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<p>SINGA's software stack includes three major components, namely, core, IO and
model. Figure 1 illustrates these components together with the hardware. The
core component provides memory management and tensor operations; IO has classes
for reading (and writing) data from (to) disk and network; The model component
provides data structures and algorithms for machine learning models, e.g.,
layers for neural network models, optimizers/initializer/metric/loss for general
machine learning models.</p>
<p><img src="/docs/assets/singav1-sw.png" alt="SINGA V1 software stack"> <br/> <strong>Figure 1 - SINGA V1
software stack.</strong></p>
<h2><a class="anchor" aria-hidden="true" id="core"></a><a href="#core" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Core</h2>
<p><a href="/docs/next/tensor">Tensor</a> and <a href="/docs/next/device">Device</a> are two core abstractions in SINGA.
Tensor class represents a multi-dimensional array, which stores model variables
and provides linear algebra operations for machine learning algorithms,
including matrix multiplication and random functions. Each tensor instance (i.e.
a tensor) is allocated on a Device instance. Each Device instance (i.e. a
device) is created against one hardware device, e.g. a GPU card or a CPU core.
Devices manage the memory of tensors and execute tensor operations on its
execution units, e.g. CPU threads or CUDA streams.</p>
<p>Depending on the hardware and the programming language, SINGA have implemented
the following specific device classes:</p>
<ul>
<li><strong>CudaGPU</strong> represents an Nvidia GPU card. The execution units are the CUDA
streams.</li>
<li><strong>CppCPU</strong> represents a normal CPU. The execution units are the CPU threads.</li>
<li><strong>OpenclGPU</strong> represents normal GPU card from both Nvidia and AMD. The
execution units are the CommandQueues. Given that OpenCL is compatible with
many hardware devices, e.g. FPGA and ARM, the OpenclGPU has the potential to
be extended for other devices.</li>
</ul>
<p>Different types of devices use different programming languages to write the
kernel functions for tensor operations,</p>
<ul>
<li>CppMath (tensor_math_cpp.h) implements the tensor operations using Cpp for
CppCPU</li>
<li>CudaMath (tensor_math_cuda.h) implements the tensor operations using CUDA for
CudaGPU</li>
<li>OpenclMath (tensor_math_opencl.h) implements the tensor operations using
OpenCL for OpenclGPU</li>
</ul>
<p>In addition, different types of data, such as float32 and float16, could be
supported by adding the corresponding tensor functions.</p>
<p>Typically, users would create a device instance and pass it to create multiple
tensor instances. When users call the Tensor functions, these function would
invoke the corresponding implementation (CppMath/CudaMath/OpenclMath)
automatically. In other words, the implementation of Tensor operations is
transparent to users.</p>
<p>Most machine learning algorithms could be expressed using (dense or sparse)
tensors. Therefore, with the Tensor abstraction, SINGA would be able to run a
wide range of models, including deep learning models and other traditional
machine learning models.</p>
<p>The Tensor and Device abstractions are extensible to support a wide range of
hardware device using different programming languages. A new hardware device
would be supported by adding a new Device subclass and the corresponding
implementation of the Tensor operations (xxxMath).</p>
<p>Optimizations in terms of speed and memory could be implemented by Device, which
manages both operation execution and memory malloc/free. More optimization
details would be described in the <a href="/docs/next/device">Device page</a>.</p>
<h2><a class="anchor" aria-hidden="true" id="model"></a><a href="#model" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Model</h2>
<p>On top of the Tensor and Device abstractions, SINGA provides some higher level
classes for machine learning modules.</p>
<ul>
<li><p><a href="/docs/next/layer">Layer</a> and its subclasses are specific for neural networks. Every
layer provides functions for forward propagating features and backward
propagating gradients w.r.t the training loss functions. They wraps the
