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<h1>Home</h1>
<h6 id="new-project-quick-start">New project Quick Start</h6>
<p>To start a new project define two files, a LightningModule and a Trainer file. <br />
To illustrate Lightning power and simplicity, here's an example of a typical research flow. </p>
<h6 id="case-1-bert">Case 1: BERT</h6>
<p>Let's say you're working on something like BERT but want to try different ways of training or even different networks.<br />
You would define a single LightningModule and use flags to switch between your different ideas. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
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17</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">class</span> <span class="nc">BERT</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">LightningModule</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_name</span><span class="p">,</span> <span class="n">task</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">task</span> <span class="o">=</span> <span class="n">task</span>
<span class="k">if</span> <span class="n">model_name</span> <span class="o">==</span> <span class="s1">&#39;transformer&#39;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">Transformer</span><span class="p">()</span>
<span class="k">elif</span> <span class="n">model_name</span> <span class="o">==</span> <span class="s1">&#39;my_cool_version&#39;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">MyCoolVersion</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span> <span class="o">==</span> <span class="s1">&#39;standard_bert&#39;</span><span class="p">:</span>
<span class="c1"># do standard bert training with self.net...</span>
<span class="c1"># return loss</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">task</span> <span class="o">==</span> <span class="s1">&#39;my_cool_task&#39;</span><span class="p">:</span>
<span class="c1"># do my own version with self.net</span>
<span class="c1"># return loss</span>
</pre></div>
</td></tr></table>
<h6 id="case-2-cooler-not-bert">Case 2: COOLER NOT BERT</h6>
<p>But if you wanted to try something <strong>completely</strong> different, you'd define a new module for that. </p>
<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="k">class</span> <span class="nc">CoolerNotBERT</span><span class="p">(</span><span class="n">pl</span><span class="o">.</span><span class="n">LightningModule</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="o">...</span>
<span class="k">def</span> <span class="nf">training_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">batch_nb</span><span class="p">):</span>
<span class="c1"># do some other cool task</span>
<span class="c1"># return loss </span>
</pre></div>
</td></tr></table>
<h6 id="rapid-research-flow">Rapid research flow</h6>
<p>Then you could do rapid research by switching between these two and using the same trainer. </p>
<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
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<span class="n">model</span> <span class="o">=</span> <span class="n">BERT</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">CoolerNotBERT</span><span class="p">()</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">gpus</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">use_amp</span><span class="o">=</span><span class="bp">True</span><span class="p">)</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>Notice a few things about this flow: <br />
1. You're writing pure PyTorch... no unnecessary abstractions or new libraries to learn. <br />
2. You get free GPU and 16-bit support without writing any of that code in your model. <br />
3. You also get all of the capabilities below (without coding or testing yourself). </p>
<hr />
<h6 id="templates">Templates</h6>
<ol>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#minimal-example">MNIST LightningModule</a> </li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/">Trainer</a><ul>
<li><a href="https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples/basic_examples">Basic CPU, GPU Trainer Template</a></li>
<li><a href="https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples/multi_node_examples">GPU cluster Trainer Template</a></li>
</ul>
</li>
</ol>
<h6 id="docs-shortcuts">Docs shortcuts</h6>
<ul>
<li><a href="LightningModule/RequiredTrainerInterface/">LightningModule</a> </li>
<li><a href="Trainer/">Trainer</a> </li>
</ul>
<h6 id="quick-start-examples">Quick start examples</h6>
<ul>
<li><a href="examples/Examples/#cpu-hyperparameter-search">CPU example</a> </li>
<li><a href="examples/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus">Hyperparameter search on single GPU</a> </li>
<li><a href="examples/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus">Hyperparameter search on multiple GPUs on same node</a> </li>
<li><a href="examples/Examples/#Hyperparameter search on a SLURM HPC cluster">Hyperparameter search on a SLURM HPC cluster</a> </li>
</ul>
<h6 id="checkpointing">Checkpointing</h6>
<ul>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving">Checkpoint callback</a> </li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving">Model saving</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics">Model loading</a> </li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session">Restoring training session</a></li>
</ul>
<h6 id="computing-cluster-slurm">Computing cluster (SLURM)</h6>
<ul>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster">Running grid search on a cluster</a> </li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit">Walltime auto-resubmit</a> </li>
</ul>
<h6 id="debugging">Debugging</h6>
<ul>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run">Fast dev run</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms">Inspect gradient norms</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage">Log GPU usage</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data">Make model overfit on subset of data</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer">Print the parameter count by layer</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan">Pring which gradients are nan</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array">Print input and output size of every module in system</a></li>
</ul>
<h6 id="distributed-training">Distributed training</h6>
<ul>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#init_ddp_connection">Implement Your Own Distributed (DDP) training</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision">16-bit mixed precision</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU">Multi-GPU</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-node">Multi-node</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#single-gpu">Single GPU</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture">Self-balancing architecture</a></li>
</ul>
<h6 id="experiment-logging">Experiment Logging</h6>
<ul>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar">Display metrics in progress bar</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches">Log metric row every k batches</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position">Process position</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support">Tensorboard support</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters">Save a snapshot of all hyperparameters</a> </li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run">Snapshot code for a training run</a> </li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches">Write logs file to csv every k batches</a></li>
</ul>
<h6 id="training-loop">Training loop</h6>
<ul>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients">Accumulate gradients</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs">Force training for min or max epochs</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#early-stopping">Early stopping callback</a> </li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop">Force disable early stop</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping">Gradient Clipping</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/">Hooks</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers">Learning rate scheduling</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers">Use multiple optimizers (like GANs)</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check">Set how much of the training set to check (1-100%)</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step">Step optimizers at arbitrary intervals</a></li>
</ul>
<h6 id="validation-loop">Validation loop</h6>
<ul>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs">Check validation every n epochs</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/">Hooks</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check">Set how much of the validation set to check</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check">Set how much of the test set to check</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch">Set validation check frequency within 1 training epoch</a></li>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-the-number-of-validation-sanity-steps">Set the number of validation sanity steps</a></li>
</ul>
<h6 id="testing-loop">Testing loop</h6>
<ul>
<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Testing%20loop/">Run test set</a> </li>
</ul>
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