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2026-09-11 12:31:23 +08:00 · 2019-11-06 15:08:12 -05:00 · 2019-10-18 23:57:14 +01:00 · 2019-10-05 16:34:18 -05:00 · 2019-10-05 16:32:22 -05:00 · 2019-10-05 16:18:33 -05:00
94 changed files with 15161 additions and 2916 deletions
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/LightningModule/methods.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Methods</h1>
+                
+                <p>Lightning modules are strict superclasses of torch.nn.Module. A LightningModule offers the following in addition to that API.</p>
+<hr />
+<h3 id="freeze">freeze</h3>
+<p>Freeze all params for inference</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="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h3 id="load_from_metrics">load_from_metrics</h3>
+<p>This is the easiest/fastest way which loads hyperparameters and weights from a checkpoint,
+such as the one saved by the <code>ModelCheckpoint</code> callback</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6
+7
+8</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">pretrained_model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_checkpoint</span><span class="p">(</span>
+    <span class="n">checkpoint_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># predict</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
+<span class="n">y_hat</span> <span class="o">=</span> <span class="n">pretrained_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h3 id="load_from_metrics_1">load_from_metrics</h3>
+<p>If you're using test tube, there is an alternate method which uses the meta_tags.csv
+file from test-tube to rebuild the model. The meta_tags.csv file can be found in the
+test-tube experiment save_dir.       </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">pretrained_model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_metrics</span><span class="p">(</span>
+    <span class="n">weights_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span><span class="p">,</span>
+    <span class="n">tags_csv</span><span class="o">=</span><span class="s1">&#39;/path/to/test_tube/experiment/version/meta_tags.csv&#39;</span><span class="p">,</span>
+    <span class="n">on_gpu</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">map_location</span><span class="o">=</span><span class="bp">None</span>
+<span class="p">)</span>
+
+<span class="c1"># predict</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="n">pretrained_model</span><span class="o">.</span><span class="n">freeze</span><span class="p">()</span>
+<span class="n">y_hat</span> <span class="o">=</span> <span class="n">pretrained_model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p><strong>Params</strong>    </p>
+<table>
+<thead>
+<tr>
+<th>Param</th>
+<th>description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>weights_path</td>
+<td>Path to a PyTorch checkpoint</td>
+</tr>
+<tr>
+<td>tags_csv</td>
+<td>Path to meta_tags.csv file generated by the test-tube Experiment</td>
+</tr>
+<tr>
+<td>on_gpu</td>
+<td>if True, puts model on GPU. Make sure to use transforms option if model devices have changed</td>
+</tr>
+<tr>
+<td>map_location</td>
+<td>A dictionary mapping saved weight GPU devices to new GPU devices</td>
+</tr>
+</tbody>
+</table>
+<p><strong>Returns</strong>    </p>
+<p>LightningModule - The pretrained LightningModule</p>
+<hr />
+<h3 id="unfreeze">unfreeze</h3>
+<p>Unfreeze all params for inference</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="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">unfreeze</span><span class="p">()</span>
+</pre></div>
+</td></tr></table>
+                
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/LightningModule/properties.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Properties</h1>
+                
+                <p>A LightningModule has the following properties which you can access at any time</p>
+<hr />
+<h4 id="current_epoch">current_epoch</h4>
+<p>The current epoch   </p>
+<hr />
+<h4 id="dtype">dtype</h4>
+<p>Current dtype    </p>
+<hr />
+<h4 id="logger">logger</h4>
+<p>A reference to the logger you passed into trainer. 
+Passing a logger is optional. If you don't pass one in, Lightning will create one for you automatically.
+This logger saves logs to '''/os.getcwd()/lightning_logs'''</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">Trainer</span><span class="p">(</span><span class="n">logger</span><span class="o">=</span><span class="n">your_logger</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>Call it from anywhere in your LightningModule to add metrics, images, etc... whatever your logger supports.  </p>
+<p>Here is an example using the TestTubeLogger (which is a wrapper on <a href="https://pytorch.org/docs/stable/tensorboard.html">PyTorch SummaryWriter</a> with versioned folder structure).     </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># if logger is a tensorboard logger or TestTubeLogger</span>
+<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>   
+<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">log</span><span class="p">({</span><span class="s1">&#39;val_loss&#39;</span><span class="p">:</span> <span class="mf">0.9</span><span class="p">})</span>   
+<span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="o">.</span><span class="n">experiment</span><span class="o">.</span><span class="n">add_scalars</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>   
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="global_step">global_step</h4>
+<p>Total training batches seen across all epochs   </p>
+<hr />
+<h4 id="gradient_clip_val">gradient_clip_val</h4>
+<p>The current gradient clip value    </p>
+<hr />
+<h4 id="on_gpu">on_gpu</h4>
+<p>True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior.    </p>
+<hr />
+<h4 id="trainer">trainer</h4>
+<p>Last resort access to any state the trainer has. Changing certain properties here could affect your training run.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">optimizers</span>
+<span class="bp">self</span><span class="o">.</span><span class="n">trainer</span><span class="o">.</span><span class="n">current_epoch</span>
+<span class="o">...</span>   
+</pre></div>
+</td></tr></table>
+
+<h2 id="debugging">Debugging</h2>
+<p>The LightningModule also offers these tricks to help debug.   </p>
+<hr />
+<h4 id="example_input_array">example_input_array</h4>
+<p>In the LightningModule init, you can set a dummy tensor for this property
+to get a print out of sizes coming into and out of every layer.   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3</pre></div></td><td class="code"><div class="codehilite"><pre><span></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="c1"># put the dimensions of the first input to your system</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">example_input_array</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">28</span> <span class="o">*</span> <span class="mi">28</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+                
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@@ -1,9 +0,0 @@
-graft docs
-
-include COPYING
-include AUTHORS
-
-recursive-include src/einsteinpy/tests *.py *.html
-
-prune docs/source/examples/.ipynb_checkpoints
-global-exclude *.py[cod] __pycache__ *.so *.dylib
@@ -1,240 +0,0 @@
-<p align="center">
-  <a href="https://williamfalcon.github.io/pytorch-lightning/">
-    <img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/lightning_logo.png" width="50">
-  </a>
-</p>
-<h3 align="center">
-  Pytorch Lightning
-</h3>
-<p align="center">
-  The Keras for ML researchers using PyTorch. More control. Less boilerplate.    
-</p>
-<p align="center">
-  <a href="https://badge.fury.io/py/pytorch-lightning"><img src="https://badge.fury.io/py/pytorch-lightning.svg" alt="PyPI version" height="18"></a>
-<!--   <a href="https://travis-ci.org/williamFalcon/test-tube"><img src="https://travis-ci.org/williamFalcon/pytorch-lightning.svg?branch=master"></a> -->
-  <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/COPYING"><img src="https://img.shields.io/badge/License-MIT-yellow.svg"></a>
-</p>   
-
-```bash
-pip install pytorch-lightning    
-```
-
-## Docs   
-In progress. Documenting now!  
-
-## Disclaimer
-This is a research tool I built for myself internally while doing my PhD. The API is not 100% production quality, but my hope is that by open-sourcing, we can all get it there (I don't have too much time nowadays to write production-level code).
-
-## What is it?  
-Keras is too abstract for researchers. Lightning makes it so you only have to define your model but still control all details of training if you need to.   
-
-Pytorch   
-<-- Lightning   
-Your model.   
-
-**Lightning will do the following for you:**   
-
-1. Run the training loop.   
-2. Run the validation loop.   
-3. Run the testing loop.   
-4. Early stopping.   
-5. Learning rate annealing.
-6. Can train complex models like GANs or anything with multiple optimizers.
-7. Weight checkpointing.   
-8. Model saving.   
-9. Model loading.   
-10. Log training details (through test-tube).  
-11. Run training on multiple GPUs (through test-tube).   
-12. Run training on a GPU cluster managed by SLURM (through test-tube).   
-13. Distribute memory-bound models on multiple GPUs.
-14. Give your model hyperparameters parsed from the command line OR a JSON file.   
-15. Run your model in a dev environment where nothing logs.      
-
-## Usage
-To use lightning do 2 things:  
-1. [Define a trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/examples/basic_trainer.py) (which will run ALL your models).   
-2. [Define a model](https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/examples/example_model.py).     
-
-#### Basic trainer example  
-See [this demo](https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/examples/fully_featured_trainer.py) for a more robust trainer example.   
-
-```python
-import os
-import sys
-
-from test_tube import HyperOptArgumentParser, Experiment
-from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.utils.arg_parse import add_default_args
-from pytorch_lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
-from demo.example_model import ExampleModel
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # init experiment
-    exp = Experiment(
-        name=hparams.tt_name,
-        debug=hparams.debug,
-        save_dir=hparams.tt_save_path,
-        version=hparams.hpc_exp_number,
-        autosave=False,
-        description=hparams.tt_description
-    )
-
-    exp.argparse(hparams)
-    exp.save()
-
-    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
-
-    # build model
-    model = ExampleModel(hparams)
-
-    # callbacks
-    early_stop = EarlyStopping(monitor='val_acc', patience=3, mode='min', verbose=True)
-    checkpoint = ModelCheckpoint(filepath=model_save_path, save_function=None, save_best_only=True, verbose=True, monitor='val_acc', mode='min')
-
-    # configure trainer
-    trainer = Trainer(experiment=exp, checkpoint_callback=checkpoint, early_stop_callback=early_stop)
-
-    # train model
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-
-    # use default args given by lightning
-    root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
-    parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
-    add_default_args(parent_parser, root_dir)
-
-    # allow model to overwrite or extend args
-    parser = ExampleModel.add_model_specific_args(parent_parser)
-    hyperparams = parser.parse_args()
-
-    # train model
-    main(hyperparams)
-
-```
-
-#### Basic model example  
-Here we only show the method signatures. It's up to you to define the content.   
-
-```python
-from torch import nn
-
-class My_Model(RootModule):
-    def __init__(self):
-        # define model
-        self.l1 = nn.Linear(200, 10)
-
-    # ---------------
-    # TRAINING
-    def training_step(self, data_batch):
-        x, y = data_batch
-        y_hat = self.l1(x)
-        loss = some_loss(y_hat)
-
-        return loss_val, {'train_loss': loss}
-
-    def validation_step(self, data_batch):
-        x, y = data_batch
-        y_hat = self.l1(x)
-        loss = some_loss(y_hat)
-
-        return loss_val, {'val_loss': loss}
-
-     def validation_end(self, outputs):
-        total_accs = []
-
-        for output in outputs:
-            total_accs.append(output['val_acc'].item())
-
-        # return a dict
-        return {'total_acc': np.mean(total_accs)}
-
-     # ---------------
-     # SAVING
-     def get_save_dict(self):
-        # lightning saves for you. Here's your chance to say what you want to save
-        checkpoint = {'state_dict': self.state_dict()}
-
-        return checkpoint
-
-    def load_model_specific(self, checkpoint):
-        # lightning loads for you. Here's your chance to say what you want to load
-        self.load_state_dict(checkpoint['state_dict'])
-
-    # ---------------
-    # TRAINING CONFIG
-    def configure_optimizers(self):
-        # give lightning the list of optimizers you want to use.
