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<h1>Examples</h1>
<h3 id="template-model-definition">Template model definition</h3>
<p>In 99% of cases you want to just copy <a href="https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples">one of the examples</a> to start a new lightningModule and change the core of what your model is actually trying to do.</p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># get a copy of the module template</span>
wget https://raw.githubusercontent.com/williamFalcon/pytorch-lightning/master/pl_examples/new_project_templates/lightning_module_template.py
</pre></div>
</td></tr></table>
<hr />
<h3 id="trainer-example">Trainer Example</h3>
<p><strong> __main__ function</strong> </p>
<p>Normally, we want to let the __main__ function start the training.
Inside the main we parse training arguments with whatever hyperparameters we want. Your LightningModule will have a
chance to add hyperparameters. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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15</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">test_tube</span> <span class="kn">import</span> <span class="n">HyperOptArgumentParser</span>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="c1"># use default args given by lightning</span>
<span class="n">root_dir</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</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">dirname</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">modules</span><span class="p">[</span><span class="s1">&#39;__main__&#39;</span><span class="p">]</span><span class="o">.</span><span class="vm">__file__</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">parent_parser</span> <span class="o">=</span> <span class="n">HyperOptArgumentParser</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;random_search&#39;</span><span class="p">,</span> <span class="n">add_help</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
<span class="n">add_default_args</span><span class="p">(</span><span class="n">parent_parser</span><span class="p">,</span> <span class="n">root_dir</span><span class="p">)</span>
<span class="c1"># allow model to overwrite or extend args</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">ExampleModel</span><span class="o">.</span><span class="n">add_model_specific_args</span><span class="p">(</span><span class="n">parent_parser</span><span class="p">)</span>
<span class="n">hyperparams</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
<span class="c1"># train model</span>
<span class="n">main</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<p><strong>Main Function</strong> </p>
<p>The main function is your entry into the program. This is where you init your model, checkpoint directory, and launch the training.
The main function should have 3 arguments: <br />
- hparams: a configuration of hyperparameters. <br />
- slurm_manager: Slurm cluster manager object (can be None)
- dict: for you to return any values you want (useful in meta-learning, otherwise set to _) </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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14</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">main</span><span class="p">(</span><span class="n">hparams</span><span class="p">,</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">results_dict</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Main training routine specific for this project</span>
<span class="sd"> :param hparams:</span>
<span class="sd"> :return:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># build model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="p">(</span><span class="n">hparams</span><span class="p">)</span>
<span class="c1"># configure trainer</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">()</span>
<span class="c1"># train model</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<p>The <strong>main</strong> function will start training on your <strong>main</strong> function. If you use the HyperParameterOptimizer
in hyper parameter optimization mode, this main function will get one set of hyperparameters. If you use it as a simple
argument parser you get the default arguments in the argument parser.</p>
<p>So, calling main(hyperparams) runs the model with the default argparse arguments. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">main</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="cpu-hyperparameter-search">CPU hyperparameter search</h4>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
2
3
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5
6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run a grid search over 20 hyperparameter combinations.</span>
<span class="n">hyperparams</span><span class="o">.</span><span class="n">optimize_parallel_cpu</span><span class="p">(</span>
<span class="n">main_local</span><span class="p">,</span>
<span class="n">nb_trials</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
<span class="n">nb_workers</span><span class="o">=</span><span class="mi">1</span>
<span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="hyperparameter-search-on-a-single-or-multiple-gpus">Hyperparameter search on a single or multiple GPUs</h4>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
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7</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run a grid search over 20 hyperparameter combinations.</span>
<span class="n">hyperparams</span><span class="o">.</span><span class="n">optimize_parallel_gpu</span><span class="p">(</span>
<span class="n">main_local</span><span class="p">,</span>
<span class="n">nb_trials</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
<span class="n">nb_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">gpus</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
<span class="p">)</span>
</pre></div>
</td></tr></table>
<hr />
<h4 id="hyperparameter-search-on-a-slurm-hpc-cluster">Hyperparameter search on a SLURM HPC cluster</h4>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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34</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">def</span> <span class="nf">optimize_on_cluster</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">):</span>
<span class="c1"># enable cluster training</span>
<span class="n">cluster</span> <span class="o">=</span> <span class="n">SlurmCluster</span><span class="p">(</span>
<span class="n">hyperparam_optimizer</span><span class="o">=</span><span class="n">hyperparams</span><span class="p">,</span>
<span class="n">log_path</span><span class="o">=</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">tt_save_path</span><span class="p">,</span>
<span class="n">test_tube_exp_name</span><span class="o">=</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">tt_name</span>
<span class="p">)</span>
<span class="c1"># email for cluster coms</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">notify_job_status</span><span class="p">(</span><span class="n">email</span><span class="o">=</span><span class="s1">&#39;add_email_here&#39;</span><span class="p">,</span> <span class="n">on_done</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">on_fail</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="c1"># configure cluster</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">per_experiment_nb_gpus</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">per_experiment_nb_gpus</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">job_time</span> <span class="o">=</span> <span class="s1">&#39;48:00:00&#39;</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">gpu_type</span> <span class="o">=</span> <span class="s1">&#39;1080ti&#39;</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">memory_mb_per_node</span> <span class="o">=</span> <span class="mi">48000</span>
<span class="c1"># any modules for code to run in env</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">add_command</span><span class="p">(</span><span class="s1">&#39;source activate pytorch_lightning&#39;</span><span class="p">)</span>
<span class="c1"># name of exp</span>
<span class="n">job_display_name</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">tt_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">job_display_name</span> <span class="o">=</span> <span class="n">job_display_name</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span>
<span class="c1"># run hopt</span>
<span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;submitting jobs...&#39;</span><span class="p">)</span>
<span class="n">cluster</span><span class="o">.</span><span class="n">optimize_parallel_cluster_gpu</span><span class="p">(</span>
<span class="n">main</span><span class="p">,</span>
<span class="n">nb_trials</span><span class="o">=</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">nb_hopt_trials</span><span class="p">,</span>
<span class="n">job_name</span><span class="o">=</span><span class="n">job_display_name</span>
<span class="p">)</span>
<span class="c1"># run cluster hyperparameter search </span>
<span class="n">optimize_on_cluster</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">)</span>
</pre></div>
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