update sparseml docs from latest on main (#3)

Author: markurtz

sparseml/_modules/sparseml/optim/modifier.html

@@ -470,8 +470,24 @@ <h1>Source code for sparseml.optim.modifier</h1><div class="highlight"><pre>
             <span class="n">modifiers</span> <span class="o">=</span> <span class="p">[</span><span class="n">container</span><span class="p">]</span>
         <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">List</span><span class="p">):</span>
             <span class="n">modifiers</span> <span class="o">=</span> <span class="n">container</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">modifiers</span> <span class="o">=</span> <span class="n">container</span><span class="p">[</span><span class="s2">&quot;modifiers&quot;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>  <span class="c1"># Dict</span>
+            <span class="n">modifiers</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">container</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                <span class="k">if</span> <span class="s2">&quot;modifiers&quot;</span> <span class="ow">in</span> <span class="n">name</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">List</span><span class="p">):</span>
+                    <span class="n">modifiers</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">BaseModifier</span><span class="p">):</span>
+                    <span class="n">modifiers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">List</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span>
+                    <span class="nb">isinstance</span><span class="p">(</span><span class="n">element</span><span class="p">,</span> <span class="n">BaseModifier</span><span class="p">)</span> <span class="k">for</span> <span class="n">element</span> <span class="ow">in</span> <span class="n">item</span>
+                <span class="p">):</span>
+                    <span class="n">modifier_type</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                        <span class="p">[</span><span class="n">mod</span> <span class="k">for</span> <span class="n">mod</span> <span class="ow">in</span> <span class="n">item</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="n">BaseModifier</span><span class="p">)][</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="p">)</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="s2">&quot;Invalid modifier location. Grouped modifiers in recipes must &quot;</span>
+                        <span class="s2">&quot;be listed in lists with &#39;modifiers&#39; in its name. A modifier of &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;type </span><span class="si">{</span><span class="n">modifier_type</span><span class="si">}</span><span class="s2"> was found in recipe list </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="p">)</span>
 
         <span class="k">return</span> <span class="n">modifiers</span></div>
 

sparseml/_modules/sparseml/tensorflow_v1/utils/variable.html

@@ -197,6 +197,7 @@ <h1>Source code for sparseml.tensorflow_v1.utils.variable</h1><div class="highli
 <span class="k">try</span><span class="p">:</span>
     <span class="kn">import</span> <span class="nn">tensorflow.contrib.graph_editor</span> <span class="k">as</span> <span class="nn">graph_editor</span>
     <span class="kn">from</span> <span class="nn">tensorflow.contrib.graph_editor.util</span> <span class="kn">import</span> <span class="n">ListView</span>
+
     <span class="n">tf_contrib_err</span> <span class="o">=</span> <span class="kc">None</span>
 <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
     <span class="n">graph_editor</span> <span class="o">=</span> <span class="kc">None</span>

sparseml/_sources/index.rst.txt

@@ -50,13 +50,13 @@ Overview
 ========
 
 SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art optimization
-algorithms such as `pruning <https://neuralmagic.com/blog/pruning-overview/ />`_ and
+algorithms such as `pruning <https://neuralmagic.com/blog/pruning-overview />`_ and
 `quantization <https://arxiv.org/abs/1609.07061 />`_ to any neural network.
 General, recipe-driven approaches built around these optimizations enable the simplification of creating faster
 and smaller models for the ML performance community at large.
 
-SparseML is integrated for easy model optimizations within the `PyTorch <https://pytorch.org/ />`_,
-`Keras <https://keras.io/ />`_, and `TensorFlow V1 <http://tensorflow.org/ />`_ ecosystems currently.
+SparseML is integrated for easy model optimizations within the `PyTorch <https://pytorch.org />`_,
+`Keras <https://keras.io />`_, and `TensorFlow V1 <http://tensorflow.org />`_ ecosystems currently.
 
 Related Products
 ================
@@ -72,19 +72,19 @@ Related Products
 Resources and Learning More
 ===========================
 
-- `SparseZoo Documentation <https://docs.neuralmagic.com/sparsezoo/ />`_
-- `Sparsify Documentation <https://docs.neuralmagic.com/sparsify/ />`_
-- `DeepSparse Documentation <https://docs.neuralmagic.com/deepsparse/ />`_
-- `Neural Magic Blog <https://www.neuralmagic.com/blog/ />`_,
-  `Resources <https://www.neuralmagic.com/resources/ />`_,
-  `Website <https://www.neuralmagic.com/ />`_
+- `SparseZoo Documentation <https://docs.neuralmagic.com/sparsezoo />`_
+- `Sparsify Documentation <https://docs.neuralmagic.com/sparsify />`_
+- `DeepSparse Documentation <https://docs.neuralmagic.com/deepsparse />`_
+- `Neural Magic Blog <https://www.neuralmagic.com/blog />`_,
+  `Resources <https://www.neuralmagic.com/resources />`_,
+  `Website <https://www.neuralmagic.com />`_
 
 Release History
 ===============
 
 Official builds are hosted on PyPi
-- stable: `sparseml <https://pypi.org/project/sparseml/ />`_
-- nightly (dev): `sparseml-nightly <https://pypi.org/project/sparseml-nightly/ />`_
+- stable: `sparseml <https://pypi.org/project/sparseml />`_
+- nightly (dev): `sparseml-nightly <https://pypi.org/project/sparseml-nightly />`_
 
 Additionally, more information can be found via
 `GitHub Releases <https://github.com/neuralmagic/sparseml/releases />`_.

sparseml/_sources/recipes.md.txt

@@ -25,6 +25,8 @@ The files are written in [YAML](https://yaml.org/) and stored in YAML or
 The rest of the SparseML system is coded to parse the recipe files into a native format for the desired framework
 and apply the modifications to the model and training pipeline.
 