-        # lightning will call automatically
-        optimizer = self.choose_optimizer('adam', self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
-        return [optimizer]
-
-    @property
-    def tng_dataloader(self):
-        return pytorch_dataloader('train')
-
-    @property
-    def val_dataloader(self):
-        return pytorch_dataloader('val')
-
-    @property
-    def test_dataloader(self):
-        return pytorch_dataloader('test')
-
-    # ---------------
-    # MODIFY YOUR COMMAND LINE ARGS
-    @staticmethod
-    def add_model_specific_args(parent_parser):    
-        parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
-        parser.add_argument('--out_features', default=20)
-        return parser
-```
-
-
-### Details    
-
-#### Model definition   
-| Name  | Description  |  Input |  Return |
-|---|---|---|---|
-| training_step  |  Called with a batch of data during training | data from your dataloaders  | tuple: scalar, dict  |
-| validation_step  |  Called with a batch of data during validation  | data from your dataloaders  | tuple: scalar, dict |
-| validation_end  |  Collate metrics from all validation steps |  outputs: array where each item is the output of a validation step | dict: for logging |
-| get_save_dict  | called when your model needs to be saved (checkpoints, hpc save, etc...)  | None  |  dict to be saved |
-
-#### Model training   
-| Name  | Description  |  Input |  Return |
-|---|---|---|---|
-| configure_optimizers  |  called during training setup | None | list: optimizers you want to use |
-| tng_dataloader  |  called during training  | None  | pytorch dataloader |
-| val_dataloader  |  called during validation  | None  | pytorch dataloader |
-| test_dataloader  |  called during testing  | None  | pytorch dataloader |    
-| add_model_specific_args  |  called with args you defined in your main. This lets you tailor args for each model and keep main the same  | argparse  | argparse |
-
-#### Model Saving/Loading   
-| Name  | Description  |  Input |  Return |
-|---|---|---|---|
-| get_save_dict  | called when your model needs to be saved (checkpoints, hpc save, etc...)  | None  |  dict to be saved |
-|  load_model_specific |  called when loading a model | checkpoint: dict you created in get_save_dict  | dict: modified in whatever way you want  |
-
-## Optional model hooks.   
-Add these to the model whenever you want to configure training behavior.    
-
-
-### Model lifecycle hooks
-Use these hooks to customize functionality
-
-| Method | Purpose  | Input  | Output  | Required  |
-|---|---|---|---|---|
-| on_batch_start()  | called right before the batch starts | - | -  | N  |
-| on_batch_end()  | called right after the batch ends | - | -  | N  |
-| on_epoch_start()  | called right before the epoch starts | - | -  | N  |
-| on_epoch_end()  | called right afger the epoch ends | - | -  | N  |
-| on_pre_performance_check()  | called right before the performance check starts | - | -  | N  |
-| on_post_performance_check()  | called right after the batch starts | - | -  | N  |
@@ -0,0 +1,756 @@
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+      
+        <li class="md-nav__item">
+  <a href="#model-saving" title="Model saving" class="md-nav__link">
+    Model saving
+  </a>
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+      
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+  <a href="#model-saving" title="Model saving" class="md-nav__link">
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/Checkpointing.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Checkpointing</h1>
+                
+                <p>Lightning can automate saving and loading checkpoints.</p>
+<hr />
+<h3 id="model-saving">Model saving</h3>
+<p>Checkpointing is enabled by default to the current working directory.
+To change the checkpoint path pass in :</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">Trainer</span><span class="p">(</span><span class="n">default_save_path</span><span class="o">=</span><span class="s1">&#39;/your/path/to/save/checkpoints&#39;</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>To modify the behavior of checkpointing pass in your own callback.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning.callbacks</span> <span class="kn">import</span> <span class="n">ModelCheckpoint</span>
+
+<span class="c1"># DEFAULTS used by the Trainer</span>
+<span class="n">checkpoint_callback</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span>
+    <span class="n">filepath</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">(),</span>
+    <span class="n">save_best_only</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_loss&#39;</span><span class="p">,</span>
+    <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span><span class="p">,</span>
+    <span class="n">prefix</span><span class="o">=</span><span class="s1">&#39;&#39;</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">checkpoint_callback</span><span class="o">=</span><span class="n">checkpoint_callback</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h3 id="restoring-training-session">Restoring training session</h3>
+<p>You might want to not only load a model but also continue training it. Use this method to
+restore the trainer state as well. This will continue from the epoch and global step you last left off.<br />
+However, the dataloaders will start from the first batch again (if you shuffled it shouldn't matter).   </p>
+<p>Lightning will restore the session if you pass a logger with the same version and there's a saved checkpoint.   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning</span> <span class="kn">import</span> <span class="n">Trainer</span>
+<span class="kn">from</span> <span class="nn">pytorch_lightning.logging</span> <span class="kn">import</span> <span class="n">TestTubeLogger</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">TestTubeLogger</span><span class="p">(</span>
+    <span class="n">save_dir</span><span class="o">=</span><span class="s1">&#39;./savepath&#39;</span><span class="p">,</span>
+    <span class="n">version</span><span class="o">=</span><span class="mi">1</span>  <span class="c1"># An existing version with a saved checkpoint</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">logger</span><span class="o">=</span><span class="n">logger</span><span class="p">,</span>
+    <span class="n">default_save_path</span><span class="o">=</span><span class="s1">&#39;./savepath&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># this fit call loads model weights and trainer state</span>
+<span class="c1"># the trainer continues seamlessly from where you left off</span>
+<span class="c1"># without having to do anything else.</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 trainer restores:  </p>
+<ul>
+<li>global_step    </li>
+<li>current_epoch    </li>
+<li>All optimizers    </li>
+<li>All lr_schedulers    </li>
+<li>Model weights</li>
+</ul>
+<p>You can even change the logic of your model as long as the weights and "architecture" of 
+the system isn't different. If you add a layer, for instance, it might not work.   </p>
+<p>At a rough level, here's <a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/model_saving.py#L63">what happens inside Trainer</a>:   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="bp">self</span><span class="o">.</span><span class="n">global_step</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;global_step&#39;</span><span class="p">]</span>
+<span class="bp">self</span><span class="o">.</span><span class="n">current_epoch</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;epoch&#39;</span><span class="p">]</span>
+
+<span class="c1"># restore the optimizers</span>
+<span class="n">optimizer_states</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;optimizer_states&#39;</span><span class="p">]</span>
+<span class="k">for</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">opt_state</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizers</span><span class="p">,</span> <span class="n">optimizer_states</span><span class="p">):</span>
+    <span class="n">optimizer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">opt_state</span><span class="p">)</span>
+
+<span class="c1"># restore the lr schedulers</span>
+<span class="n">lr_schedulers</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;lr_schedulers&#39;</span><span class="p">]</span>
+<span class="k">for</span> <span class="n">scheduler</span><span class="p">,</span> <span class="n">lrs_state</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lr_schedulers</span><span class="p">,</span> <span class="n">lr_schedulers</span><span class="p">):</span>
+    <span class="n">scheduler</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">lrs_state</span><span class="p">)</span>
+
+<span class="c1"># uses the model you passed into trainer        </span>
+<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s1">&#39;state_dict&#39;</span><span class="p">])</span>
+</pre></div>
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/SLURM Managed Cluster.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>SLURM Managed Cluster</h1>
+                
+                <p>Lightning supports model training on a cluster managed by SLURM in the following cases:    </p>
+<ol>
+<li>Training on a single cpu or single GPU.</li>
+<li>Train on multiple GPUs on the same node using DataParallel or DistributedDataParallel</li>
+<li>Training across multiple GPUs on multiple different nodes via DistributedDataParallel.</li>
+</ol>
+<p><strong>Note: A node means a machine with multiple GPUs</strong></p>
+<hr />
+<h4 id="running-grid-search-on-a-cluster">Running grid search on a cluster</h4>
+<p>To use lightning to run a hyperparameter search (grid-search or random-search) on a cluster do 4 things:   </p>
+<p>(1). Define the parameters for the grid search    </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10</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="c1"># subclass of argparse</span>
+<span class="n">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">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--learning_rate&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mf">0.002</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">help</span><span class="o">=</span><span class="s1">&#39;the learning rate&#39;</span><span class="p">)</span>
+
+<span class="c1"># let&#39;s enable optimizing over the number of layers in the network</span>
+<span class="n">parser</span><span class="o">.</span><span class="n">opt_list</span><span class="p">(</span><span class="s1">&#39;--nb_layers&#39;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">tunable</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
+
+<span class="n">hparams</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>    
+</pre></div>
+</td></tr></table>
+
+<p><strong>NOTE</strong> You must set <code>Tunable=True</code> for that argument to be considered in the permutation set. Otherwise
+test-tube will use the default value. This flag is useful when you don't want to search over an argument and
+want to use the default instead.   </p>
+<p>(2). Define the cluster options in the <a href="https://williamfalcon.github.io/test-tube/hpc/SlurmCluster/">SlurmCluster object</a> (over 5 nodes and 8 gpus)    </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">test_tube.hpc</span> <span class="kn">import</span> <span class="n">SlurmCluster</span>
+
+<span class="c1"># hyperparameters is a test-tube hyper params object</span>
+<span class="c1"># see https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser/</span>
+<span class="n">hyperparams</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">parse</span><span class="p">()</span>
+
+<span class="c1"># init cluster</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="s1">&#39;/path/to/log/results/to&#39;</span><span class="p">,</span>
+    <span class="n">python_cmd</span><span class="o">=</span><span class="s1">&#39;python3&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># let the cluster know where to email for a change in job status (ie: complete, fail, etc...)</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;some@email.com&#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"># set the job options. In this instance, we&#39;ll run 20 different models</span>
+<span class="c1"># each with its own set of hyperparameters giving each one 1 GPU (ie: taking up 20 GPUs)</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">per_experiment_nb_gpus</span> <span class="o">=</span> <span class="mi">8</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">per_experiment_nb_nodes</span> <span class="o">=</span> <span class="mi">5</span>
+
+<span class="c1"># we&#39;ll request 10GB of memory per node</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">10000</span>
+
+<span class="c1"># set a walltime of 10 minues</span>
+<span class="n">cluster</span><span class="o">.</span><span class="n">job_time</span> <span class="o">=</span> <span class="s1">&#39;10:00&#39;</span>
+</pre></div>
+</td></tr></table>
+
+<p>(3). Make a main function with your model and trainer. Each job will call this function with a particular
+hparams configuration.    </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning</span> <span class="kn">import</span> <span class="n">Trainer</span>
+
+<span class="k">def</span> <span class="nf">train_fx</span><span class="p">(</span><span class="n">trial_hparams</span><span class="p">,</span> <span class="n">cluster_manager</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span>
+    <span class="c1"># hparams has a specific set of hyperparams</span>
+
+    <span class="n">my_model</span> <span class="o">=</span> <span class="n">MyLightningModel</span><span class="p">()</span>
+
+    <span class="c1"># give the trainer the cluster object</span>
+    <span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</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">my_model</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>(3). Start the grid/random search     </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run the models on the cluster</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">train_fx</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">job_name</span><span class="o">=</span><span class="s1">&#39;my_grid_search_exp_name&#39;</span><span class="p">,</span> 
+    <span class="n">job_display_name</span><span class="o">=</span><span class="s1">&#39;my_exp&#39;</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p><strong>NOTE</strong> nb_trials specifies how many of the possible permutations to use. If using <code>grid_search</code> it will use
+the depth first ordering. If using <code>random_search</code> it will use the first k shuffled options. FYI, random search
+has been shown to be just as good as any Bayesian optimization method when using a reasonable number of samples (60),
+<a href="http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf">see this paper for more information</a>.</p>
+<hr />
+<h4 id="walltime-auto-resubmit">Walltime auto-resubmit</h4>
+<p>Lightning automatically resubmits jobs when they reach the walltime. Make sure to set the SIGUSR1 signal in 
+your SLURM script.   </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"># 90 seconds before training ends</span>
+<span class="c1">#SBATCH --signal=SIGUSR1@90</span>
+</pre></div>
+</td></tr></table>
+
+<p>When lightning receives the SIGUSR1 signal it will:
+1. save a checkpoint with 'hpc_ckpt' in the name.
+2. resubmit the job using the SLURM_JOB_ID  </p>
+<p>When the script starts again, Lightning will:
+1. search for a 'hpc_ckpt' checkpoint. 
+2. restore the model, optimizers, schedulers, epoch, etc...   </p>
+                
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/Testing loop.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Testing loop</h1>
+                
+                <p>To ensure you don't accidentally use test data to guide training decisions Lightning makes running the test set deliberate.   </p>
+<hr />
+<h4 id="test">test</h4>
+<p>You have two options to run the test set. 