complex layer operations so that users can easily create neural nets by
connecting a set of layers.</p></li>
<li><p><a href="/docs/next/initializer">Initializer</a> and its subclasses provide variant methods of
initializing model parameters (stored in Tensor instances), following Uniform,
Gaussian, etc.</p></li>
<li><p><a href="/docs/next/loss">Loss</a> and its subclasses defines the training objective loss
functions. Both functions of computing the loss values and computing the
gradient of the prediction w.r.t the objective loss are implemented. Example
loss functions include squared error and cross entropy.</p></li>
<li><p><a href="/docs/next/metric">Metric</a> and its subclasses provide the function to measure the
performance of the model, e.g., the accuracy.</p></li>
<li><p><a href="/docs/next/optimizer">Optimizer</a> and its subclasses implement the methods for
updating model parameter values using parameter gradients, including SGD,
AdaGrad, RMSProp etc.</p></li>
</ul>
<h2><a class="anchor" aria-hidden="true" id="io"></a><a href="#io" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>IO</h2>
<p>The IO module consists of classes for data loading, data preprocessing and
message passing.</p>
<ul>
<li>Reader and its subclasses load string records from disk files</li>
<li>Writer and its subclasses write string records to disk files</li>
<li>Encoder and its subclasses encode Tensor instances into string records</li>
<li>Decoder and its subclasses decodes string records into Tensor instances</li>
<li>Endpoint represents a communication endpoint which provides functions for
passing messages to each other.</li>
<li>Message represents communication messages between Endpoint instances. It
carries both meta data and payload.</li>
</ul>
</span></div></article></div><div class="docLastUpdate"><em>Last updated on 4/2/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/next/installation"><span class="arrow-prev">← </span><span>Installation</span></a><a class="docs-next button" href="/docs/next/benchmark-train"><span>Benchmark for Distributed Training</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#core">Core</a></li><li><a href="#model">Model</a></li><li><a href="#io">IO</a></li></ul></nav></div><footer class="nav-footer" id="footer"><section class="sitemap"><a href="/" class="nav-home"><img src="/img/singa-logo-square.png" alt="Apache SINGA" width="66" height="58"/></a><div><h5>Docs</h5><a href="/docs/installation">Getting Started</a><a href="/docs/device">Guides</a><a href="/en/#">API Reference (coming soon)</a><a href="/docs/model-zoo-cnn-cifar10">Model Zoo</a><a href="/docs/download-singa">Development</a></div><div><h5>Community</h5><a href="/en/users.html">User Showcase</a><a href="/docs/history-singa">SINGA History</a><a href="/docs/team-list">SINGA Team</a><a href="/news">SINGA News</a><a href="https://github.com/apache/singa-doc">GitHub</a><div class="social"><a class="github-button" href="https://github.com/apache/singa-doc" data-count-href="/apache/singa/stargazers" data-show-count="true" data-count-aria-label="# stargazers on GitHub" aria-label="Star this project on GitHub">apache/singa-doc</a></div><div class="social"><a href="https://twitter.com/ApacheSINGA" class="twitter-follow-button">Follow @ApacheSINGA</a></div></div><div><h5>Apache Software Foundation</h5><a href="https://apache.org/" target="_blank" rel="noreferrer noopener">Foundation</a><a href="http://www.apache.org/licenses/" target="_blank" rel="noreferrer noopener">License</a><a href="http://www.apache.org/foundation/sponsorship.html" target="_blank" rel="noreferrer noopener">Sponsorship</a><a href="http://www.apache.org/foundation/thanks.html" target="_blank" rel="noreferrer noopener">Thanks</a><a href="http://www.apache.org/events/current-event" target="_blank" rel="noreferrer noopener">Events</a><a href="http://www.apache.org/security/" target="_blank" rel="noreferrer noopener">Security</a></div></section><div style="width:100%;text-align:center"><a href="https://apache.org/" target="_blank" rel="noreferrer noopener" class="ApacheOpenSource"><img src="/img/asf_logo_wide.svg" alt="Apache Open Source"/></a><section class="copyright" style="max-width:60%;margin:0 auto">Copyright © 2020
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