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 <div class="section" id="mxnet-0-9-nnvm-release-note">
 <span id="mxnet-0-9-nnvm-release-note"></span><h1>MXNet 0.9 (NNVM) Release Note<a class="headerlink" href="#mxnet-0-9-nnvm-release-note" title="Permalink to this headline">¶</a></h1>
 <p>Version 0.9 brings a number of important features and changes, including a back-end refactor to adopt the <a class="reference external" href="https://github.com/dmlc/nnvm">NNVM</a> framework, a profiler for analyzing performance, a fast image IO and augmentation module that bypasses GIL, and various other changes.</p>
 <div class="section" id="nnvm-refactor">
 <span id="nnvm-refactor"></span><h2>NNVM Refactor<a class="headerlink" href="#nnvm-refactor" title="Permalink to this headline">¶</a></h2>
 <p>NNVM is a library for neural network graph construction, optimization, and operator registration. It serves as an intermediary layer between the front-end (MXNet user API) and the back-end (computation on the device). After version 0.9, MXNet fully adopts the NNVM framework. Now it’s easier to create operators. You can also register “pass”es that process and optimizes the graph when <code class="docutils literal"><span class="pre">bind</span></code> is called on the symbol. For more discussion on how to create operators with NNVM, please refer to <a class="reference internal" href="../how_to/new_op.html"><em>How to Create New Operators</em></a></p>
 <p>Other changes brought by NNVM include:</p>
 <ul class="simple">
 <li>Backward shape inference is now supported</li>
 <li>All operators can now be used with both symbolic and ndarray API. For example, <code class="docutils literal"><span class="pre">mx.nd.Activation(x,</span> <span class="pre">act_type='relu')</span></code> works now.</li>
 <li>Optional cython API for mx.symbol and mx.ndarray is now available. Use <code class="docutils literal"><span class="pre">make</span> <span class="pre">cython</span></code> to activate it for accelerated communication with the back-end.</li>
 </ul>
 </div>
 <div class="section" id="profiler">
 <span id="profiler"></span><h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this headline">¶</a></h2>
 <p><img alt="MLP Profile" src="https://cloud.githubusercontent.com/assets/17693755/18035938/0a43484a-6d93-11e6-80d4-241c6ca552ea.png"/></p>
 <p>MXNet now provides a native profiler for analyzing the performance of operators. This feature compliments general profiling tools like nvprof and gprof by summarizing at the operator level, instead of function, kernel, or instruction level.</p>
 <p>To use this feature, first set <code class="docutils literal"><span class="pre">USE_PROFILER</span> <span class="pre">=</span> <span class="pre">1</span></code> in <code class="docutils literal"><span class="pre">config.mk</span></code> and rebuild mxnet. Then add three lines at the beginning and end of the section of your program you want to profile:</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_config</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">scope</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">fname</span><span class="p">)</span>
 <span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_state</span><span class="p">(</span><span class="s1">'run'</span><span class="p">)</span>

 <span class="c1"># do computation ...</span>

 <span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_state</span><span class="p">(</span><span class="s1">'stop'</span><span class="p">)</span>
 </pre></div>
 </div>
 <p><code class="docutils literal"><span class="pre">scope</span></code> can be ‘symbolic’ (to only include symbolic operations) or ‘all’ (to include all operations), and <code class="docutils literal"><span class="pre">fname</span></code> is the path to save profiler output.</p>
 <p>After program finishes, navigate to <a class="reference external" href="chrome://tracing">chrome://tracing</a> in a Chrome browser and load profiler output to see the results.</p>
 </div>
 <div class="section" id="image-io">
 <span id="image-io"></span><h2>Image IO<a class="headerlink" href="#image-io" title="Permalink to this headline">¶</a></h2>
 <p>MXNet already has <code class="docutils literal"><span class="pre">mx.io.ImageRecordIter</span></code> for loading and preprocessing images. However, some tasks need more flexible image processing API. Detection, for example, requires transforming labels together with images. Usually, people write custom data iterators in python to handle this. But due to the infamous Global Interpreter Lock (GIL), python scripts cannot use multithreading to speed up processing.</p>
