rebuild docs based on latest repos (#22)

Co-authored-by: Jenkins <jenkins@neuralmagic.com>

deepsparse/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 09cf2dae090a5215faeb91fd805fe11e
+config: c0e84c5bd7f1ae1547decebf78328f3b
 tags: 645f666f9bcd5a90fca523b33c5a78b7

deepsparse/_modules/deepsparse/engine.html

@@ -303,9 +303,6 @@ <h1>Source code for deepsparse.engine</h1><div class="highlight"><pre>
 <span class="sd">    Note 1: Engines are compiled for a specific batch size and</span>
 <span class="sd">    for a specific number of CPU cores.</span>
 
-<span class="sd">    Note 2: multi socket support is not yet built in to the Engine,</span>
-<span class="sd">    all execution assumes single socket</span>
-
 <span class="sd">    | Example:</span>
 <span class="sd">    |    # create an engine for batch size 1 on all available cores</span>
 <span class="sd">    |    engine = Engine(&quot;path/to/onnx&quot;, batch_size=1, num_cores=None)</span>
@@ -405,8 +402,7 @@ <h1>Source code for deepsparse.engine</h1><div class="highlight"><pre>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">num_sockets</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
         <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        :return: The number of sockets the engine is compiled to run on;</span>
-<span class="sd">            only current support is 1</span>
+<span class="sd">        :return: The number of sockets the engine is compiled to run on</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_sockets</span>
 
@@ -682,8 +678,8 @@ <h1>Source code for deepsparse.engine</h1><div class="highlight"><pre>
     <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Convenience function to compile a model in the DeepSparse Engine</span>
 <span class="sd">    from an ONNX file for inference.</span>
-<span class="sd">    Gives defaults of batch_size == 1 and num_cores == None</span>
-<span class="sd">    (will use all physical cores available on a single socket).</span>
+<span class="sd">    Gives defaults of batch_size == 1, num_cores == None and num_sockets = None</span>
+<span class="sd">    (will use all physical cores available on all available sockets).</span>
 
 <span class="sd">    :param model: Either a path to the model&#39;s onnx file, a SparseZoo model stub</span>
 <span class="sd">        prefixed by &#39;zoo:&#39;, a SparseZoo Model object, or a SparseZoo ONNX File</span>
@@ -718,6 +714,8 @@ <h1>Source code for deepsparse.engine</h1><div class="highlight"><pre>
 <span class="sd">    Gives defaults of batch_size == 1 and num_cores == None</span>
 <span class="sd">    (will use all physical cores available on a single socket).</span>
 
+<span class="sd">    Note 1: Benchmarking is currently only supported on a single socket.</span>
+
 <span class="sd">    :param model: Either a path to the model&#39;s onnx file, a SparseZoo model stub</span>
 <span class="sd">        prefixed by &#39;zoo:&#39;, a SparseZoo Model object, or a SparseZoo ONNX File</span>
 <span class="sd">        object that defines the neural network</span>
@@ -773,6 +771,8 @@ <h1>Source code for deepsparse.engine</h1><div class="highlight"><pre>
 <span class="sd">    Gives defaults of batch_size == 1 and num_cores == None</span>
 <span class="sd">    (will use all physical cores available on a single socket).</span>
 
+<span class="sd">    Note 1: Analysis is currently only supported on a single socket.</span>
+
 <span class="sd">    :param model: Either a path to the model&#39;s onnx file, a SparseZoo model stub</span>
 <span class="sd">        prefixed by &#39;zoo:&#39;, a SparseZoo Model object, or a SparseZoo ONNX File</span>
 <span class="sd">        object that defines the neural network graph definition to analyze</span>

deepsparse/_modules/deepsparse/utils/data.html

@@ -201,11 +201,54 @@ <h1>Source code for deepsparse.utils.data</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">deepsparse.utils.log</span> <span class="kn">import</span> <span class="n">log_init</span>
 
 
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;verify_outputs&quot;</span><span class="p">]</span>
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;arrays_to_bytes&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;bytes_to_arrays&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;verify_outputs&quot;</span><span class="p">,</span>
+<span class="p">]</span>
 
 <span class="n">log</span> <span class="o">=</span> <span class="n">log_init</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="vm">__file__</span><span class="p">))</span>
 
