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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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