@@ -304,7 +304,7 @@ For more information on how to do it go to <span class="xref myst">Getting Start
 <div class="section" id="start-training-with-just-1-command-line">
 <h2>Start Training with Just 1 Command Line<a class="headerlink" href="#start-training-with-just-1-command-line" title="Permalink to this heading"></a></h2>
 <p>The most simple and straightforward way to start training SOTA performance models with SuperGradients reproducible recipes. Just define your dataset path and where you want your checkpoints to be saved and you are good to go from your terminal!</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python -m super_gradients.examples.train_from_recipe_example.train_from_recipe --config-name<span class="o">=</span>imagenet_regnetY <span class="nv">architecture</span><span class="o">=</span>regnetY800 dataset_interface.data_dir<span class="o">=</span>&lt;YOUR_Imagenet_LOCAL_PATH&gt; <span class="nv">ckpt_root_dir</span><span class="o">=</span>&lt;CHEKPOINT_DIRECTORY&gt;
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python -m super_gradients.train_from_recipe --config-name<span class="o">=</span>imagenet_regnetY <span class="nv">architecture</span><span class="o">=</span>regnetY800 dataset_interface.data_dir<span class="o">=</span>&lt;YOUR_Imagenet_LOCAL_PATH&gt; <span class="nv">ckpt_root_dir</span><span class="o">=</span>&lt;CHEKPOINT_DIRECTORY&gt;
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