Updated docs

README.md

@@ -196,7 +196,7 @@ ludwig visualize --visualization learning_curves --training_statistics path/to/t
 
 The commands will display a graph that looks like the following, where you can see loss and accuracy as functions of train iteration number:
 
-![Learning Curves](docs/images/getting_started_learning_curves.png "Learning Curves")
+![Learning Curves](https://github.com/uber/ludwig/raw/master/docs/images/getting_started_learning_curves.png "Learning Curves")
 
 Several visualizations are available, please refer to [Visualizations](https://uber.github.io/ludwig/user_guide/#visualizations) for more details.
 
@@ -226,7 +226,7 @@ ludwig visualize --visualization compare_performance --test_statistics path/to/t
 
 will return a bar plot comparing the models on different measures:
 
-![Performance Comparison](docs/images/compare_performance.png "Performance Comparison")
+![Performance Comparison](https://github.com/uber/ludwig/raw/master/docs/images/compare_performance.png "Performance Comparison")
 
 A handy `ludwig experiment` command that performs training and prediction one after the other is also available.
 

docs/404.html

@@ -36,7 +36,7 @@
  <meta name="lang:search.tokenizer" content="[\s\-]+">
 
  <link rel="shortcut icon" href="/ludwig/favicon.ico">
- <meta name="generator" content="mkdocs-1.0.4, mkdocs-material-4.4.2">
+ <meta name="generator" content="mkdocs-1.0.4, mkdocs-material-4.4.3">
 
 
 
@@ -394,7 +394,7 @@ <h1>404 - Not found</h1>
 
  </div>
 
- <script src="/ludwig/assets/javascripts/application.c648116f.js"></script>
+ <script src="/ludwig/assets/javascripts/application.ac79c3b0.js"></script>
 
  <script>app.initialize({version:"1.0.4",url:{base:"/ludwig/"}})</script>
 

docs/api/LudwigModel/index.html

@@ -38,7 +38,7 @@
  <meta name="lang:search.tokenizer" content="[\s\-]+">
 
  <link rel="shortcut icon" href="../../favicon.ico">
- <meta name="generator" content="mkdocs-1.0.4, mkdocs-material-4.4.2">
+ <meta name="generator" content="mkdocs-1.0.4, mkdocs-material-4.4.3">
 
 
 
@@ -602,8 +602,8 @@ <h2 id="ludwigmodel-class">LudwigModel class<a class="headerlink" href="#ludwigm
 <p><strong>Inputs</strong></p>
 <ul>
 <li><strong>model_definition</strong> (dict): a dictionary containing information needed
- to build a model. Refer to the [User Guide]
- (http://ludwig.ai/user_guide/#model-definition) for details.</li>
+ to build a model. Refer to the <a href="http://ludwig.ai/user_guide/#model-definition">User Guide</a> 
+ for details.</li>
 <li><strong>model_definition_file</strong> (string, optional, default: <code>None</code>): path to
  a YAML file containing the model definition. If available it will be
  used instead of the model_definition dict.</li>
@@ -738,7 +738,8 @@ <h3 id="predict">predict<a class="headerlink" href="#predict" title="Permanent l
  <span class="n">return_type</span><span class="o">=&lt;</span><span class="k">class</span> <span class="err">&#39;</span><span class="nc">pandas</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">frame</span><span class="o">.</span><span class="n">DataFrame</span><span class="s1">&#39;&gt;,</span>
  <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
  <span class="n">gpus</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
- <span class="n">gpu_fraction</span><span class="o">=</span><span class="mi">1</span>
+ <span class="n">gpu_fraction</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+ <span class="n">skip_save_unprocessed_output</span><span class="o">=</span><span class="bp">True</span>
 <span class="p">)</span>
 </pre></div>
 
