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Trainer/Logging/index.html

@@ -403,11 +403,24 @@
 </li>
       
         <li class="md-nav__item">
+  <a href="#tensorboard-support" title="Tensorboard support" class="md-nav__link">
+    Tensorboard support
+  </a>
+  
+    <nav class="md-nav">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
   <a href="#write-logs-file-to-csv-every-k-batches" title="Write logs file to csv every k batches" class="md-nav__link">
     Write logs file to csv every k batches
   </a>
   
 </li>
+        
+      </ul>
+    </nav>
+  
+</li>
       
       
       
@@ -575,11 +588,24 @@
 </li>
       
         <li class="md-nav__item">
+  <a href="#tensorboard-support" title="Tensorboard support" class="md-nav__link">
+    Tensorboard support
+  </a>
+  
+    <nav class="md-nav">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
   <a href="#write-logs-file-to-csv-every-k-batches" title="Write logs file to csv every k batches" class="md-nav__link">
     Write logs file to csv every k batches
   </a>
   
 </li>
+        
+      </ul>
+    </nav>
+  
+</li>
       
       
       
@@ -647,6 +673,33 @@ exp = Experiment(create_git_tag=True)
 Trainer(experiment=exp)
 </code></pre>
 
+<hr />
+<h3 id="tensorboard-support">Tensorboard support</h3>
+<p>The experiment object is a strict subclass of Pytorch SummaryWriter. However, this class
+also snapshots every detail about the experiment (data folder paths, code, hyperparams),
+and allows you to visualize it using tensorboard.</p>
+<pre><code class="python">from test_tube import Experiment, HyperOptArgumentParser
+
+# exp hyperparams
+args = HyperOptArgumentParser()
+hparams = args.parse_args()
+
+# this is a summaryWriter with nicer logging structure
+exp = Experiment(save_dir='/some/path', create_git_tag=True)
+
+# track experiment details (must be ArgumentParser or HyperOptArgumentParser).
+# each option in the parser is tracked
+exp.argparse(hparams)
+exp.tag({'description': 'running demo'})
+
+# trainer uses the exp object to log exp data
+trainer = Trainer(experiment=exp)
+trainer.fit(model)
+
+# view logs at:
+# tensorboard --logdir /some/path   
+</code></pre>
+
 <hr />
 <h4 id="write-logs-file-to-csv-every-k-batches">Write logs file to csv every k batches</h4>
 <p>Every k batches, lightning will write the new logs to disk</p>