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<p>SINGA is a distributed deep learning library.</p>
<p>This release includes following changes:</p>
<ul>
<li><p>Tensor core:</p>
<ul>
<li>Support tensor transformation (reshape, transpose) for tensors up to 6
dimensions.</li>
<li>Implement traverse_unary_transform in Cuda backend, which is similar to CPP
backend one.</li>
</ul></li>
<li><p>Add new tensor operators into the autograd module, including CosSim,
DepthToSpace, Embedding, Erf, Expand, Floor, Pad, Round, Rounde, SpaceToDepth,
UpSample, Where. The corresponding ONNX operators are thus supported by SINGA.</p></li>
<li><p>Add Embedding and Gemm into the layer module.</p></li>
<li><p>Add SGD operators to opt module, including RMSProp, Adam, and AdaGrad.</p></li>
<li><p>Extend the sonnx module to support DenseNet121, ShuffleNetv1, ShuffleNetv2,
SqueezeNet, VGG19, GPT2, and RoBERTa,</p></li>
<li><p>Reconstruct sonnx to</p>
<ul>
<li>Support creating operators from both layer and autograd.</li>
<li>Re-write SingaRep to provide a more powerful intermediate representation of
SINGA.</li>
<li>Add a SONNXModel which implements from Model to provide uniform API and
features.</li>
</ul></li>
<li><p>Add one example that trains a BiLSTM model over the InsuranceQA data.</p></li>
<li><p>Replace the Travis CI with Github workflow. Add quality and coverage
management.</p></li>
<li><p>Add compiling and packaging scripts to creat wheel packages for distribution.</p></li>
<li><p>Fix bugs</p>
<ul>
<li>Fix IMDB LSTM model example training script.</li>
<li>Fix Tensor operation Mult on Broadcasting use cases.</li>
<li>Gaussian function on Tensor now can run on Tensor with odd size.</li>
<li>Updated a testing helper function gradients() in autograd to lookup param
gradient by param python object id for testing purpose.</li>
</ul></li>
</ul>
</span></div></article></div><div class="docLastUpdate"><em>Last updated on 10/30/2020</em></div><div class="docs-prevnext"></div></div></div><nav class="onPageNav"></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 © 2023
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