+In a recipe, modifiers must be written in a list that includes "modifiers" in its name.
+
 The easiest ways to get or create optimization recipes are by either using 
 the pre-configured recipes in [SparseZoo](https://github.com/neuralmagic/sparsezoo) or 
 using [Sparsify's](https://github.com/neuralmagic/sparsify) autoML style creation.

sparseml/index.html

@@ -205,12 +205,12 @@ <h1>SparseML 0.1<a class="headerlink" href="#sparseml-version" title="Permalink
  </div><div class="section" id="overview">
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p>SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art optimization
-algorithms such as <a class="reference external" href="https://neuralmagic.com/blog/pruning-overview//">pruning</a> and
+algorithms such as <a class="reference external" href="https://neuralmagic.com/blog/pruning-overview/">pruning</a> and
 <a class="reference external" href="https://arxiv.org/abs/1609.07061/">quantization</a> to any neural network.
 General, recipe-driven approaches built around these optimizations enable the simplification of creating faster
 and smaller models for the ML performance community at large.</p>
-<p>SparseML is integrated for easy model optimizations within the <a class="reference external" href="https://pytorch.org//">PyTorch</a>,
-<a class="reference external" href="https://keras.io//">Keras</a>, and <a class="reference external" href="http://tensorflow.org//">TensorFlow V1</a> ecosystems currently.</p>
+<p>SparseML is integrated for easy model optimizations within the <a class="reference external" href="https://pytorch.org/">PyTorch</a>,
+<a class="reference external" href="https://keras.io/">Keras</a>, and <a class="reference external" href="http://tensorflow.org/">TensorFlow V1</a> ecosystems currently.</p>
 </div>
 <div class="section" id="related-products">
 <h2>Related Products<a class="headerlink" href="#related-products" title="Permalink to this headline">¶</a></h2>
@@ -227,19 +227,19 @@ <h2>Related Products<a class="headerlink" href="#related-products" title="Permal
 <div class="section" id="resources-and-learning-more">
 <h2>Resources and Learning More<a class="headerlink" href="#resources-and-learning-more" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
-<li><p><a class="reference external" href="https://docs.neuralmagic.com/sparsezoo//">SparseZoo Documentation</a></p></li>
-<li><p><a class="reference external" href="https://docs.neuralmagic.com/sparsify//">Sparsify Documentation</a></p></li>
-<li><p><a class="reference external" href="https://docs.neuralmagic.com/deepsparse//">DeepSparse Documentation</a></p></li>
-<li><p><a class="reference external" href="https://www.neuralmagic.com/blog//">Neural Magic Blog</a>,
-<a class="reference external" href="https://www.neuralmagic.com/resources//">Resources</a>,
-<a class="reference external" href="https://www.neuralmagic.com//">Website</a></p></li>
+<li><p><a class="reference external" href="https://docs.neuralmagic.com/sparsezoo/">SparseZoo Documentation</a></p></li>
+<li><p><a class="reference external" href="https://docs.neuralmagic.com/sparsify/">Sparsify Documentation</a></p></li>
+<li><p><a class="reference external" href="https://docs.neuralmagic.com/deepsparse/">DeepSparse Documentation</a></p></li>
+<li><p><a class="reference external" href="https://www.neuralmagic.com/blog/">Neural Magic Blog</a>,
+<a class="reference external" href="https://www.neuralmagic.com/resources/">Resources</a>,
+<a class="reference external" href="https://www.neuralmagic.com/">Website</a></p></li>
 </ul>
 </div>
 <div class="section" id="release-history">
 <h2>Release History<a class="headerlink" href="#release-history" title="Permalink to this headline">¶</a></h2>
 <p>Official builds are hosted on PyPi
-- stable: <a class="reference external" href="https://pypi.org/project/sparseml//">sparseml</a>
-- nightly (dev): <a class="reference external" href="https://pypi.org/project/sparseml-nightly//">sparseml-nightly</a></p>
+- stable: <a class="reference external" href="https://pypi.org/project/sparseml/">sparseml</a>
+- nightly (dev): <a class="reference external" href="https://pypi.org/project/sparseml-nightly/">sparseml-nightly</a></p>
 <p>Additionally, more information can be found via
 <a class="reference external" href="https://github.com/neuralmagic/sparseml/releases/">GitHub Releases</a>.</p>
 <div class="toctree-wrapper compound">

sparseml/recipes.html

@@ -226,6 +226,7 @@ <h1>Optimization Recipes<a class="headerlink" href="#optimization-recipes" title
 <a class="reference external" href="https://assemble.io/docs/YAML-front-matter.html">YAML front matter</a>.
 The rest of the SparseML system is coded to parse the recipe files into a native format for the desired framework
 and apply the modifications to the model and training pipeline.</p>
+<p>In a recipe, modifiers must be written in a list that includes “modifiers” in its name.</p>
 <p>The easiest ways to get or create optimization recipes are by either using
 the pre-configured recipes in <a class="reference external" href="https://github.com/neuralmagic/sparsezoo">SparseZoo</a> or
 using <a class="reference external" href="https://github.com/neuralmagic/sparsify">Sparsify’s</a> autoML style creation.</p>