 <p><code class="docutils literal"><span class="pre">mx.image</span></code> provides a set of fast image processing API that leverage MXNet Engine to automatically parallelize processing. You can write</p>
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">imgs</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">imdecode</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">())</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">img_paths</span><span class="p">]</span>
 </pre></div>
 </div>
 <p>and decoding will be automatically run in parallel.</p>
 </div>
 <div class="section" id="miscellaneous">
 <span id="miscellaneous"></span><h2>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
 <li>sgd and adam optimizer are now implemented with a single imperative call. They should be as fast and memory efficient as cc optimizers. ccsgd is now deprecated and redirects to sgd.</li>
 <li>Layout support is added. Use <code class="docutils literal"><span class="pre">mx.io.DataDesc(...,</span> <span class="pre">layout='NHWC')</span></code> in provide_data to specify data layout. use <code class="docutils literal"><span class="pre">mx.sym.YourSymbol(...,</span> <span class="pre">__layout__='NHWC')</span></code> to specify output layout. <code class="docutils literal"><span class="pre">layout</span></code> option is now available for Convolution layer.</li>
 <li>element_mask is removed. Please use src*mask.reshape((mask.size, 1, 1, ..., 1)) directly as binary ops now support broadcasting.</li>
 <li>sum_axis, max_axis, and min_axis are deprecated. Please use mx.nd.max(src, axis=n) instead.</li>
 <li>symbol attributes are now limited to ctx_group, lr_mult, wd_mult, force_mirroring. All other custom attributes need to be in <strong>xxx</strong> format (start and end with double underscore) or an error will be triggered during attribute parsing.</li>
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 <h3><a href="../index.html">Table Of Contents</a></h3>
 <ul>
 <li><a class="reference internal" href="#">MXNet 0.9 (NNVM) Release Note</a><ul>
 <li><a class="reference internal" href="#nnvm-refactor">NNVM Refactor</a></li>
 <li><a class="reference internal" href="#profiler">Profiler</a></li>
 <li><a class="reference internal" href="#image-io">Image IO</a></li>
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	<div class="section" id="mxnet-0-9-nnvm-release-note">
	<span id="mxnet-0-9-nnvm-release-note"></span><h1>MXNet 0.9 (NNVM) Release Note<a class="headerlink" href="#mxnet-0-9-nnvm-release-note" title="Permalink to this headline">¶</a></h1>
	<p>Version 0.9 brings a number of important features and changes, including a back-end refactor to adopt the <a class="reference external" href="https://github.com/dmlc/nnvm">NNVM</a> framework, a profiler for analyzing performance, a fast image IO and augmentation module that bypasses GIL, and various other changes.</p>
	<div class="section" id="nnvm-refactor">
	<span id="nnvm-refactor"></span><h2>NNVM Refactor<a class="headerlink" href="#nnvm-refactor" title="Permalink to this headline">¶</a></h2>
	<p>NNVM is a library for neural network graph construction, optimization, and operator registration. It serves as an intermediary layer between the front-end (MXNet user API) and the back-end (computation on the device). After version 0.9, MXNet fully adopts the NNVM framework. Now it’s easier to create operators. You can also register “pass”es that process and optimizes the graph when <code class="docutils literal"><span class="pre">bind</span></code> is called on the symbol. For more discussion on how to create operators with NNVM, please refer to <a class="reference internal" href="../how_to/new_op.html"><em>How to Create New Operators</em></a></p>
	<p>Other changes brought by NNVM include:</p>
	<ul class="simple">
	<li>Backward shape inference is now supported</li>
	<li>All operators can now be used with both symbolic and ndarray API. For example, <code class="docutils literal"><span class="pre">mx.nd.Activation(x,</span> <span class="pre">act_type='relu')</span></code> works now.</li>
	<li>Optional cython API for mx.symbol and mx.ndarray is now available. Use <code class="docutils literal"><span class="pre">make</span> <span class="pre">cython</span></code> to activate it for accelerated communication with the back-end.</li>
	</ul>
	</div>
	<div class="section" id="profiler">
	<span id="profiler"></span><h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this headline">¶</a></h2>
	<p><img alt="MLP Profile" src="https://cloud.githubusercontent.com/assets/17693755/18035938/0a43484a-6d93-11e6-80d4-241c6ca552ea.png"/></p>
	<p>MXNet now provides a native profiler for analyzing the performance of operators. This feature compliments general profiling tools like nvprof and gprof by summarizing at the operator level, instead of function, kernel, or instruction level.</p>