+First case is where you test right after a full training routine.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># run full training</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>
+
+<span class="c1"># run test set</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">test</span><span class="p">()</span>
+</pre></div>
+</td></tr></table>
+
+<p>Second case is where you load a model and run the test set   </p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">MyLightningModule</span><span class="o">.</span><span class="n">load_from_metrics</span><span class="p">(</span>
+    <span class="n">weights_path</span><span class="o">=</span><span class="s1">&#39;/path/to/pytorch_checkpoint.ckpt&#39;</span><span class="p">,</span>
+    <span class="n">tags_csv</span><span class="o">=</span><span class="s1">&#39;/path/to/test_tube/experiment/version/meta_tags.csv&#39;</span><span class="p">,</span>
+    <span class="n">on_gpu</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">map_location</span><span class="o">=</span><span class="bp">None</span>
+<span class="p">)</span>
+
+<span class="c1"># init trainer with whatever options</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
+
+<span class="c1"># test (pass in the model)</span>
+<span class="n">trainer</span><span class="o">.</span><span class="n">test</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>In this second case, the options you pass to trainer will be used when running the test set (ie: 16-bit, dp, ddp, etc...)  </p>
+                
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+      
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+  <a href="#accumulated-gradients" title="Accumulated gradients" class="md-nav__link">
+    Accumulated gradients
+  </a>
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+      
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+    Force training for min or max epochs
+  </a>
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+</li>
+      
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+    Early stopping
+  </a>
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+      
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+  <a href="#force-disable-early-stop" title="Force disable early stop" class="md-nav__link">
+    Force disable early stop
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#gradient-clipping" title="Gradient Clipping" class="md-nav__link">
+    Gradient Clipping
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#inspect-gradient-norms" title="Inspect gradient norms" class="md-nav__link">
+    Inspect gradient norms
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-how-much-of-the-training-set-to-check" title="Set how much of the training set to check" class="md-nav__link">
+    Set how much of the training set to check
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#packed-sequences-as-inputs" title="Packed sequences as inputs" class="md-nav__link">
+    Packed sequences as inputs
+  </a>
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+      
+        <li class="md-nav__item">
+  <a href="#truncated-back-propagation-through-time" title="Truncated Back Propagation Through Time" class="md-nav__link">
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+  <a href="#accumulated-gradients" title="Accumulated gradients" class="md-nav__link">
+    Accumulated gradients
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+  
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+      
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+  <a href="#force-training-for-min-or-max-epochs" title="Force training for min or max epochs" class="md-nav__link">
+    Force training for min or max epochs
+  </a>
+  
+</li>
+      
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+    Early stopping
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+  
+</li>
+      
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+  <a href="#force-disable-early-stop" title="Force disable early stop" class="md-nav__link">
+    Force disable early stop
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#gradient-clipping" title="Gradient Clipping" class="md-nav__link">
+    Gradient Clipping
+  </a>
+  
+</li>
+      
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+  <a href="#inspect-gradient-norms" title="Inspect gradient norms" class="md-nav__link">
+    Inspect gradient norms
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-how-much-of-the-training-set-to-check" title="Set how much of the training set to check" class="md-nav__link">
+    Set how much of the training set to check
+  </a>
+  
+</li>
+      
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+  <a href="#packed-sequences-as-inputs" title="Packed sequences as inputs" class="md-nav__link">
+    Packed sequences as inputs
+  </a>
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+      
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+    Truncated Back Propagation Through Time
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+              
+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/Training Loop.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Training Loop</h1>
+                
+                <p>The lightning training loop handles everything except the actual computations of your model. To decide what will happen in your training loop, define the <a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#training_step">training_step function</a>.</p>
+<p>Below are all the things lightning automates for you in the training loop.</p>
+<hr />
+<h4 id="accumulated-gradients">Accumulated gradients</h4>
+<p>Accumulated gradients runs K small batches of size N before doing a backwards pass. The effect is a large effective batch size of size KxN.</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"># DEFAULT (ie: no accumulated grads)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">accumulate_grad_batches</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="force-training-for-min-or-max-epochs">Force training for min or max epochs</h4>
+<p>It can be useful to force training for a minimum number of epochs or limit to a max number</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"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">min_nb_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_nb_epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="early-stopping">Early stopping</h4>
+<p>The trainer already sets up default early stopping for you.
+To modify this behavior, pass in your own EarlyStopping callback.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning.callbacks</span> <span class="kn">import</span> <span class="n">EarlyStopping</span>
+
+<span class="c1"># DEFAULTS used by Trainer</span>
+<span class="n">early_stop_callback</span> <span class="o">=</span> <span class="n">EarlyStopping</span><span class="p">(</span>
+    <span class="n">monitor</span><span class="o">=</span><span class="s1">&#39;val_loss&#39;</span><span class="p">,</span>
+    <span class="n">min_delta</span><span class="o">=</span><span class="mf">0.00</span><span class="p">,</span>
+    <span class="n">patience</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
+    <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span>
+<span class="p">)</span>
+
+<span class="c1"># without passing anything in, uses the default callback above</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">()</span>
+
+<span class="c1"># pass in your own to override the default callback</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">early_stop_callback</span><span class="o">=</span><span class="n">early_stop_callback</span><span class="p">)</span>
+
+<span class="c1"># pass in None to disable it</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">early_stop_callback</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="force-disable-early-stop">Force disable early stop</h4>
+<p>To disable early stopping pass None to the early_stop_callback</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"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">early_stop_callback</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="gradient-clipping">Gradient Clipping</h4>
+<p>Gradient clipping may be enabled to avoid exploding gradients.
+Specifically, this will <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_">clip the gradient norm computed over all model parameters <em>together</em></a>.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (ie: don&#39;t clip)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">gradient_clip_val</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># clip gradients with norm above 0.5</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">gradient_clip_val</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="inspect-gradient-norms">Inspect gradient norms</h4>
+<p>Looking at grad norms can help you figure out where training might be going wrong.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (-1 doesn&#39;t track norms)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># track the LP norm (P=2 here)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-how-much-of-the-training-set-to-check">Set how much of the training set to check</h4>
+<p>If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.</p>
+<p>train_percent_check will be overwritten by overfit_pct if <code>overfit_pct &gt; 0</code></p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">train_percent_check</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+<span class="c1"># check 10% only</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">train_percent_check</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="packed-sequences-as-inputs">Packed sequences as inputs</h4>
+<p>When using PackedSequence, do 2 things:
+1. return either a padded tensor in dataset or a list of variable length tensors in the dataloader collate_fn (example above shows the list implementation).  <br />
+2. Pack the sequence in forward or training and validation steps depending on use case.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre> 1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># For use in dataloader</span>
+<span class="k">def</span> <span class="nf">collate_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
+    <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
+
+<span class="c1"># In module</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="n">x</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">pack_sequence</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">enforce_sorted</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="n">rnn</span><span class="o">.</span><span class="n">pack_sequence</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">enforce_sorted</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="truncated-back-propagation-through-time">Truncated Back Propagation Through Time</h4>
+<p>There are times when multiple backwards passes are needed for each batch. For example, it may save memory to use Truncated Back Propagation Through Time when training RNNs.</p>
+<p>When this flag is enabled each batch is split into sequences of size truncated_bptt_steps and passed to training_step(...) separately. A default splitting function is provided, however, you can override it for more flexibility. See <a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks#tbptt_split_batch">tbptt_split_batch</a>.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (single backwards pass per batch)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">truncated_bptt_steps</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
+
+<span class="c1"># (split batch into sequences of size 2)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">truncated_bptt_steps</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
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+  <a href="#check-validation-every-n-epochs" title="Check validation every n epochs" class="md-nav__link">
+    Check validation every n epochs
+  </a>
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+</li>
+      
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+  <a href="#set-how-much-of-the-validation-set-to-check" title="Set how much of the validation set to check" class="md-nav__link">
+    Set how much of the validation set to check
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+  <a href="#set-validation-check-frequency-within-1-training-epoch" title="Set validation check frequency within 1 training epoch" class="md-nav__link">
+    Set validation check frequency within 1 training epoch
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#set-the-number-of-validation-sanity-steps" title="Set the number of validation sanity steps" class="md-nav__link">
+    Set the number of validation sanity steps
+  </a>
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+      
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+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/Validation loop.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Validation loop</h1>
+                
+                <p>The lightning validation loop handles everything except the actual computations of your model. To decide what will happen in your validation loop, define the <a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#validation_step">validation_step function</a>.
+Below are all the things lightning automates for you in the validation loop.</p>
+<p><strong>Note</strong> <br />
+Lightning will run 5 steps of validation in the beginning of training as a sanity check so you don't have to wait until a full epoch to catch possible validation issues.</p>
+<hr />
+<h4 id="check-validation-every-n-epochs">Check validation every n epochs</h4>
+<p>If you have a small dataset you might want to check validation every n epochs</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"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">check_val_every_n_epoch</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-how-much-of-the-validation-set-to-check">Set how much of the validation set to check</h4>
+<p>If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag</p>
+<p>val_percent_check will be overwritten by overfit_pct if <code>overfit_pct &gt; 0</code></p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_percent_check</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+<span class="c1"># check 10% only</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_percent_check</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-how-much-of-the-test-set-to-check">Set how much of the test set to check</h4>
+<p>If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag</p>
+<p>test_percent_check will be overwritten by overfit_pct if <code>overfit_pct &gt; 0</code></p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">test_percent_check</span><span class="o">=</span><span class="mf">1.0</span><span class="p">)</span>
+
+<span class="c1"># check 10% only</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">test_percent_check</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-validation-check-frequency-within-1-training-epoch">Set validation check frequency within 1 training epoch</h4>
+<p>For large datasets it's often desirable to check validation multiple times within a training loop.
+Pass in a float to check that often within 1 training epoch.
+Pass in an int k to check every k training batches. Must use an int if using 
+an IterableDataset.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6
+7
+8</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_check_interval</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
+
+<span class="c1"># check every .25 of an epoch </span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_check_interval</span><span class="o">=</span><span class="mf">0.25</span><span class="p">)</span>
+
+<span class="c1"># check every 100 train batches (ie: for IterableDatasets or fixed frequency)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">val_check_interval</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="set-the-number-of-validation-sanity-steps">Set the number of validation sanity steps</h4>
+<p>Lightning runs a few steps of validation in the beginning of training. This avoids crashing in the validation loop sometime deep into a lengthy training loop.</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"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">nb_sanity_val_steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<p>You can use <code>Trainer(nb_sanity_val_steps=0)</code> to skip the sanity check.</p>
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+  
+</li>
+      
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+  <a href="#make-model-overfit-on-subset-of-data" title="Make model overfit on subset of data" class="md-nav__link">
+    Make model overfit on subset of data
+  </a>
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+</li>
+      
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+  <a href="#print-the-parameter-count-by-layer" title="Print the parameter count by layer" class="md-nav__link">
+    Print the parameter count by layer
+  </a>
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+</li>
+      
+        <li class="md-nav__item">
+  <a href="#print-which-gradients-are-nan" title="Print which gradients are nan" class="md-nav__link">
+    Print which gradients are nan
+  </a>
+  
+</li>
+      
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+  <a href="#log-gpu-usage" title="Log GPU usage" class="md-nav__link">
+    Log GPU usage
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+    Fast dev run
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+      
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+    Inspect gradient norms
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+    Make model overfit on subset of data
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+      
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+  <a href="#print-the-parameter-count-by-layer" title="Print the parameter count by layer" class="md-nav__link">
+    Print the parameter count by layer
+  </a>
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+</li>
+      
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+  <a href="#print-which-gradients-are-nan" title="Print which gradients are nan" class="md-nav__link">
+    Print which gradients are nan
+  </a>
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+      
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+  <a href="#log-gpu-usage" title="Log GPU usage" class="md-nav__link">
+    Log GPU usage
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+    </ul>
+  
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+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/debugging.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <h1>Debugging</h1>
+                
+                <p>These flags are useful to help debug a model.</p>
+<hr />
+<h4 id="fast-dev-run">Fast dev run</h4>
+<p>This flag is meant for debugging a full train/val/test loop. It'll activate callbacks, everything but only with 1 training and 1 validation batch.