 
+<div class="viewcode-block" id="arrays_to_bytes"><a class="viewcode-back" href="../../../api/deepsparse.utils.html#deepsparse.utils.data.arrays_to_bytes">[docs]</a><span class="k">def</span> <span class="nf">arrays_to_bytes</span><span class="p">(</span><span class="n">arrays</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">bytearray</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    :param arrays: List of numpy arrays to serialize as bytes</span>
+<span class="sd">    :return: bytearray representation of list of numpy arrays</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">to_return</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">arr</span> <span class="ow">in</span> <span class="n">arrays</span><span class="p">:</span>
+        <span class="n">arr_dtype</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span> <span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
+        <span class="n">arr_shape</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">arr</span><span class="o">.</span><span class="n">shape</span><span class="p">]),</span> <span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
+        <span class="n">sep</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(</span><span class="s2">&quot;|&quot;</span><span class="p">,</span> <span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
+        <span class="n">arr_bytes</span> <span class="o">=</span> <span class="n">arr</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span><span class="o">.</span><span class="n">tobytes</span><span class="p">()</span>
+        <span class="n">to_return</span> <span class="o">+=</span> <span class="n">arr_dtype</span> <span class="o">+</span> <span class="n">sep</span> <span class="o">+</span> <span class="n">arr_shape</span> <span class="o">+</span> <span class="n">sep</span> <span class="o">+</span> <span class="n">arr_bytes</span>
+    <span class="k">return</span> <span class="n">to_return</span></div>
+
+
+<div class="viewcode-block" id="bytes_to_arrays"><a class="viewcode-back" href="../../../api/deepsparse.utils.html#deepsparse.utils.data.bytes_to_arrays">[docs]</a><span class="k">def</span> <span class="nf">bytes_to_arrays</span><span class="p">(</span><span class="n">serialized_arr</span><span class="p">:</span> <span class="nb">bytearray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">]:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    :param serialized_arr: bytearray representation of list of numpy arrays</span>
+<span class="sd">    :return: List of numpy arrays decoded from input</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">sep</span> <span class="o">=</span> <span class="s2">&quot;|&quot;</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span>
+    <span class="n">arrays</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">i_start</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">while</span> <span class="n">i_start</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">serialized_arr</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">i_0</span> <span class="o">=</span> <span class="n">serialized_arr</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">sep</span><span class="p">,</span> <span class="n">i_start</span><span class="p">)</span>
+        <span class="n">i_1</span> <span class="o">=</span> <span class="n">serialized_arr</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="n">sep</span><span class="p">,</span> <span class="n">i_0</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">arr_dtype</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">serialized_arr</span><span class="p">[</span><span class="n">i_start</span><span class="p">:</span><span class="n">i_0</span><span class="p">]</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">))</span>
+        <span class="n">arr_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span>
+            <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">a</span><span class="p">)</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">serialized_arr</span><span class="p">[</span><span class="n">i_0</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:</span> <span class="n">i_1</span><span class="p">]</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s2">&quot;utf-8&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)]</span>
+        <span class="p">)</span>
+        <span class="n">arr_num_bytes</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span><span class="n">arr_shape</span><span class="p">)</span> <span class="o">*</span> <span class="n">arr_dtype</span><span class="o">.</span><span class="n">itemsize</span>
+        <span class="n">arr_str</span> <span class="o">=</span> <span class="n">serialized_arr</span><span class="p">[</span><span class="n">i_1</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:</span> <span class="n">arr_num_bytes</span> <span class="o">+</span> <span class="p">(</span><span class="n">i_1</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
+        <span class="n">arr</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">frombuffer</span><span class="p">(</span><span class="n">arr_str</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">arr_dtype</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">arr_shape</span><span class="p">)</span>
+        <span class="n">arrays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">arr</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>
+
+        <span class="n">i_start</span> <span class="o">=</span> <span class="n">i_1</span> <span class="o">+</span> <span class="n">arr_num_bytes</span> <span class="o">+</span> <span class="mi">1</span>
+    <span class="k">return</span> <span class="n">arrays</span></div>
+
+
 <div class="viewcode-block" id="verify_outputs"><a class="viewcode-back" href="../../../api/deepsparse.utils.html#deepsparse.utils.data.verify_outputs">[docs]</a><span class="k">def</span> <span class="nf">verify_outputs</span><span class="p">(</span>
     <span class="n">outputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">],</span>
     <span class="n">gt_outputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">],</span>

deepsparse/_sources/debugging-optimizing/numactl-utility.md.txt

@@ -52,7 +52,7 @@ Given the architecture above, to run the DeepSparse Engine on the first four CPU
 
 Appending `--preferred 1` is needed here since the DeepSparse Engine is being bound to CPUs on the second socket.
 
