<!DOCTYPE html><html lang="en"><head><meta charSet="utf-8"/><meta http-equiv="X-UA-Compatible" content="IE=edge"/><title>Tensor · Apache SINGA</title><meta name="viewport" content="width=device-width"/><meta name="generator" content="Docusaurus"/><meta name="description" content="&lt;!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.  See the NOTICE file distributed with this work for additional information regarding copyright ownership.  The ASF licenses this file to you under the Apache License, Version 2.0 (the &quot;License&quot;); you may not use this file except in compliance with the License.  You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an &quot;AS IS&quot; BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  See the License for the specific language governing permissions and limitations under the License.  --&gt;"/><meta name="docsearch:version" content="2.0.0"/><meta name="docsearch:language" content="en"/><meta property="og:title" content="Tensor · Apache SINGA"/><meta property="og:type" content="website"/><meta property="og:url" content="https://feynmandna.github.io/"/><meta property="og:description" content="&lt;!--- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.  See the NOTICE file distributed with this work for additional information regarding copyright ownership.  The ASF licenses this file to you under the Apache License, Version 2.0 (the &quot;License&quot;); you may not use this file except in compliance with the License.  You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an &quot;AS IS&quot; BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  See the License for the specific language governing permissions and limitations under the License.  --&gt;"/><meta property="og:image" content="https://feynmandna.github.io/img/singa_twitter_banner.jpeg"/><meta name="twitter:card" content="summary"/><meta name="twitter:image" content="https://feynmandna.github.io/img/singa_twitter_banner.jpeg"/><link rel="shortcut icon" href="/img/favicon.ico"/><link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/atom-one-dark.min.css"/><link rel="alternate" type="application/atom+xml" href="https://feynmandna.github.io/blog/atom.xml" title="Apache SINGA Blog ATOM Feed"/><link rel="alternate" type="application/rss+xml" href="https://feynmandna.github.io/blog/feed.xml" title="Apache SINGA Blog RSS Feed"/><script type="text/javascript" src="https://buttons.github.io/buttons.js"></script><script src="https://unpkg.com/vanilla-back-to-top@7.1.14/dist/vanilla-back-to-top.min.js"></script><script>
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<p>Each Tensor instance is a multi-dimensional array allocated on a specific Device instance. Tensor instances store variables and provide linear algebra operations over different types of hardware devices without user awareness. Note that users need to make sure the tensor operands are allocated on the same device except copy functions.</p>
<h2><a class="anchor" aria-hidden="true" id="tensor-usage"></a><a href="#tensor-usage" 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 Usage</h2>
<h3><a class="anchor" aria-hidden="true" id="create-tensor"></a><a href="#create-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>Create Tensor</h3>
<pre><code class="hljs css language-python"><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> tensor
<span class="hljs-meta">&gt;&gt;&gt; </span>tensor.from_numpy( np.asarray([[<span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>], [<span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>]], dtype=np.float32) )
[[<span class="hljs-number">1.</span> <span class="hljs-number">0.</span> <span class="hljs-number">0.</span>]
 [<span class="hljs-number">0.</span> <span class="hljs-number">1.</span> <span class="hljs-number">0.</span>]]
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="convert-to-numpy"></a><a href="#convert-to-numpy" 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>Convert to numpy</h3>
<pre><code class="hljs css language-python"><span class="hljs-meta">&gt;&gt;&gt; </span>a = np.asarray([[<span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>], [<span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>]], dtype=np.float32)
<span class="hljs-meta">&gt;&gt;&gt; </span>tensor.from_numpy(a)
[[<span class="hljs-number">1.</span> <span class="hljs-number">0.</span> <span class="hljs-number">0.</span>]
 [<span class="hljs-number">0.</span> <span class="hljs-number">1.</span> <span class="hljs-number">0.</span>]]