@@ -766,6 +767,11 @@ <h3 id="predict">predict<a class="headerlink" href="#predict" title="Permanent l
  DataFrame , while <code>'dict'</code>, ''dictionary'<code>and</code>dict` will
  return a dictionary.</li>
 <li><strong>batch_size</strong> (int, default: <code>128</code>): batch size</li>
+<li><strong>skip_save_unprocessed_output</strong> (skip_save_unprocessed_output: If this parameter is False):skip_save_unprocessed_output: If this parameter is False,
+ predictions and their probabilities are saved in both raw
+ unprocessed numpy files contaning tensors and as postprocessed
+ CSV files (one for each output feature). If this parameter is
+ True, only the CSV ones are saved and the numpy ones are skipped.</li>
 <li><strong>gpus</strong> (string, default: <code>None</code>): list of GPUs to use (it uses the
  same syntax of CUDA_VISIBLE_DEVICES)</li>
 <li><strong>gpu_fraction</strong> (float, default <code>1.0</code>): fraction of gpu memory to
@@ -847,6 +853,7 @@ <h3 id="test">test<a class="headerlink" href="#test" title="Permanent link">&par
  <span class="n">data_dict</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
  <span class="n">return_type</span><span class="o">=&lt;</span><span class="k">class</span> <span class="err">&#39;</span><span class="nc">pandas</span><span class="o">.</span><span class="n">core</span><span class="o">.</span><span class="n">frame</span><span class="o">.</span><span class="n">DataFrame</span><span class="s1">&#39;&gt;,</span>
  <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
+ <span class="n">skip_save_unprocessed_output</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
  <span class="n">gpus</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
  <span class="n">gpu_fraction</span><span class="o">=</span><span class="mi">1</span>
 <span class="p">)</span>
@@ -878,6 +885,11 @@ <h3 id="test">test<a class="headerlink" href="#test" title="Permanent link">&par
  DataFrame , while <code>'dict'</code>, ''dictionary'<code>and</code>dict` will
  return a dictionary.</li>
 <li><strong>batch_size</strong> (int, default: <code>128</code>): batch size</li>
+<li><strong>skip_save_unprocessed_output</strong> (skip_save_unprocessed_output: If this parameter is False):skip_save_unprocessed_output: If this parameter is False,
+ predictions and their probabilities are saved in both raw
+ unprocessed numpy files contaning tensors and as postprocessed
+ CSV files (one for each output feature). If this parameter is
+ True, only the CSV ones are saved and the numpy ones are skipped.</li>
 <li><strong>gpus</strong> (string, default: <code>None</code>): list of GPUs to use (it uses the
  same syntax of CUDA_VISIBLE_DEVICES)</li>
 <li><strong>gpu_fraction</strong> (float, default <code>1.0</code>): fraction of GPU memory to
@@ -930,6 +942,8 @@ <h3 id="train">train<a class="headerlink" href="#train" title="Permanent link">&
  <span class="n">model_name</span><span class="o">=</span><span class="s1">&#39;run&#39;</span><span class="p">,</span>
  <span class="n">model_load_path</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
  <span class="n">model_resume_path</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
+ <span class="n">skip_save_training_description</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
+ <span class="n">skip_save_training_statistics</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
  <span class="n">skip_save_model</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
  <span class="n">skip_save_progress</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
  <span class="n">skip_save_log</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
@@ -1024,6 +1038,10 @@ <h3 id="train">train<a class="headerlink" href="#train" title="Permanent link">&
  initialization</li>
 <li><strong>model_resume_path</strong> (string): path of a the model directory to
  resume training of</li>
+<li><strong>skip_save_training_description</strong> (bool, default: <code>False</code>): disables
+ saving the description JSON file.</li>
+<li><strong>skip_save_training_statistics</strong> (bool, default: <code>False</code>): disables
+ saving training statistics JSON file.</li>
 <li><strong>skip_save_model</strong> (bool, default: <code>False</code>): disables
  saving model weights and hyperparameters each time the model
  improves. By default Ludwig saves model weights after each epoch
@@ -1237,7 +1255,7 @@ <h3 id="train_online">train_online<a class="headerlink" href="#train_online" tit
 
  </div>
 
- <script src="../../assets/javascripts/application.c648116f.js"></script>
+ <script src="../../assets/javascripts/application.ac79c3b0.js"></script>
 
  <script>app.initialize({version:"1.0.4",url:{base:"../.."}})</script>
 

docs/api/index.html

@@ -38,7 +38,7 @@
  <meta name="lang:search.tokenizer" content="[\s\-]+">
 
  <link rel="shortcut icon" href="../favicon.ico">
- <meta name="generator" content="mkdocs-1.0.4, mkdocs-material-4.4.2">
+ <meta name="generator" content="mkdocs-1.0.4, mkdocs-material-4.4.3">
 
 
 
@@ -446,7 +446,7 @@ <h3 id="ludwigvisualization"><a href="visualization/">ludwig.visualization</a><a
 
  </div>
 
- <script src="../assets/javascripts/application.c648116f.js"></script>
+ <script src="../assets/javascripts/application.ac79c3b0.js"></script>
 
  <script>app.initialize({version:"1.0.4",url:{base:".."}})</script>