+Use this to debug a full run of your program quickly</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"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">fast_dev_run</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="inspect-gradient-norms">Inspect gradient norms</h4>
+<p>Looking at grad norms can help you figure out where training might be going wrong.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT (-1 doesn&#39;t track norms)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># track the LP norm (P=2 here)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">track_grad_norm</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="make-model-overfit-on-subset-of-data">Make model overfit on subset of data</h4>
+<p>A useful debugging trick is to make your model overfit a tiny fraction of the data.</p>
+<p>setting <code>overfit_pct &gt; 0</code> will overwrite train_percent_check, val_percent_check, test_percent_check</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT don&#39;t overfit (ie: normal training)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">overfit_pct</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
+
+<span class="c1"># overfit on 1% of data </span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">overfit_pct</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="print-the-parameter-count-by-layer">Print the parameter count by layer</h4>
+<p>By default lightning prints a list of parameters <em>and submodules</em> when it starts training.</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="c1"># DEFAULT print a full list of all submodules and their parameters.</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">weights_summary</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">)</span>
+
+<span class="c1"># only print the top-level modules (i.e. the children of LightningModule).</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">weights_summary</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="print-which-gradients-are-nan">Print which gradients are nan</h4>
+<p>This option prints a list of tensors with nan gradients.</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"># DEFAULT</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span><span class="n">print_nan_grads</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+</pre></div>
+</td></tr></table>
+
+<hr />
+<h4 id="log-gpu-usage">Log GPU usage</h4>
+<p>Lightning automatically logs gpu usage to the test tube logs. It'll only do it at the metric logging interval, so it doesn't slow down training.</p>
+                
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+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/Trainer/index.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                <h1 id="trainer">Trainer</h1>
+<p>[<a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/trainer/trainer.py">Github Code</a>]</p>
+<p>The lightning trainer abstracts best practices for running a training, val, test routine. It calls parts of your model when it wants to hand over full control and otherwise makes training assumptions which are now standard practice in AI research.</p>
+<p>This is the basic use of the trainer:</p>
+<table class="codehilitetable"><tr><td class="linenos"><div class="linenodiv"><pre>1
+2
+3
+4
+5
+6</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="kn">from</span> <span class="nn">pytorch_lightning</span> <span class="kn">import</span> <span class="n">Trainer</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">LightningTemplate</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">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>But of course the fun is in all the advanced things it can do:</p>
+<p><strong>Checkpointing</strong>    </p>
+<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>
+<p><strong>Computing cluster (SLURM)</strong>    </p>
+<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>
+<p><strong>Debugging</strong>  </p>
+<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">Print 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>
+<p><strong>Distributed training</strong>    </p>
+<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>
+<p><strong>Experiment Logging</strong>   </p>
+<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>
+<p><strong>Training loop</strong>    </p>
+<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>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#packed-sequences-as-inputs">Packed sequences</a></li>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning//Training%20Loop/#truncated-back-propation-through-time">Truncated Back Propagation Through Time</a></li>
+</ul>
+<p><strong>Validation loop</strong>    </p>
+<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>
+<p><strong>Testing loop</strong>    </p>
+<ul>
+<li><a href="https://williamfalcon.github.io/pytorch-lightning/Trainer/Testing%20loop/">Run test set</a>  </li>
+</ul>
+                
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+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,m,i;e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=(r=e.stemmerSupport.Among,m=e.stemmerSupport.SnowballProgram,i=new function(){var i,t,n,s=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],o=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],u=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],c=new m;function l(){var e,r=c.limit-c.cursor;c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.find_among_b(o,4)?(c.bra=c.cursor,c.limit_backward=e,c.cursor=c.limit-r,c.cursor>c.limit_backward&&(c.cursor--,c.bra=c.cursor,c.slice_del())):c.limit_backward=e)}this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var e,r=c.cursor;return function(){var e,r=c.cursor+3;if(t=c.limit,0<=r&&r<=c.limit){for(i=r;;){if(e=c.cursor,c.in_grouping(d,97,248)){c.cursor=e;break}if((c.cursor=e)>=c.limit)return;c.cursor++}for(;!c.out_grouping(d,97,248);){if(c.cursor>=c.limit)return;c.cursor++}(t=c.cursor)<i&&(t=i)}}(),c.limit_backward=r,c.cursor=c.limit,function(){var e,r;if(c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(s,32),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.in_grouping_b(u,97,229)&&c.slice_del()}}(),c.cursor=c.limit,l(),c.cursor=c.limit,function(){var e,r,i,n=c.limit-c.cursor;if(c.ket=c.cursor,c.eq_s_b(2,"st")&&(c.bra=c.cursor,c.eq_s_b(2,"ig")&&c.slice_del()),c.cursor=c.limit-n,c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(a,5),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del(),i=c.limit-c.cursor,l(),c.cursor=c.limit-i;break;case 2:c.slice_from("løs")}}(),c.cursor=c.limit,c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.out_grouping_b(d,97,248)?(c.bra=c.cursor,n=c.slice_to(n),c.limit_backward=e,c.eq_v_b(n)&&c.slice_del()):c.limit_backward=e),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(m){if(void 0===m)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===m.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var l="2"==m.version[0];m.ja=function(){this.pipeline.reset(),this.pipeline.add(m.ja.trimmer,m.ja.stopWordFilter,m.ja.stemmer),l?this.tokenizer=m.ja.tokenizer:(m.tokenizer&&(m.tokenizer=m.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=m.ja.tokenizer))};var j=new m.TinySegmenter;m.ja.tokenizer=function(e){var r,t,i,n,o,s,p,a,u;if(!arguments.length||null==e||null==e)return[];if(Array.isArray(e))return e.map(function(e){return l?new m.Token(e.toLowerCase()):e.toLowerCase()});for(r=(t=e.toString().toLowerCase().replace(/^\s+/,"")).length-1;0<=r;r--)if(/\S/.test(t.charAt(r))){t=t.substring(0,r+1);break}for(o=[],i=t.length,p=a=0;a<=i;a++)if(s=a-p,t.charAt(a).match(/\s/)||a==i){if(0<s)for(n=j.segment(t.slice(p,a)).filter(function(e){return!!e}),u=p,r=0;r<n.length;r++)l?o.push(new m.Token(n[r],{position:[u,n[r].length],index:o.length})):o.push(n[r]),u+=n[r].length;p=a+1}return o},m.ja.stemmer=function(e){return e},m.Pipeline.registerFunction(m.ja.stemmer,"stemmer-ja"),m.ja.wordCharacters="一二三四五六七八九十百千万億兆一-龠々〆ヵヶぁ-んァ-ヴーｱ-ﾝﾞa-zA-Zａ-ｚＡ-Ｚ0-9０-９",m.ja.trimmer=m.trimmerSupport.generateTrimmer(m.ja.wordCharacters),m.Pipeline.registerFunction(m.ja.trimmer,"trimmer-ja"),m.ja.stopWordFilter=m.generateStopWordFilter("これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし".split(" ")),m.Pipeline.registerFunction(m.ja.stopWordFilter,"stopWordFilter-ja"),m.jp=m.ja,m.Pipeline.registerFunction(m.jp.stemmer,"stemmer-jp"),m.Pipeline.registerFunction(m.jp.trimmer,"trimmer-jp"),m.Pipeline.registerFunction(m.jp.stopWordFilter,"stopWordFilter-jp")}});
@@ -0,0 +1 @@
+module.exports=require("./lunr.ja");
@@ -0,0 +1 @@
+!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(o){o.multiLanguage=function(){for(var e=Array.prototype.slice.call(arguments),i=e.join("-"),t="",r=[],n=[],s=0;s<e.length;++s)"en"==e[s]?(t+="\\w",r.unshift(o.stopWordFilter),r.push(o.stemmer),n.push(o.stemmer)):(t+=o[e[s]].wordCharacters,r.unshift(o[e[s]].stopWordFilter),r.push(o[e[s]].stemmer),n.push(o[e[s]].stemmer));var p=o.trimmerSupport.generateTrimmer(t);return o.Pipeline.registerFunction(p,"lunr-multi-trimmer-"+i),r.unshift(p),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,r),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,n))}}}});
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,n,i;e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=(r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){var o,s,a=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],m=[new r("dt",-1,-1),new r("vt",-1,-1)],l=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],u=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],d=[119,125,149,1],c=new n;this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var e,r,n,i,t=c.cursor;return function(){var e,r=c.cursor+3;if(s=c.limit,0<=r||r<=c.limit){for(o=r;;){if(e=c.cursor,c.in_grouping(u,97,248)){c.cursor=e;break}if(e>=c.limit)return;c.cursor=e+1}for(;!c.out_grouping(u,97,248);){if(c.cursor>=c.limit)return;c.cursor++}(s=c.cursor)<o&&(s=o)}}(),c.limit_backward=t,c.cursor=c.limit,function(){var e,r,n;if(c.cursor>=s&&(r=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,e=c.find_among_b(a,29),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:n=c.limit-c.cursor,c.in_grouping_b(d,98,122)?c.slice_del():(c.cursor=c.limit-n,c.eq_s_b(1,"k")&&c.out_grouping_b(u,97,248)&&c.slice_del());break;case 3:c.slice_from("er")}}(),c.cursor=c.limit,r=c.limit-c.cursor,c.cursor>=s&&(e=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,c.find_among_b(m,2)?(c.bra=c.cursor,c.limit_backward=e,c.cursor=c.limit-r,c.cursor>c.limit_backward&&(c.cursor--,c.bra=c.cursor,c.slice_del())):c.limit_backward=e),c.cursor=c.limit,c.cursor>=s&&(i=c.limit_backward,c.limit_backward=s,c.ket=c.cursor,(n=c.find_among_b(l,11))?(c.bra=c.cursor,c.limit_backward=i,1==n&&c.slice_del()):c.limit_backward=i),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}});
@@ -0,0 +1 @@