<span class="hljs-meta">&gt;&gt;&gt; </span>tensor.to_numpy(tensor.from_numpy(a))
array([[<span class="hljs-number">1.</span>, <span class="hljs-number">0.</span>, <span class="hljs-number">0.</span>],
       [<span class="hljs-number">0.</span>, <span class="hljs-number">1.</span>, <span class="hljs-number">0.</span>]], dtype=float32)
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="tensor-methods"></a><a href="#tensor-methods" 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 Methods</h3>
<pre><code class="hljs css language-python"><span class="hljs-meta">&gt;&gt;&gt; </span>t = tensor.from_numpy(a)
<span class="hljs-meta">&gt;&gt;&gt; </span>t.transpose([<span class="hljs-number">1</span>,<span class="hljs-number">0</span>])
[[<span class="hljs-number">1.</span> <span class="hljs-number">0.</span>]
 [<span class="hljs-number">0.</span> <span class="hljs-number">1.</span>]
 [<span class="hljs-number">0.</span> <span class="hljs-number">0.</span>]]
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="tensor-arithmetic-methods"></a><a href="#tensor-arithmetic-methods" 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 Arithmetic Methods</h3>
<p><code>tensor</code> is evaluated in real time.</p>
<pre><code class="hljs css language-python"><span class="hljs-meta">&gt;&gt;&gt; </span>t + <span class="hljs-number">1</span>
[[<span class="hljs-number">2.</span> <span class="hljs-number">1.</span> <span class="hljs-number">1.</span>]
 [<span class="hljs-number">1.</span> <span class="hljs-number">2.</span> <span class="hljs-number">1.</span>]]
<span class="hljs-meta">&gt;&gt;&gt; </span>t / <span class="hljs-number">5</span>
[[<span class="hljs-number">0.2</span> <span class="hljs-number">0.</span>  <span class="hljs-number">0.</span> ]
 [<span class="hljs-number">0.</span>  <span class="hljs-number">0.2</span> <span class="hljs-number">0.</span> ]]
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="tensor-functions"></a><a href="#tensor-functions" 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 Functions</h3>
<p>Functions in module <code>singa.tensor</code> return new <code>tensor</code> object after applying defined transformation in the function.</p>
<pre><code class="hljs css language-python"><span class="hljs-meta">&gt;&gt;&gt; </span>tensor.log(t+<span class="hljs-number">1</span>)
[[<span class="hljs-number">0.6931472</span> <span class="hljs-number">0.</span>        <span class="hljs-number">0.</span>       ]
 [<span class="hljs-number">0.</span>        <span class="hljs-number">0.6931472</span> <span class="hljs-number">0.</span>       ]]
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="tensor-on-different-devices"></a><a href="#tensor-on-different-devices" 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 on Different Devices</h3>
<p><code>tensor</code> is created on host(CPU) by default, and can also be created on different backends by specifiying the <code>device</code>. Existing <code>tensor</code> could also be moved between <code>device</code> by <code>to_device()</code>.</p>
<pre><code class="hljs css language-python"><span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> device
<span class="hljs-meta">&gt;&gt;&gt; </span>x = tensor.Tensor((<span class="hljs-number">2</span>, <span class="hljs-number">3</span>), device.create_cuda_gpu())
<span class="hljs-meta">&gt;&gt;&gt; </span>x.gaussian(<span class="hljs-number">1</span>,<span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>x
[[<span class="hljs-number">1.531889</span>   <span class="hljs-number">1.0128608</span>  <span class="hljs-number">0.12691343</span>]
 [<span class="hljs-number">2.1674204</span>  <span class="hljs-number">3.083676</span>   <span class="hljs-number">2.7421203</span> ]]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># move to host</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>x.to_device(device.get_default_device())
</code></pre>
<h3><a class="anchor" aria-hidden="true" id="simple-neural-network-example"></a><a href="#simple-neural-network-example" 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>Simple Neural Network Example</h3>
<pre><code class="hljs css language-python"><span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> device
<span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> tensor
<span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> opt
<span class="hljs-keyword">from</span> singa <span class="hljs-keyword">import</span> autograd