	<p>To use this feature, first set <code class="docutils literal"><span class="pre">USE_PROFILER</span> <span class="pre">=</span> <span class="pre">1</span></code> in <code class="docutils literal"><span class="pre">config.mk</span></code> and rebuild mxnet. Then add three lines at the beginning and end of the section of your program you want to profile:</p>
	<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">mx</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_config</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">scope</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">fname</span><span class="p">)</span>
	<span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_state</span><span class="p">(</span><span class="s1">'run'</span><span class="p">)</span>

	<span class="c1"># do computation ...</span>

	<span class="n">profiler</span><span class="o">.</span><span class="n">profiler_set_state</span><span class="p">(</span><span class="s1">'stop'</span><span class="p">)</span>
	</pre></div>
	</div>
	<p><code class="docutils literal"><span class="pre">scope</span></code> can be ‘symbolic’ (to only include symbolic operations) or ‘all’ (to include all operations), and <code class="docutils literal"><span class="pre">fname</span></code> is the path to save profiler output.</p>
	<p>After program finishes, navigate to <a class="reference external" href="chrome://tracing">chrome://tracing</a> in a Chrome browser and load profiler output to see the results.</p>
	</div>
	<div class="section" id="image-io">
	<span id="image-io"></span><h2>Image IO<a class="headerlink" href="#image-io" title="Permalink to this headline">¶</a></h2>
	<p>MXNet already has <code class="docutils literal"><span class="pre">mx.io.ImageRecordIter</span></code> for loading and preprocessing images. However, some tasks need more flexible image processing API. Detection, for example, requires transforming labels together with images. Usually, people write custom data iterators in python to handle this. But due to the infamous Global Interpreter Lock (GIL), python scripts cannot use multithreading to speed up processing.</p>
	<p><code class="docutils literal"><span class="pre">mx.image</span></code> provides a set of fast image processing API that leverage MXNet Engine to automatically parallelize processing. You can write</p>
	<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">imgs</span> <span class="o">=</span> <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">imdecode</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">())</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">img_paths</span><span class="p">]</span>
	</pre></div>
	</div>
	<p>and decoding will be automatically run in parallel.</p>
	</div>
	<div class="section" id="miscellaneous">
	<span id="miscellaneous"></span><h2>Miscellaneous<a class="headerlink" href="#miscellaneous" title="Permalink to this headline">¶</a></h2>
	<ul class="simple">
	<li>sgd and adam optimizer are now implemented with a single imperative call. They should be as fast and memory efficient as cc optimizers. ccsgd is now deprecated and redirects to sgd.</li>
	<li>Layout support is added. Use <code class="docutils literal"><span class="pre">mx.io.DataDesc(...,</span> <span class="pre">layout='NHWC')</span></code> in provide_data to specify data layout. use <code class="docutils literal"><span class="pre">mx.sym.YourSymbol(...,</span> <span class="pre">__layout__='NHWC')</span></code> to specify output layout. <code class="docutils literal"><span class="pre">layout</span></code> option is now available for Convolution layer.</li>
	<li>element_mask is removed. Please use src*mask.reshape((mask.size, 1, 1, ..., 1)) directly as binary ops now support broadcasting.</li>
	<li>sum_axis, max_axis, and min_axis are deprecated. Please use mx.nd.max(src, axis=n) instead.</li>
	<li>symbol attributes are now limited to ctx_group, lr_mult, wd_mult, force_mirroring. All other custom attributes need to be in <strong>xxx</strong> format (start and end with double underscore) or an error will be triggered during attribute parsing.</li>
	</ul>
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	<h3><a href="../index.html">Table Of Contents</a></h3>
	<ul>
	<li><a class="reference internal" href="#">MXNet 0.9 (NNVM) Release Note</a><ul>
	<li><a class="reference internal" href="#nnvm-refactor">NNVM Refactor</a></li>
	<li><a class="reference internal" href="#profiler">Profiler</a></li>
	<li><a class="reference internal" href="#image-io">Image IO</a></li>
	<li><a class="reference internal" href="#miscellaneous">Miscellaneous</a></li>
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