	<li>v1.1.0 (12 February 2017):<ul>
	<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://dist.apache.org/repos/dist/release/incubator/singa/1.1.0/apache-singa-incubating-1.1.0.tar.gz.md5">[MD5]</a>
	<a class="reference external" href="https://dist.apache.org/repos/dist/release/incubator/singa/1.1.0/KEYS">[KEYS]</a></li>
	<li><a class="reference external" href="releases/RELEASE_NOTES_1.1.0.html">Release Notes 1.1.0</a></li>
	<li>New features and major updates,<ul>
	<li>Create Docker images (CPU and GPU versions)</li>
	<li>Create Amazon AMI for SINGA (CPU version)</li>
	<li>Integrate with Jenkins for automatically generating Wheel and Debian packages (for installation), and updating the website.</li>
	<li>Enhance the FeedFowardNet, e.g., multiple inputs and verbose mode for debugging</li>
	<li>Add Concat and Slice layers</li>
	<li>Extend CrossEntropyLoss to accept instance with multiple labels</li>
	<li>Add image_tool.py with image augmentation methods</li>
	<li>Support model loading and saving via the Snapshot API</li>
	<li>Compile SINGA source on Windows</li>
	<li>Compile mandatory dependent libraries together with SINGA code</li>
	<li>Enable Java binding (basic) for SINGA</li>
	<li>Add version ID in checkpointing files</li>
	<li>Add Rafiki toolkit for providing RESTFul APIs</li>
	<li>Add examples pretrained from Caffe, including GoogleNet</li>
	</ul>
	</li>
	</ul>
	</li>
	</ul>
	<ul class="simple">
	<li>v1.0.0 (8 September 2016):<ul>
	<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></li>
	<li><a class="reference external" href="releases/RELEASE_NOTES_1.0.0.html">Release Notes 1.0.0</a></li>
	<li>New features and major updates,<ul>
	<li>Tensor abstraction for supporting more machine learning models.</li>
	<li>Device abstraction for running on different hardware devices, including CPU, (Nvidia/AMD) GPU and FPGA (to be tested in later versions).</li>
	<li>Replace GNU autotool with cmake for compilation.</li>
	<li>Support Mac OS</li>
	<li>Improve Python binding, including installation and programming</li>
	<li>More deep learning models, including VGG and ResNet</li>
	<li>More IO classes for reading/writing files and encoding/decoding data</li>
	<li>New network communication components directly based on Socket.</li>
	<li>Cudnn V5 with Dropout and RNN layers.</li>
	<li>Replace website building tool from maven to Sphinx</li>
	<li>Integrate Travis-CI</li>
	</ul>
	</li>
	</ul>
	</li>
	</ul>
	<ul class="simple">
	<li>v0.3.0 (20 April 2016):<ul>
	<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></li>
	<li><a class="reference external" href="releases/RELEASE_NOTES_0.3.0.html">Release Notes 0.3.0</a></li>
	<li>New features and major updates,<ul>
	<li><a class="reference external" href="v0.3.0/gpu.html">Training on GPU cluster</a> enables training of deep learning models over a GPU cluster.</li>
	<li><a class="reference external" href="v0.3.0/python.html">Python wrapper improvement</a> makes it easy to configure the job, including neural net and SGD algorithm.</li>
	<li><a class="reference external" href="v0.3.0/updater.html">New SGD updaters</a> are added, including Adam, AdaDelta and AdaMax.</li>
	<li><a class="reference external" href="v0.3.0/installation.html">Installation</a> has fewer dependent libraries for single node training.</li>
	<li>Heterogeneous training with CPU and GPU.</li>
	<li>Support cuDNN V4.</li>
	<li>Data prefetching.</li>
	<li>Fix some bugs.</li>
	</ul>
	</li>
	</ul>
	</li>
	</ul>
	<ul class="simple">
	<li>v0.2.0 (14 January 2016):<ul>
	<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></li>
	<li><a class="reference external" href="releases/RELEASE_NOTES_0.2.0.html">Release Notes 0.2.0</a></li>
	<li>New features and major updates,<ul>
	<li><a class="reference external" href="v0.2.0/gpu.html">Training on GPU</a> enables training of complex models on a single node with multiple GPU cards.</li>
	<li><a class="reference external" href="v0.2.0/hybrid.html">Hybrid neural net partitioning</a> supports data and model parallelism at the same time.</li>
	<li><a class="reference external" href="v0.2.0/python.html">Python wrapper</a> makes it easy to configure the job, including neural net and SGD algorithm.</li>
	<li><a class="reference external" href="v0.2.0/general-rnn.html">RNN model and BPTT algorithm</a> are implemented to support applications based on RNN models, e.g., GRU.</li>
	<li><a class="reference external" href="v0.2.0/distributed-training.html">Cloud software integration</a> includes Mesos, Docker and HDFS.</li>
	<li>Visualization of neural net structure and layer information, which is helpful for debugging.</li>
	<li>Linear algebra functions and random functions against Blobs and raw data pointers.</li>
	<li>New layers, including SoftmaxLayer, ArgSortLayer, DummyLayer, RNN layers and cuDNN layers.</li>
	<li>Update Layer class to carry multiple data/grad Blobs.</li>
	<li>Extract features and test performance for new data by loading previously trained model parameters.</li>
	<li>Add Store class for IO operations.</li>
	</ul>
	</li>
	</ul>
	</li>
	</ul>
	<ul class="simple">
	<li>v0.1.0 (8 October 2015):<ul>
	<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></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">Release Notes 0.1.0</a></li>
	<li>Major features include,<ul>
	<li>Installation using GNU build utility</li>
	<li>Scripts for job management with zookeeper</li>
	<li>Programming model based on NeuralNet and Layer abstractions.</li>
	<li>System architecture based on Worker, Server and Stub.</li>
	<li>Training models from three different model categories, namely, feed-forward models, energy models and RNN models.</li>
	<li>Synchronous and asynchronous distributed training frameworks using CPU</li>
	<li>Checkpoint and restore</li>
	<li>Unit test using gtest</li>
	</ul>
	</li>
	</ul>
	</li>
	</ul>
	<p><strong>Disclaimer</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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