<span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">MLP</span>:</span>
    <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">__init__</span><span class="hljs-params">(self)</span>:</span>
        self.linear1 = autograd.Linear(<span class="hljs-number">3</span>, <span class="hljs-number">4</span>)
        self.linear2 = autograd.Linear(<span class="hljs-number">4</span>, <span class="hljs-number">5</span>)
    <span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">forward</span><span class="hljs-params">(self, x)</span>:</span>
        y=self.linear1(x)
        <span class="hljs-keyword">return</span> self.linear2(y)
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">train</span><span class="hljs-params">(model, x, t, dev, epochs=<span class="hljs-number">10</span>)</span>:</span>
    <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(epochs):
        y = model.forward(x)
        loss = autograd.mse_loss(y, t)
        print(<span class="hljs-string">"loss: "</span>, loss)
        sgd = opt.SGD()
        <span class="hljs-keyword">for</span> p, gp <span class="hljs-keyword">in</span> autograd.backward(loss):
            sgd.update(p, gp)
        sgd.step()
    print(<span class="hljs-string">"training completed"</span>)
<span class="hljs-keyword">if</span> __name__ == <span class="hljs-string">"__main__"</span>:
    autograd.training = <span class="hljs-literal">True</span>
    model = MLP()
    dev = device.get_default_device()
    x = tensor.Tensor((<span class="hljs-number">2</span>, <span class="hljs-number">3</span>), dev)
    t = tensor.Tensor((<span class="hljs-number">2</span>, <span class="hljs-number">5</span>), dev)
    x.gaussian(<span class="hljs-number">1</span>,<span class="hljs-number">1</span>)
    t.gaussian(<span class="hljs-number">1</span>,<span class="hljs-number">1</span>)
    train(model, x, t, dev)
</code></pre>
<p>Output:</p>
<pre><code class="hljs">loss:  [<span class="hljs-number">4.917431</span>]
loss:  [<span class="hljs-number">2.5147934</span>]
loss:  [<span class="hljs-number">2.0670078</span>]
loss:  [<span class="hljs-number">1.9179827</span>]
loss:  [<span class="hljs-number">1.8192691</span>]
loss:  [<span class="hljs-number">1.7269677</span>]
loss:  [<span class="hljs-number">1.6308627</span>]
loss:  [<span class="hljs-number">1.52674</span>]
loss:  [<span class="hljs-number">1.4122975</span>]
loss:  [<span class="hljs-number">1.2866782</span>]
training completed
</code></pre>
<h2><a class="anchor" aria-hidden="true" id="tensor-implementation"></a><a href="#tensor-implementation" 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 implementation</h2>
<p>SINGA has three different sets of implmentations of Tensor functions, one for each type of Device.</p>
<ul>
<li>'tensor_math_cpp.h' implements operations using Cpp (with CBLAS) for CppGPU devices.</li>
<li>'tensor_math_cuda.h' implements operations using Cuda (with cuBLAS) for CudaGPU devices.</li>
<li>'tensor_math_opencl.h' implements operations using OpenCL for OpenclGPU devices.</li>
</ul>
<h2><a class="anchor" aria-hidden="true" id="python-api"></a><a href="#python-api" 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 API</h2>
<p><em>work in progress</em></p>
<h2><a class="anchor" aria-hidden="true" id="cpp-api"></a><a href="#cpp-api" 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>CPP API</h2>
<p><em>work in progress</em></p>
</span></div></article></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/2.0.0/device"><span class="arrow-prev">← </span><span>Device</span></a><a class="docs-next button" href="/docs/2.0.0/autograd"><span>Autograd</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#tensor-usage">Tensor Usage</a><ul class="toc-headings"><li><a href="#create-tensor">Create Tensor</a></li><li><a href="#convert-to-numpy">Convert to numpy</a></li><li><a href="#tensor-methods">Tensor Methods</a></li><li><a href="#tensor-arithmetic-methods">Tensor Arithmetic Methods</a></li><li><a href="#tensor-functions">Tensor Functions</a></li><li><a href="#tensor-on-different-devices">Tensor on Different Devices</a></li><li><a href="#simple-neural-network-example">Simple Neural Network Example</a></li></ul></li><li><a href="#tensor-implementation">Tensor implementation</a></li><li><a href="#python-api">Python API</a></li><li><a href="#cpp-api">CPP API</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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