+!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r||!t&&0!=t||!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var b;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(r){b=r,this.cursor=0,this.limit=r.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var r=b;return b=null,r},in_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor++,!0}return!1},in_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor--,!0}return!1},out_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(i<s||s<t)return this.cursor++,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor++,!0}return!1},out_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(i<s||s<t)return this.cursor--,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor--,!0}return!1},eq_s:function(r,t){if(this.limit-this.cursor<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor+i)!=t.charCodeAt(i))return!1;return this.cursor+=r,!0},eq_s_b:function(r,t){if(this.cursor-this.limit_backward<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor-r+i)!=t.charCodeAt(i))return!1;return this.cursor-=r,!0},find_among:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=r[c],_=f;_<l.s_size;_++){if(e+f==n){a=-1;break}if(a=b.charCodeAt(e+f)-l.s[_])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i||s==i||h)break;h=!0}}for(;;){if(u>=(l=r[i]).s_size){if(this.cursor=e+l.s_size,!l.method)return l.result;var m=l.method();if(this.cursor=e+l.s_size,m)return l.result}if((i=l.substring_i)<0)return 0}},find_among_b:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit_backward,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=(_=r[c]).s_size-1-f;0<=l;l--){if(e-f==n){a=-1;break}if(a=b.charCodeAt(e-1-f)-_.s[l])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i||s==i||h)break;h=!0}}for(;;){var _;if(u>=(_=r[i]).s_size){if(this.cursor=e-_.s_size,!_.method)return _.result;var m=_.method();if(this.cursor=e-_.s_size,m)return _.result}if((i=_.substring_i)<0)return 0}},replace_s:function(r,t,i){var s=i.length-(t-r);return b=b.substring(0,r)+i+b.substring(t),this.limit+=s,this.cursor>=t?this.cursor+=s:this.cursor>r&&(this.cursor=r),s},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>b.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),b.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,l,n;e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=(r=e.stemmerSupport.Among,l=e.stemmerSupport.SnowballProgram,n=new function(){var n,t,i=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],s=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],o=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],u=[119,127,149],m=new l;this.setCurrent=function(e){m.setCurrent(e)},this.getCurrent=function(){return m.getCurrent()},this.stem=function(){var e,r=m.cursor;return function(){var e,r=m.cursor+3;if(t=m.limit,0<=r||r<=m.limit){for(n=r;;){if(e=m.cursor,m.in_grouping(o,97,246)){m.cursor=e;break}if(m.cursor=e,m.cursor>=m.limit)return;m.cursor++}for(;!m.out_grouping(o,97,246);){if(m.cursor>=m.limit)return;m.cursor++}(t=m.cursor)<n&&(t=n)}}(),m.limit_backward=r,m.cursor=m.limit,function(){var e,r=m.limit_backward;if(m.cursor>=t&&(m.limit_backward=t,m.cursor=m.limit,m.ket=m.cursor,e=m.find_among_b(i,37),m.limit_backward=r,e))switch(m.bra=m.cursor,e){case 1:m.slice_del();break;case 2:m.in_grouping_b(u,98,121)&&m.slice_del()}}(),m.cursor=m.limit,e=m.limit_backward,m.cursor>=t&&(m.limit_backward=t,m.cursor=m.limit,m.find_among_b(s,7)&&(m.cursor=m.limit,m.ket=m.cursor,m.cursor>m.limit_backward&&(m.bra=--m.cursor,m.slice_del())),m.limit_backward=e),m.cursor=m.limit,function(){var e,r;if(m.cursor>=t){if(r=m.limit_backward,m.limit_backward=t,m.cursor=m.limit,m.ket=m.cursor,e=m.find_among_b(a,5))switch(m.bra=m.cursor,e){case 1:m.slice_del();break;case 2:m.slice_from("lös");break;case 3:m.slice_from("full")}m.limit_backward=r}}(),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});
@@ -0,0 +1 @@
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(t){if(void 0===t)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===t.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==t.version[0];t.th=function(){this.pipeline.reset(),this.pipeline.add(t.th.trimmer),i?this.tokenizer=t.th.tokenizer:(t.tokenizer&&(t.tokenizer=t.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=t.th.tokenizer))},t.th.wordCharacters="[฀-๿]",t.th.trimmer=t.trimmerSupport.generateTrimmer(t.th.wordCharacters),t.Pipeline.registerFunction(t.th.trimmer,"trimmer-th");var n=t.wordcut;n.init(),t.th.tokenizer=function(e){if(!arguments.length||null==e||null==e)return[];if(Array.isArray(e))return e.map(function(e){return i?new t.Token(e):e});var r=e.toString().replace(/^\s+/,"");return n.cut(r).split("|")}}});
@@ -1,74 +0,0 @@
-import os
-import sys
-
-from test_tube import HyperOptArgumentParser, Experiment
-from pytorch-lightning.models.trainer import Trainer
-from pytorch-lightning.utils.arg_parse import add_default_args
-from pytorch-lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
-from demo.example_model import ExampleModel
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # init experiment
-    exp = Experiment(
-        name=hparams.tt_name,
-        debug=hparams.debug,
-        save_dir=hparams.tt_save_path,
-        version=hparams.hpc_exp_number,
-        autosave=False,
-        description=hparams.tt_description
-    )
-
-    exp.argparse(hparams)
-    exp.save()
-
-    # build model
-    model = ExampleModel(hparams)
-
-    # callbacks
-    early_stop = EarlyStopping(
-        monitor='val_acc',
-        patience=3,
-        mode='min',
-        verbose=True,
-    )
-
-    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
-    checkpoint = ModelCheckpoint(
-        filepath=model_save_path,
-        save_function=None,
-        save_best_only=True,
-        verbose=True,
-        monitor='val_acc',
-        mode='min'
-    )
-
-    # configure trainer
-    trainer = Trainer(
-        experiment=exp,
-        checkpoint_callback=checkpoint,
-        early_stop_callback=early_stop,
-    )
-
-    # train model
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-
-    # use default args given by lightning
-    root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
-    parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
-    add_default_args(parent_parser, root_dir)
-
-    # allow model to overwrite or extend args
-    parser = ExampleModel.add_model_specific_args(parent_parser)
-    hyperparams = parser.parse_args()
-
-    # train model
-    main(hyperparams)
@@ -1,200 +0,0 @@
-import torch.nn as nn
-import numpy as np
-from pytorch-lightning.root_module.root_module import RootModule
-from test_tube import HyperOptArgumentParser
-from torchvision.datasets import MNIST
-import torchvision.transforms as transforms
-import torch
-import torch.nn.functional as F
-
-
-class ExampleModel(RootModule):
-    """
-    Sample model to show how to define a template
-    """
-
-    def __init__(self, hparams):
-        # init superclass
-        super(ExampleModel, self).__init__(hparams)
-
-        self.batch_size = hparams.batch_size
-
-        # build model
-        self.__build_model()
-
-    # ---------------------
-    # MODEL SETUP
-    # ---------------------
-    def __build_model(self):
-        """
-        Layout model
-        :return:
-        """
-        self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
-        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
-        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
-
-        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)
-
-    # ---------------------
-    # TRAINING
-    # ---------------------
-    def forward(self, x):
-        x = self.c_d1(x)
-        x = F.tanh(x)
-        x = self.c_d1_bn(x)
-        x = self.c_d1_drop(x)
-
-        x = self.c_d2(x)
-        logits = F.log_softmax(x, dim=1)
-
-        return logits
-
-    def loss(self, labels, logits):
-        nll = F.nll_loss(logits, labels)
-        return nll
-
-    def training_step(self, data_batch):
-        """
-        Called inside the training loop
-        :param data_batch:
-        :return:
-        """
-        # forward pass
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
-
-        # calculate loss
-        loss_val = self.loss(y, y_hat)
-
-        tqdm_dic = {'tng_loss': loss_val.item()}
-        return loss_val, tqdm_dic
-
-    def validation_step(self, data_batch):
-        """
-        Called inside the validation loop
-        :param data_batch:
-        :return:
-        """
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
-
-        loss_val = self.loss(y, y_hat)
-
-        # acc
-        labels_hat = torch.argmax(y_hat, dim=1)
-        val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
-
-        output = {'y_hat': y_hat, 'val_loss': loss_val.item(), 'val_acc': val_acc}
-        return output
-
-    def validation_end(self, outputs):
-        """
-        Called at the end of validation to aggregate outputs
-        :param outputs: list of individual outputs of each validation step
-        :return:
-        """
-        val_loss_mean = 0
-        accs = []
-        for output in outputs:
-            val_loss_mean += output['val_loss']
-            accs.append(output['val_acc'])
-
-        val_loss_mean /= len(outputs)
-        tqdm_dic = {'val_loss': val_loss_mean, 'val_acc': np.mean(accs)}
-        return tqdm_dic
-
-    def update_tng_log_metrics(self, logs):
-        return logs
-
-    # ---------------------
-    # MODEL SAVING
-    # ---------------------
-    def get_save_dict(self):
-        checkpoint = {'state_dict': self.state_dict()}
-        return checkpoint
-
-    def load_model_specific(self, checkpoint):
-        self.load_state_dict(checkpoint['state_dict'])
-        pass
-
-    # ---------------------
-    # TRAINING SETUP
-    # ---------------------
-    def configure_optimizers(self):
-        """
-        return whatever optimizers we want here
-        :return: list of optimizers
-        """
-        optimizer = self.choose_optimizer(self.hparams.optimizer_name, self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
-        self.optimizers = [optimizer]
-        return self.optimizers
-
-    def __dataloader(self, train):
-        # init data generators
-        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
-
-        dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)
-
-        loader = torch.utils.data.DataLoader(
-            dataset=dataset,
-            batch_size=self.hparams.batch_size,
-            shuffle=True
-        )
-
-        return loader
-
-    @property
-    def tng_dataloader(self):
-        if self._tng_dataloader is None:
-            try:
-                self._tng_dataloader = self.__dataloader(train=True)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._tng_dataloader
-
-    @property
-    def val_dataloader(self):
-        if self._val_dataloader is None:
-            try:
-                self._val_dataloader = self.__dataloader(train=False)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._val_dataloader
-
-    @property
-    def test_dataloader(self):
-        if self._test_dataloader is None:
-            try:
-                self._test_dataloader = self.__dataloader(train=False)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._test_dataloader
-
-    @staticmethod
-    def add_model_specific_args(parent_parser):
-        parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
-
-        # param overwrites
-        # parser.set_defaults(gradient_clip=5.0)
-
-        # network params
-        parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
-        parser.add_argument('--in_features', default=28*28)
-        parser.add_argument('--hidden_dim', default=500)
-        parser.add_argument('--out_features', default=10)
-
-        # data
-        parser.add_argument('--data_root', default='/Users/williamfalcon/Developer/personal/research_lib/research_proj/datasets/mnist', type=str)
-
-        # training params (opt)
-        parser.opt_list('--learning_rate', default=0.001, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
-                        tunable=False)
-        parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
-        parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
-        return parser
@@ -1,201 +0,0 @@
-import os
-import sys
-import numpy as np
-from time import sleep
-import torch
-
-from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
-from pytorch-lightning.models.trainer import Trainer
-from pytorch-lightning.utils.arg_parse import add_default_args
-
-from pytorch-lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-# ---------------------
-# DEFINE MODEL HERE
-# ---------------------
-from example_model import ExampleModel
-# ---------------------
-
-AVAILABLE_MODELS = {
-    'model_template': ExampleModel
-}
-
-
-"""
-Allows training by using command line arguments
-Run by:
-# TYPE YOUR RUN COMMAND HERE
-"""
-
-
-def main_local(hparams):
-    main(hparams, None, None)
-
-
-def main(hparams, cluster, results_dict):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    on_gpu = torch.cuda.is_available()
-    if hparams.disable_cuda:
-        on_gpu = False
-
-    device = 'cuda' if on_gpu else 'cpu'
-    hparams.__setattr__('device', device)
-    hparams.__setattr__('on_gpu', on_gpu)
-    hparams.__setattr__('nb_gpus', torch.cuda.device_count())
-    hparams.__setattr__('inference_mode', hparams.model_load_weights_path is not None)
-
-    # delay each training start to not overwrite logs
-    process_position, current_gpu = TRAINING_MODEL.get_process_position(hparams.gpus)
-    sleep(process_position + 1)
-
-    # init experiment
-    log_dir = os.path.dirname(os.path.realpath(__file__))
-    exp = Experiment(
-        name='test_tube_exp',
-        debug=True,
-        save_dir=log_dir,
-        version=0,
-        autosave=False,
-        description='test demo'
-    )
-
-    exp.argparse(hparams)
-    exp.save()
-
-    # build model
-    print('loading model...')
