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<p>SINGA's software stack includes two major levels, the low level backend classes
and the Python interface level. Figure 1 illustrates them together with the
hardware. The backend components provide basic data structures for deep learning
models, hardware abstractions for scheduling and executing operations, and
communication components for distributed training. The Python interface wraps
some CPP data structures and provides additional high-level classes for neural
network training, which makes it convenient to implement complex neural network
models.</p>
<p>Next, we introduce the software stack in a bottom-up manner.</p>
<p><img src="/docs/assets/singav3.1-sw.png" alt="SINGA V3 software stack"> <br/> <strong>Figure 1 - SINGA V3
software stack.</strong></p>
<h2><a class="anchor" aria-hidden="true" id="low-level-backend"></a><a href="#low-level-backend" 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>Low-level Backend</h2>
<h3><a class="anchor" aria-hidden="true" id="device"></a><a href="#device" 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>Device</h3>
<p>Each <code>Device</code> instance, i.e., a device, is created against one hardware device,
e.g. a GPU or a CPU. <code>Device</code> manages the memory of the data structures, and
schedules the operations for executing, e.g., on CUDA streams or CPU threads.
Depending on the hardware and its 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>
<h3><a class="anchor" aria-hidden="true" id="tensor"></a><a href="#tensor" 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>Tensor</h3>
<p><code>Tensor</code> class represents a multi-dimensional array, which stores model
variables, e.g., the input images and feature maps from the convolution layer.
Each <code>Tensor</code> instance (i.e. a tensor) is allocated on a device, which manages
the memory of the tensor and schedules the (computation) operations against
tensors. Most machine learning algorithms could be expressed using (dense or
sparse) the tensor abstraction and its operations. Therefore, SINGA would be
able to run a wide range of models, including deep learning models and other
traditional machine learning models.</p>
<h3><a class="anchor" aria-hidden="true" id="operator"></a><a href="#operator" 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>Operator</h3>
<p>There are two types of operators against tensors, linear algebra operators like
matrix multiplication, and neural network specific operators like convolution
and pooling. The linear algebra operators are provided as <code>Tensor</code> functions and
are implemented separately for different hardware devices</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>The neural network specific operators are also implemented separately, e.g.,</p>
<ul>
<li>GpuConvFoward (convolution.h) implements the forward function of convolution
via CuDNN on Nvidia GPU.</li>
<li>CpuConvForward (convolution.h) implements the forward function of convolution
using CPP on CPU.</li>
</ul>
<p>Typically, users create a <code>Device</code> instance and use it to create multiple
<code>Tensor</code> instances. When users call the Tensor functions or neural network
operations, the corresponding implementation for the resident device will be
invoked. In other words, the implementation of operators is transparent to
users.</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 operators.</p>
<p>Optimizations in terms of speed and memory are done by the <code>Scheduler</code> and
<code>MemPool</code> of the <code>Device</code>. For example, the <code>Scheduler</code> creates a
<a href="./graph">computational graph</a> according to the dependency of the operators.