-Note that using more than two sockets may not offer improvements over two sockets; if you have options, try different scenarios to see which setup is ideal for your use case. For batch size considerations, use an amount that is evenly divisible by the number of sockets you intend to use.
+Note: When running on multiple sockets using a batch size that is evenly divisible by the number of sockets will yield the best performance.
 
 
 ## DeepSparse Engine and Thread Pinning

deepsparse/_static/doctools.js

@@ -29,14 +29,9 @@ if (!window.console || !console.firebug) {
 
 /**
  * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
  */
 jQuery.urldecode = function(x) {
-  if (!x) {
-    return x
-  }
-  return decodeURIComponent(x.replace(/\+/g, ' '));
+  return decodeURIComponent(x).replace(/\+/g, ' ');
 };
 
 /**

deepsparse/_static/language_data.js

@@ -13,8 +13,7 @@
 var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
-
+/* Non-minified version JS is _stemmer.js if file is provided */ 
 /**
  * Porter Stemmer
  */
@@ -200,6 +199,7 @@ var Stemmer = function() {
 
 
 
+
 var splitChars = (function() {
     var result = {};
     var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,

deepsparse/_static/pygments.css

@@ -1,10 +1,5 @@
-pre { line-height: 125%; }
-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 .highlight .hll { background-color: #ffffcc }
-.highlight { background: #eeffcc; }
+.highlight  { background: #eeffcc; }
 .highlight .c { color: #408090; font-style: italic } /* Comment */
 .highlight .err { border: 1px solid #FF0000 } /* Error */
 .highlight .k { color: #007020; font-weight: bold } /* Keyword */

deepsparse/_static/searchtools.js

@@ -248,7 +248,7 @@ var Search = {
       // results left, load the summary and display it
       if (results.length) {
         var item = results.pop();
-        var listItem = $('<li></li>');
+        var listItem = $('<li style="display:none"></li>');
         var requestUrl = "";
         var linkUrl = "";
         if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
@@ -273,9 +273,9 @@ var Search = {
         if (item[3]) {
           listItem.append($('<span> (' + item[3] + ')</span>'));
           Search.output.append(listItem);
-          setTimeout(function() {
+          listItem.slideDown(5, function() {
             displayNextItem();
-          }, 5);
+          });
         } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
           $.ajax({url: requestUrl,
                   dataType: "text",
@@ -285,16 +285,16 @@ var Search = {
                       listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
                     }
                     Search.output.append(listItem);
-                    setTimeout(function() {
+                    listItem.slideDown(5, function() {
                       displayNextItem();
-                    }, 5);
+                    });
                   }});
         } else {
           // no source available, just display title
           Search.output.append(listItem);
-          setTimeout(function() {
+          listItem.slideDown(5, function() {
             displayNextItem();
-          }, 5);
+          });
         }
       }
       // search finished, update title and status message
@@ -379,13 +379,6 @@ var Search = {
     return results;
   },
 
-  /**
-   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
-   */
-  escapeRegExp : function(string) {
-    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
-  },
-
   /**
    * search for full-text terms in the index
    */
@@ -409,14 +402,13 @@ var Search = {
       ];
       // add support for partial matches
       if (word.length > 2) {
-        var word_regex = this.escapeRegExp(word);
         for (var w in terms) {
-          if (w.match(word_regex) && !terms[word]) {
+          if (w.match(word) && !terms[word]) {
             _o.push({files: terms[w], score: Scorer.partialTerm})
           }
         }
         for (var w in titleterms) {
-          if (w.match(word_regex) && !titleterms[word]) {
+          if (w.match(word) && !titleterms[word]) {
               _o.push({files: titleterms[w], score: Scorer.partialTitle})
           }
         }