-    model = TRAINING_MODEL(hparams)
-    print('model built')
-
-    # callbacks
-    early_stop = EarlyStopping(
-        monitor=hparams.early_stop_metric,
-        patience=hparams.early_stop_patience,
-        verbose=True,
-        mode=hparams.early_stop_mode
-    )
-
-    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
-    checkpoint = ModelCheckpoint(
-        filepath=model_save_path,
-        save_function=None,
-        save_best_only=True,
-        verbose=True,
-        monitor=hparams.model_save_monitor_value,
-        mode=hparams.model_save_monitor_mode
-    )
-
-    # configure trainer
-    trainer = Trainer(
-        experiment=exp,
-        cluster=cluster,
-        checkpoint_callback=checkpoint,
-        early_stop_callback=early_stop,
-    )
-
-    # train model
-    trainer.fit(model)
-
-
-def get_default_parser(strategy, root_dir):
-
-    possible_model_names = list(AVAILABLE_MODELS.keys())
-    parser = HyperOptArgumentParser(strategy=strategy, add_help=False)
-    add_default_args(parser, root_dir, possible_model_names=possible_model_names, rand_seed=SEED)
-    return parser
-
-
-def get_model_name(args):
-    for i, arg in enumerate(args):
-        if 'model_name' in arg:
-            return args[i+1]
-
-
-def optimize_on_cluster(hyperparams):
-    # enable cluster training
-    cluster = SlurmCluster(
-        hyperparam_optimizer=hyperparams,
-        log_path=hyperparams.tt_save_path,
-        test_tube_exp_name=hyperparams.tt_name
-    )
-
-    # email for cluster coms
-    cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
-
-    # configure cluster
-    cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
-    cluster.job_time = '48:00:00'
-    cluster.gpu_type = '1080ti'
-    cluster.memory_mb_per_node = 48000
-
-    # any modules for code to run in env
-    cluster.add_command('source activate pytorch_lightning')
-
-    # name of exp
-    job_display_name = hyperparams.tt_name.split('_')[0]
-    job_display_name = job_display_name[0:3]
-
-    # run hopt
-    print('submitting jobs...')
-    cluster.optimize_parallel_cluster_gpu(
-        main,
-        nb_trials=hyperparams.nb_hopt_trials,
-        job_name=job_display_name
-    )
-
-
-if __name__ == '__main__':
-
-    model_name = get_model_name(sys.argv)
-    if model_name is None:
-        model_name = 'model_template'
-
-    # use default args
-    root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
-    parent_parser = get_default_parser(strategy='random_search', root_dir=root_dir)
-
-    # allow model to overwrite or extend args
-    TRAINING_MODEL = AVAILABLE_MODELS[model_name]
-    parser = TRAINING_MODEL.add_model_specific_args(parent_parser)
-    hyperparams = parser.parse_args()
-
-    # format GPU layout
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-    gpu_ids = hyperparams.gpus.split(';')
-
-    # RUN TRAINING
-    if hyperparams.on_cluster:
-        # Gets called when running via HPC cluster
-        print('RUNNING ON SLURM CLUSTER')
-        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpu_ids)
-        optimize_on_cluster(hyperparams)
-
-    elif hyperparams.single_run_gpu:
-        # run on 1 gpu
-        print(f'RUNNING 1 TRIAL ON GPU. gpu: {gpu_ids[0]}')
-        os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids[0]
-        main(hyperparams, None, None)
-
-    elif hyperparams.local or hyperparams.single_run:
-        # run 1 trial but on CPU
-        os.environ["CUDA_VISIBLE_DEVICES"] = '0'
-        print('RUNNING LOCALLY')
-        main(hyperparams, None, None)
-
-    else:
-        # multiple GPUs on same machine
-        print(f'RUNNING MULTI GPU. GPU ids: {gpu_ids}')
-        hyperparams.optimize_parallel_gpu(
-            main_local,
-            gpu_ids=gpu_ids,
-            nb_trials=hyperparams.nb_hopt_trials,
-            nb_workers=len(gpu_ids)
-        )
@@ -0,0 +1,871 @@
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+        Examples
+      </label>
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+      Examples
+    </a>
+    
+      
+<nav class="md-nav md-nav--secondary">
+  
+  
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+    <label class="md-nav__title" for="__toc">Table of contents</label>
+    <ul class="md-nav__list" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#template-model-definition" title="Template model definition" class="md-nav__link">
+    Template model definition
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#trainer-example" title="Trainer Example" class="md-nav__link">
+    Trainer Example
+  </a>
+  
+    <nav class="md-nav">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#cpu-hyperparameter-search" title="CPU hyperparameter search" class="md-nav__link">
+    CPU hyperparameter search
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#hyperparameter-search-on-a-single-or-multiple-gpus" title="Hyperparameter search on a single or multiple GPUs" class="md-nav__link">
+    Hyperparameter search on a single or multiple GPUs
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#hyperparameter-search-on-a-slurm-hpc-cluster" title="Hyperparameter search on a SLURM HPC cluster" class="md-nav__link">
+    Hyperparameter search on a SLURM HPC cluster
+  </a>
+  
+</li>
+        
+      </ul>
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+    <label class="md-nav__title" for="__toc">Table of contents</label>
+    <ul class="md-nav__list" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#template-model-definition" title="Template model definition" class="md-nav__link">
+    Template model definition
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#trainer-example" title="Trainer Example" class="md-nav__link">
+    Trainer Example
+  </a>
+  
+    <nav class="md-nav">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#cpu-hyperparameter-search" title="CPU hyperparameter search" class="md-nav__link">
+    CPU hyperparameter search
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#hyperparameter-search-on-a-single-or-multiple-gpus" title="Hyperparameter search on a single or multiple GPUs" class="md-nav__link">
+    Hyperparameter search on a single or multiple GPUs
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#hyperparameter-search-on-a-slurm-hpc-cluster" title="Hyperparameter search on a SLURM HPC cluster" class="md-nav__link">
+    Hyperparameter search on a SLURM HPC cluster
+  </a>
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+            <article class="md-content__inner md-typeset">
+              
+                
+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/examples/Examples.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <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
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+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
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+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
+4
+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
+2
+3
+4
+5
+6
+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
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27
+28
+29
+30
+31
+32
+33
+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>
+</td></tr></table>
+                
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+                  <a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/index.md" title="Edit this page" class="md-icon md-content__icon">&#xE3C9;</a>
+                
+                
+                  <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
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+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
+2
+3
+4
+5
+6
+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
+2
+3
+4
+5
+6
+7</pre></div></td><td class="code"><div class="codehilite"><pre><span></span><span class="k">if</span> <span class="n">use_bert</span><span class="p">:</span>
+    <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>
+                
+                  
+                
+              
+              
+                
+
+
+              
+            </article>
+          </div>
+        </div>
+      </main>
+      
+        
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+    
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+</html>
@@ -1,5 +0,0 @@
-[build-system]
-requires = [
-    "setuptools",
-    "wheel",
-]
@@ -1,203 +0,0 @@
-import torch.nn as nn
-import numpy as np
-from pytorch_lightning.root_module.root_module import RootModule
-from test_tube import HyperOptArgumentParser
-from torchvision.datasets import MNIST
-import torchvision.transforms as transforms
-import torch
-import torch.nn.functional as F
-
-
-class ExampleModel1(RootModule):
-    """
-    Sample model to show how to define a template
-    """
-
-    def __init__(self, hparams):
-        # init superclass
-        super(ExampleModel1, self).__init__(hparams)
-
-        self.batch_size = hparams.batch_size
-
-        # build model
-        self.__build_model()
-
-    # ---------------------
-    # MODEL SETUP
-    # ---------------------
-    def __build_model(self):
-        """
-        Layout model
-        :return:
-        """
-        self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
-        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
-        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
-
-        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)
-
-    # ---------------------
-    # TRAINING
-    # ---------------------
-    def forward(self, x):
-        x = self.c_d1(x)
-        x = F.tanh(x)
-        x = self.c_d1_bn(x)
-        x = self.c_d1_drop(x)
-
-        x = self.c_d2(x)
-        logits = F.log_softmax(x, dim=1)
-
-        return logits
-
-    def loss(self, labels, logits):
-        nll = F.nll_loss(logits, labels)
-        return nll
-
-    def training_step(self, data_batch):
-        """
-        Called inside the training loop
-        :param data_batch:
-        :return:
-        """
-        # forward pass
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
-
-        # calculate loss
-        loss_val = self.loss(y, y_hat)
-
-        tqdm_dic = {'jefe': 1}
-        return loss_val, tqdm_dic
-
-    def validation_step(self, data_batch):
-        """
-        Called inside the validation loop
-        :param data_batch:
-        :return:
-        """
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
-
-        loss_val = self.loss(y, y_hat)
-
-        # acc
-        labels_hat = torch.argmax(y_hat, dim=1)
-        val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
-
-        output = {'y_hat': y_hat, 'val_loss': loss_val.item(), 'val_acc': val_acc}
-        return output
-
-    def validation_end(self, outputs):
-        """
-        Called at the end of validation to aggregate outputs
-        :param outputs: list of individual outputs of each validation step
-        :return:
-        """
-        val_loss_mean = 0
-        accs = []
-        for output in outputs:
-            val_loss_mean += output['val_loss']
-            accs.append(output['val_acc'])
-
-        val_loss_mean /= len(outputs)
-        tqdm_dic = {'val_loss': val_loss_mean, 'val_acc': np.mean(accs)}
-        return tqdm_dic
-
-    def update_tng_log_metrics(self, logs):
-        return logs
-
-    # ---------------------
-    # MODEL SAVING
-    # ---------------------
-    def get_save_dict(self):
-        checkpoint = {
-            'state_dict': self.state_dict(),
-        }
-
-        return checkpoint
-
-    def load_model_specific(self, checkpoint):
-        self.load_state_dict(checkpoint['state_dict'])
-        pass
-
-    # ---------------------
-    # TRAINING SETUP
-    # ---------------------
-    def configure_optimizers(self):
-        """
-        return whatever optimizers we want here
-        :return: list of optimizers
-        """
-        optimizer = self.choose_optimizer(self.hparams.optimizer_name, self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
-        self.optimizers = [optimizer]
-        return self.optimizers
-
-    def __dataloader(self, train):
-        # init data generators
-        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
-
-        dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)
-
-        loader = torch.utils.data.DataLoader(
-            dataset=dataset,
-            batch_size=self.hparams.batch_size,
-            shuffle=True
-        )
-
-        return loader
-
-    @property
-    def tng_dataloader(self):
-        if self._tng_dataloader is None:
-            try:
-                self._tng_dataloader = self.__dataloader(train=True)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._tng_dataloader
-
-    @property
-    def val_dataloader(self):
-        if self._val_dataloader is None:
-            try:
-                self._val_dataloader = self.__dataloader(train=False)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._val_dataloader
-
-    @property
-    def test_dataloader(self):
-        if self._test_dataloader is None:
-            try:
-                self._test_dataloader = self.__dataloader(train=False)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._test_dataloader
-
-    @staticmethod
-    def add_model_specific_args(parent_parser):
-        parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
-
-        # param overwrites
-        # parser.set_defaults(gradient_clip=5.0)
-
-        # network params
-        parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
-        parser.add_argument('--in_features', default=28*28)
-        parser.add_argument('--hidden_dim', default=500)
-        parser.add_argument('--out_features', default=10)
-
-        # data
-        parser.add_argument('--data_root', default='/Users/williamfalcon/Developer/personal/research_lib/research_proj/datasets/mnist', type=str)
-
-        # training params (opt)
-        parser.opt_list('--learning_rate', default=0.001, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
-                        tunable=False)
-        parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
-        parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
-        return parser
@@ -1,455 +0,0 @@
-import torch
-import tqdm
-import numpy as np
-from pytorch_lightning.root_module.memory import get_gpu_memory_map
-import traceback
-from pytorch_lightning.root_module.model_saving import TrainerIO
-from torch.optim.lr_scheduler import MultiStepLR
-import pdb
-
-try:
-    from apex import amp
-    APEX_AVAILABLE = True
-except ModuleNotFoundError:
-    APEX_AVAILABLE = False
-
-class Trainer(TrainerIO):
-
-    def __init__(self,
-                 experiment,
-                 checkpoint_callback, early_stop_callback,
-                 cluster=None,
-                 process_position=0,
-                 current_gpu_name=0,
-                 on_gpu=False,
-                 enable_tqdm=True,
-                 overfit_pct=0.0,
-                 track_grad_norm=-1,
-                 check_val_every_n_epoch=1,
-                 fast_dev_run=False,
-                 accumulate_grad_batches=1,
-                 enable_early_stop=True, max_nb_epochs=5, min_nb_epochs=1,