Then it can optimize the execution order of the operators for parallelism and
memory sharing.</p>
<h3><a class="anchor" aria-hidden="true" id="communicator"></a><a href="#communicator" 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>Communicator</h3>
<p><code>Communicator</code> is to support <a href="./dist-train">distributed training</a>. It implements
the communication protocols using sockets, MPI and NCCL. Typically users only
need to call the high-level APIs like <code>put()</code> and <code>get()</code> for sending and
receiving tensors. Communication optimization for the topology, message size,
etc. is done internally.</p>
<h2><a class="anchor" aria-hidden="true" id="python-interface"></a><a href="#python-interface" 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>Python Interface</h2>
<p>All the backend components are exposed as Python modules via SWIG. In addition,
the following classes are added to support the implementation of complex neural
networks.</p>
<h3><a class="anchor" aria-hidden="true" id="opt"></a><a href="#opt" 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>Opt</h3>
<p><code>Opt</code> and its subclasses implement the methods (such as SGD) for updating model
parameter values using parameter gradients. A subclass <a href="./dist-train">DistOpt</a>
synchronizes the gradients across the workers for distributed training by
calling methods from <code>Communicator</code>.</p>
<h3><a class="anchor" aria-hidden="true" id="operator-1"></a><a href="#operator-1" 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>Operator</h3>
<p><code>Operator</code> wraps multiple functions implemented using the Tensor or neural
network operators from the backend. For example, the forward function and
backward function <code>ReLU</code> compose the <code>ReLU</code> operator.</p>
<h3><a class="anchor" aria-hidden="true" id="layer"></a><a href="#layer" 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>Layer</h3>
<p><code>Layer</code> and its subclasses wraps the operators with parameters. For instance,
convolution and linear operators<br>
have weight and bias parameters. The parameters are maintained by the
corresponding <code>Layer</code> class.</p>
<h3><a class="anchor" aria-hidden="true" id="autograd"></a><a href="#autograd" 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>Autograd</h3>
<p><a href="./autograd">Autograd</a> implements the
<a href="https://rufflewind.com/2016-12-30/reverse-mode-automatic-differentiation">reverse-mode automatic differentiation</a>
by recording the execution of the forward functions of the operators calling the
backward functions automatically in the reverse order. All functions can be
buffered by the <code>Scheduler</code> to create a <a href="./graph">computational graph</a> for
efficiency and memory optimization.</p>
<h3><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</h3>
<p><a href="./graph">Model</a> provides an easy interface to implement new network models. You
just need to inherit <code>Model</code> and define the forward propagation of the model by
creating and calling the layers or operators. <code>Model</code> will do autograd and
update the parameters via <code>Opt</code> automatically when training data is fed into it.
With the <code>Model</code> API, SINGA enjoys the advantages of imperative programming and
declarative programming. Users implement a network using the <a href="./graph">Model</a>
API following the imperative programming style like PyTorch. Different from
PyTorch which recreates the operations in every iteration, SINGA buffers the
operations to create a computational graph implicitly (when this feature is
enabled) after the first iteration. The graph is similar to that created by
libraries using declarative programming, e.g., TensorFlow. Therefore, SINGA can
apply the memory and speed optimization techniques over the computational graph.</p>
<h3><a class="anchor" aria-hidden="true" id="onnx"></a><a href="#onnx" 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>ONNX</h3>
<p>To support ONNX, SINGA implements a <a href="./onnx">sonnx</a> module, which includes:</p>
<ul>
<li>SingaFrontend for saving SINGA model into onnx format.</li>
<li>SingaBackend for loading onnx format model into SINGA for training and
inference.</li>
</ul>
</span></div></article></div><div class="docLastUpdate"><em>Last updated on 04/10/2020</em></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/installation"><span class="arrow-prev">← </span><span>Installation</span></a><a class="docs-next button" href="/docs/examples"><span>Examples</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#low-level-backend">Low-level Backend</a><ul class="toc-headings"><li><a href="#device">Device</a></li><li><a href="#tensor">Tensor</a></li><li><a href="#operator">Operator</a></li><li><a href="#communicator">Communicator</a></li></ul></li><li><a href="#python-interface">Python Interface</a><ul class="toc-headings"><li><a href="#opt">Opt</a></li><li><a href="#operator-1">Operator</a></li><li><a href="#layer">Layer</a></li><li><a href="#autograd">Autograd</a></li><li><a href="#model">Model</a></li><li><a href="#onnx">ONNX</a></li></ul></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/https://apache-singa.readthedocs.io/en/latest/">API Reference</a><a href="/docs/examples">Examples</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="/blog">SINGA News</a><a href="https://github.com/apache/singa">GitHub</a><div class="social"><a class="github-button" href="https://github.com/apache/singa" 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 © 2021
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