-                 train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=0.95,
-                 log_save_interval=1, add_log_row_interval=1,
-                 lr_scheduler_milestones=None,
-                 use_amp=False,
-                 check_grad_nans=False,
-                 amp_level='O2',
-                 nb_sanity_val_steps=5):
-
-        # Transfer params
-        self.check_val_every_n_epoch = check_val_every_n_epoch
-        self.enable_early_stop = enable_early_stop
-        self.track_grad_norm = track_grad_norm
-        self.fast_dev_run = fast_dev_run
-        self.on_gpu = on_gpu
-        self.enable_tqdm = enable_tqdm
-        self.experiment = experiment
-        self.exp_save_path = experiment.get_data_path(experiment.name, experiment.version)
-        self.cluster = cluster
-        self.process_position = process_position
-        self.current_gpu_name = current_gpu_name
-        self.checkpoint_callback = checkpoint_callback
-        self.checkpoint_callback.save_function = self.save_checkpoint
-        self.early_stop = early_stop_callback
-        self.model = None
-        self.max_nb_epochs = max_nb_epochs
-        self.accumulate_grad_batches = accumulate_grad_batches
-        self.early_stop_callback = early_stop_callback
-        self.min_nb_epochs = min_nb_epochs
-        self.nb_sanity_val_steps = nb_sanity_val_steps
-        self.lr_scheduler_milestones = [] if lr_scheduler_milestones is None else [int(x.strip()) for x in lr_scheduler_milestones.split(',')]
-        self.lr_schedulers = []
-        self.amp_level = amp_level
-        self.check_grad_nans = check_grad_nans
-
-        # training state
-        self.optimizers = None
-        self.prog_bar = None
-        self.global_step = 0
-        self.current_epoch = 0
-        self.total_batches = 0
-
-        # logging
-        self.log_save_interval = log_save_interval
-        self.val_check_interval = val_check_interval
-        self.add_log_row_interval = add_log_row_interval
-
-        # dataloaders
-        self.tng_dataloader = None
-        self.test_dataloader = None
-        self.val_dataloader = None
-
-        # how much of the data to use
-        self.__determine_data_use_amount(train_percent_check, val_percent_check, test_percent_check, overfit_pct)
-        print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))
-
-        # apex test
-        self.use_amp = use_amp and APEX_AVAILABLE
-        if self.use_amp:
-            print('using 16bit precision')
-
-    def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct):
-        """
-        Use less data for debugging purposes
-        """
-        self.train_percent_check = train_percent_check
-        self.val_percent_check = val_percent_check
-        self.test_percent_check = test_percent_check
-        if overfit_pct > 0:
-            self.train_percent_check = overfit_pct
-            self.val_percent_check = overfit_pct
-            self.test_percent_check = overfit_pct
-
-    def __is_function_implemented(self, f_name):
-        f_op = getattr(self.model, f_name, None)
-        return callable(f_op)
-
-    @property
-    def __tng_tqdm_dic(self):
-        tqdm_dic = {
-            'tng_loss': '{0:.3f}'.format(self.avg_loss),
-            'gpu': '{}'.format(self.current_gpu_name),
-            'v_nb': '{}'.format(self.experiment.version),
-            'epoch': '{}'.format(self.current_epoch),
-            'batch_nb':'{}'.format(self.batch_nb),
-        }
-        tqdm_dic.update(self.tqdm_metrics)
-        return tqdm_dic
-
-    def __layout_bookeeping(self):
-        # training bookeeping
-        self.total_batch_nb = 0
-        self.running_loss = []
-        self.avg_loss = 0
-        self.batch_nb = 0
-        self.tqdm_metrics = {}
-
-        # determine number of training batches
-        self.nb_tng_batches = self.model.nb_batches(self.tng_dataloader)
-        self.nb_tng_batches = int(self.nb_tng_batches * self.train_percent_check)
-
-        # determine number of validation batches
-        self.nb_val_batches = self.model.nb_batches(self.val_dataloader)
-        self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)
-        self.nb_val_batches = max(1, self.nb_val_batches)
-        self.nb_val_batches = self.nb_val_batches
-
-        # determine number of test batches
-        self.nb_test_batches = self.model.nb_batches(self.test_dataloader)
-        self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)
-
-        # determine when to check validation
-        self.val_check_batch = int(self.nb_tng_batches * self.val_check_interval)
-
-    def __add_tqdm_metrics(self, metrics):
-        for k, v in metrics.items():
-            self.tqdm_metrics[k] = v
-
-    def validate(self, model, dataloader, max_batches):
-        """
-        Run validation code
-        :param model: PT model
-        :param dataloader: PT dataloader
-        :param max_batches: Scalar
-        :return:
-        """
-        print('validating...')
-
-        # enable eval mode
-        model.zero_grad()
-        model.eval()
-
-        # disable gradients to save memory
-        torch.set_grad_enabled(False)
-
-        # bookkeeping
-        outputs = []
-
-        # run training
-        for batch_i, data_batch in enumerate(dataloader):
-
-            if data_batch is None:
-                continue
-
-            # stop short when on fast dev run
-            if max_batches is not None and batch_i >= max_batches:
-                break
-
-            # -----------------
-            # RUN VALIDATION STEP
-            # -----------------
-            output = model.validation_step(data_batch, batch_i)
-            outputs.append(output)
-
-            # batch done
-            if self.enable_tqdm and self.prog_bar is not None:
-                self.prog_bar.update(1)
-
-        # give model a chance to do something with the outputs
-        val_results = model.validation_end(outputs)
-
-        # enable train mode again
-        model.train()
-
-        # enable gradients to save memory
-        torch.set_grad_enabled(True)
-        return val_results
-
-    def __get_dataloaders(self, model):
-        """
-        Dataloaders are provided by the model
-        :param model:
-        :return:
-        """
-        self.tng_dataloader = model.tng_dataloader
-        self.test_dataloader = model.test_dataloader
-        self.val_dataloader = model.val_dataloader
-
-    # -----------------------------
-    # MODEL TRAINING
-    # -----------------------------
-    def fit(self, model):
-        self.model = model
-        model.trainer = self
-
-        # transfer data loaders from model
-        self.__get_dataloaders(model)
-
-        # init training constants
-        self.__layout_bookeeping()
-
-        # CHOOSE OPTIMIZER
-        # filter out the weights that were done on gpu so we can load on good old cpus
-        self.optimizers = model.configure_optimizers()
-
-        if self.use_amp:
-            # An example
-            self.model, optimizer = amp.initialize(
-                self.model, self.optimizers[0], opt_level=self.amp_level,
-            )
-            self.optimizers[0] = optimizer
-            model.trainer = self
-
-        # add lr schedulers
-        if self.lr_scheduler_milestones is not None:
-            for optimizer in self.optimizers:
-                scheduler = MultiStepLR(optimizer, self.lr_scheduler_milestones)
-                self.lr_schedulers.append(scheduler)
-
-        # print model summary
-        model.summarize()
-
-        # put on gpu if needed
-        if self.on_gpu:
-            model = model.cuda()
-
-        # run tiny validation to make sure program won't crash during val
-        _ = self.validate(model, self.val_dataloader, max_batches=self.nb_sanity_val_steps)
-
-        # save exp to get started
-        self.experiment.save()
-
-        # enable cluster checkpointing
-        if self.cluster is not None:
-            self.enable_auto_hpc_walltime_manager()
-
-        # ---------------------------
-        # CORE TRAINING LOOP
-        # ---------------------------
-        self.__train()
-
-    def __train(self):
-        # run all epochs
-        for epoch_nb in range(self.current_epoch, self.max_nb_epochs):
-            # update the lr scheduler
-            for lr_scheduler in self.lr_schedulers:
-                lr_scheduler.step()
-
-            self.model.current_epoch = epoch_nb
-
-            # hook
-            if self.__is_function_implemented('on_epoch_start'):
-                self.model.on_epoch_start()
-
-            self.current_epoch = epoch_nb
-            self.total_batches = self.nb_tng_batches + self.nb_val_batches
-            self.batch_loss_value = 0  # accumulated grads
-
-            # init progbar when requested
-            if self.enable_tqdm:
-                self.prog_bar = tqdm.tqdm(range(self.total_batches), position=self.process_position)
-
-            for batch_nb, data_batch in enumerate(self.tng_dataloader):
-                self.batch_nb = batch_nb
-                self.global_step += 1
-                self.model.global_step = self.global_step
-
-                # stop when the flag is changed or we've gone past the amount requested in the batches
-                self.total_batch_nb += 1
-                met_batch_limit = batch_nb > self.nb_tng_batches
-                if met_batch_limit:
-                    break
-
-                # ---------------
-                # RUN TRAIN STEP
-                # ---------------
-                batch_result = self.__run_tng_batch(data_batch, batch_nb)
-                early_stop_epoch = batch_result == -1
-
-                # ---------------
-                # RUN VAL STEP
-                # ---------------
-                is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0
-                if self.fast_dev_run or is_val_check_batch or early_stop_epoch:
-                    self.__run_validation()
-
-                # when batch should be saved
-                if (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch:
-                    self.experiment.save()
-
-                # when metrics should be logged
-                if batch_nb % self.add_log_row_interval == 0 or early_stop_epoch:
-                    # count items in memory
-                    # nb_params, nb_tensors = count_mem_items()
-
-                    metrics = self.model.update_tng_log_metrics(self.__tng_tqdm_dic)
-
-                    # add gpu memory
-                    if self.on_gpu:
-                        mem_map = get_gpu_memory_map()
-                        metrics.update(mem_map)
-
-                    # add norms
-                    if self.track_grad_norm > 0:
-                        grad_norm_dic = self.model.grad_norm(self.track_grad_norm)
-                        metrics.update(grad_norm_dic)
-
-                    # log metrics
-                    self.experiment.log(metrics)
-                    self.experiment.save()
-
-                # hook
-                if self.__is_function_implemented('on_batch_end'):
-                    self.model.on_batch_end()
-
-                # end epoch early
-                if early_stop_epoch:
-                    break
-
-            # hook
-            if self.__is_function_implemented('on_epoch_end'):
-                self.model.on_epoch_end()
-
-            # early stopping
-            if self.enable_early_stop:
-                should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch_nb, logs=self.__tng_tqdm_dic)
-                met_min_epochs = epoch_nb > self.min_nb_epochs
-
-                # stop training
-                stop = should_stop and met_min_epochs
-                if stop:
-                    return
-
-
-    def __run_tng_batch(self, data_batch, batch_nb):
-        if data_batch is None:
-            return 0
-
-        # hook
-        if self.__is_function_implemented('on_batch_start'):
-            response = self.model.on_batch_start(data_batch)
-            if response == -1:
-                return -1
-
-        if self.enable_tqdm:
-            self.prog_bar.update(1)
-
-        # forward pass
-        # return a scalar value and a dic with tqdm metrics
-        loss, model_specific_tqdm_metrics_dic = self.model.training_step(data_batch, batch_nb)
-        self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
-
-        # backward pass
-        if self.use_amp:
-            for optimizer in self.optimizers:
-                with amp.scale_loss(loss, optimizer) as scaled_loss:
-                    scaled_loss.backward()
-        else:
-            loss.backward()
-
-        if self.check_grad_nans:
-            for param in self.model.parameters():
-                print(param.grad.float().sum())
-
-        self.batch_loss_value += loss.item()
-
-        # gradient update with accumulated gradients
-        if (self.batch_nb + 1) % self.accumulate_grad_batches == 0:
-
-            # update gradients across all optimizers
-            for optimizer in self.optimizers:
-                optimizer.step()
-
-                # clear gradients
-                optimizer.zero_grad()
-
-            # queuing loss across batches blows it up proportionally... divide out the number accumulated
-            self.batch_loss_value = self.batch_loss_value / self.accumulate_grad_batches
-
-            # track loss
-            self.running_loss.append(self.batch_loss_value)
-            self.batch_loss_value = 0
-            self.avg_loss = np.mean(self.running_loss[-100:])
-
-            # update progbar
-            if self.enable_tqdm:
-                # add model specific metrics
-                tqdm_metrics = self.__tng_tqdm_dic
-                self.prog_bar.set_postfix(**tqdm_metrics)
-
-        # activate batch end hook
-        if self.__is_function_implemented('on_batch_end'):
-            self.model.on_batch_end()
-
-        return 0
-
-    def __run_validation(self):
-        # decide if can check epochs
-        can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
-        if self.fast_dev_run:
-            print('skipping to check performance bc of --fast_dev_run')
-        elif not can_check_epoch:
-            return
-
-        try:
-            # hook
-            if self.__is_function_implemented('on_pre_performance_check'):
-                self.model.on_pre_performance_check()
-
-            # use full val set on end of epoch
-            # use a small portion otherwise
-            max_batches = None if not self.fast_dev_run else 1
-            model_specific_tqdm_metrics_dic = self.validate(
-                self.model,
-                self.val_dataloader,
-                max_batches
-            )
-            self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
-
-            # hook
-            if self.__is_function_implemented('on_post_performance_check'):
-                self.model.on_post_performance_check()
-
-        except Exception as e:
-            print(e)
-            print(traceback.print_exc())
-
-        if self.enable_tqdm:
-            # add model specific metrics
-            tqdm_metrics = self.__tng_tqdm_dic
-            self.prog_bar.set_postfix(**tqdm_metrics)
-
-        # model checkpointing
-        print('save callback...')
-        self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch, logs=self.__tng_tqdm_dic)
@@ -1,40 +0,0 @@
-import numpy as np
-from torch import nn
-
-"""
-Module to describe gradients
-"""
-
-
-class GradInformation(nn.Module):
-
-    def grad_norm(self, norm_type):
-        results = {}
-        total_norm = 0
-        for i, p in enumerate(self.parameters()):
-            if p.requires_grad:
-                try:
-                    param_norm = p.grad.data.norm(norm_type)
-                    total_norm += param_norm ** norm_type
-                    norm = param_norm ** (1 / norm_type)
-
-                    results['grad_{}_norm_{}'.format(norm_type, i)] = round(norm.data.cpu().numpy().flatten()[0], 3)
-                except Exception as e:
-                    # this param had no grad
-                    pass
-
-        total_norm = total_norm ** (1. / norm_type)
-        results['grad_{}_norm_total'.format(norm_type)] = round(total_norm.data.cpu().numpy().flatten()[0], 3)
-        return results
-
-
-    def describe_grads(self):
-        for p in self.parameters():
-            g = p.grad.data.numpy().flatten()
-            print(np.max(g), np.min(g), np.mean(g))
-
-
-    def describe_params(self):
-        for p in self.parameters():
-            g = p.data.numpy().flatten()
-            print(np.max(g), np.min(g), np.mean(g))
@@ -1,21 +0,0 @@
-import torch
-
-class ModelHooks(torch.nn.Module):
-    def on_batch_start(self, data_batch):
-        pass
-
-    def on_batch_end(self):
-        pass
-
-    def on_epoch_start(self):
-        pass
-
-    def on_epoch_end(self):
-        pass
-
-    def on_pre_performance_check(self):
-        pass
-
-    def on_post_performance_check(self):
-        pass
-
@@ -1,180 +0,0 @@
-import torch
-import gc
-import subprocess
-import numpy as np
-import pandas as pd
-
-
-'''
-Generates a summary of a model's layers and dimensionality
-'''
-
-
-class ModelSummary(object):
-
-    def __init__(self, model):
-        '''
-        Generates summaries of model layers and dimensions.
-        '''
-        self.model = model
-        self.in_sizes = []
-        self.out_sizes = []
-
-        self.summarize()
-
-    def __str__(self):
-        return self.summary.__str__()
-
-    def __repr__(self):
-        return self.summary.__str__()
-
-    def get_variable_sizes(self):
-        '''Run sample input through each layer to get output sizes'''
-        mods = list(self.model.modules())
-        in_sizes = []
-        out_sizes = []
-        input_ = self.example_input_array
-        for i in range(1, len(mods)):
-            m = mods[i]
-            if type(input_) is list or type(input_) is tuple:
-                out = m(*input_)
-            else:
-                out = m(input_)
-
-            if type(input_) is tuple or type(input_) is list:
-                in_size = []
-                for x in input_:
-                    if type(x) is list:
-                        in_size.append(len(x))
-                    else:
-                        in_size.append(x.size())
-            else:
-                in_size = np.array(input_.size())
-
-            in_sizes.append(in_size)
-
-            if type(out) is tuple or type(out) is list:
-                out_size = np.asarray([x.size() for x in out])
-            else:
-                out_size = np.array(out.size())
-
-            out_sizes.append(out_size)
-            input_ = out
-
-        self.in_sizes = in_sizes
-        self.out_sizes = out_sizes
-        return
-
-    def get_layer_names(self):
-        '''Collect Layer Names'''
-        mods = list(self.model.named_modules())
-        names = []
-        layers = []
-        for m in mods[1:]:
-            names += [m[0]]
-            layers += [str(m[1].__class__)]
-
-        layer_types = [x.split('.')[-1][:-2] for x in layers]
-
-        self.layer_names = names
-        self.layer_types = layer_types
-        return
-
-    def get_parameter_sizes(self):
-        '''Get sizes of all parameters in `model`'''
-        mods = list(self.model.modules())
-        sizes = []
-
-        for i in range(1,len(mods)):
-            m = mods[i]
-            p = list(m.parameters())
-            modsz = []
-            for j in range(len(p)):
-                modsz.append(np.array(p[j].size()))
-            sizes.append(modsz)
-
-        self.param_sizes = sizes
-        return
-
-    def get_parameter_nums(self):
-        '''Get number of parameters in each layer'''
-        param_nums = []
-        for mod in self.param_sizes:
-            all_params = 0
-            for p in mod:
-                all_params += np.prod(p)
-            param_nums.append(all_params)
-        self.param_nums = param_nums
-        return
-
-    def make_summary(self):
-        '''
-        Makes a summary listing with:
-
-        Layer Name, Layer Type, Input Size, Output Size, Number of Parameters
-        '''
-
-        df = pd.DataFrame( np.zeros( (len(self.layer_names), 3) ) )
-        df.columns = ['Name', 'Type', 'Params']
-
-        df['Name'] = self.layer_names
-        df['Type'] = self.layer_types
-        df['Params'] = self.param_nums
-
-        self.summary = df
-        return
-
-    def summarize(self):
-        self.get_layer_names()
-        self.get_parameter_sizes()
-        self.get_parameter_nums()
-        self.make_summary()
-
-
-def print_mem_stack():
-    for obj in gc.get_objects():
-        try:
-            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
-                print(type(obj), obj.size())
-        except Exception as e:
-            pass
-
-
-def count_mem_items():
-    nb_params = 0
-    nb_tensors = 0
-    for obj in gc.get_objects():
-        try:
-            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
-                obj_type = str(type(obj))
-                if 'parameter' in obj_type:
-                    nb_params += 1
-                else:
-                    nb_tensors += 1
-        except Exception as e:
-            pass
-
-    return nb_params, nb_tensors
-
-
-def get_gpu_memory_map():
-    """Get the current gpu usage.
-
-    Returns
-    -------
-    usage: dict
-        Keys are device ids as integers.
-        Values are memory usage as integers in MB.
-    """
-    result = subprocess.check_output(
-        [
-            'nvidia-smi', '--query-gpu=memory.used',
-            '--format=csv,nounits,noheader'
-        ], encoding='utf-8')
-    # Convert lines into a dictionary
-    gpu_memory = [int(x) for x in result.strip().split('\n')]
-    gpu_memory_map = {}
-    for k, v in zip(range(len(gpu_memory)), gpu_memory):
-        k = f'gpu_{k}'
-        gpu_memory_map[k] = v
-    return gpu_memory_map
@@ -1,169 +0,0 @@
-import torch
-import os
-import re
-
-
-class ModelIO(object):
-
-    def load_model_specific(self, checkpoint):
-        """
-        Do something with the checkpoint
-        :param checkpoint:
-        :return:
-        """
-        raise NotImplementedError
-
-    def get_save_dict(self):
-        """
-        Return specific things for the model
-        :return:
-        """
-        raise NotImplementedError
-
-
-class TrainerIO(object):
-
-    # --------------------
-    # MODEL SAVE CHECKPOINT
-    # --------------------
-    def save_checkpoint(self, filepath):
-        checkpoint = self.dump_checkpoint()
-
-        # do the actual save
-        torch.save(checkpoint, filepath)
-
-    def dump_checkpoint(self):
-        checkpoint = {
-            'epoch': self.current_epoch,
-            'checkpoint_callback_best': self.checkpoint_callback.best,
-            'early_stop_callback_wait': self.early_stop_callback.wait,
-            'early_stop_callback_patience': self.early_stop_callback.patience,
-            'global_step': self.global_step
-        }
-
-        optimizer_states = []
-        for i, optimizer in enumerate(self.optimizers):
-            optimizer_states.append(optimizer.state_dict())
-
-        checkpoint['optimizer_states'] = optimizer_states
-
-        # request what to save from the model
-        checkpoint_dict = self.model.get_save_dict()
-
-        # merge trainer and model saving items
-        checkpoint.update(checkpoint_dict)
-        return checkpoint
-
-    # --------------------
-    # HPC IO
-    # --------------------
-    def enable_auto_hpc_walltime_manager(self):
-        if self.cluster is None:
-            return
-
-        # allow test tube to handle model check pointing automatically
-        self.cluster.set_checkpoint_save_function(
-            self.hpc_save,
-            kwargs={
-                'folderpath': self.checkpoint_callback.filepath,
-                'experiment': self.experiment
-            }
-        )
-        self.cluster.set_checkpoint_load_function(
-            self.hpc_load,
-            kwargs={
-                'folderpath': self.checkpoint_callback.filepath,
-                'on_gpu': self.on_gpu
-            }
-        )
-
-    def restore_training_state(self, checkpoint):
-        """
-        Restore trainer state.
-        Model will get its change to update
-        :param checkpoint:
-        :return:
-        """
-        self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']
-        self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']
-        self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']
-        self.global_step = checkpoint['global_step']
-        self.current_epoch = checkpoint['epoch']
-
-        # restore the optimizers
-        optimizer_states = checkpoint['optimizer_states']
-        for optimizer, opt_state in zip(self.optimizers, optimizer_states):
-            optimizer.load_state_dict(opt_state)
-
-    # ----------------------------------
-    # PRIVATE OPS
-    # ----------------------------------
-    def hpc_save(self, folderpath, experiment):
-        # save exp to make sure we get all the metrics
-        experiment.save()
-
-        ckpt_number = self.max_ckpt_in_folder(folderpath) + 1
-
-        if not os.path.exists(folderpath):
-            os.makedirs(folderpath, exist_ok=True)
-        filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, ckpt_number)
-
-        # request what to save from the model
-        checkpoint_dict = self.dump_checkpoint()
-
-        # do the actual save
-        torch.save(checkpoint_dict, filepath)
-
-    def hpc_load(self, folderpath, on_gpu):
-        filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))
-
-        if on_gpu:
-            checkpoint = torch.load(filepath)
-        else:
-            checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)
-
-        # load training state
-        self.restore_training_state(checkpoint)
-
-        # load model state
-        self.model.load_model_specific(checkpoint)
-
-    def max_ckpt_in_folder(self, path):
-        files = os.listdir(path)
-        ckpt_vs = []
-        for name in files:
-            name = name.split('ckpt_')[-1]
-            name = re.sub('[^0-9]', '', name)
-            ckpt_vs.append(int(name))
-
-        return max(ckpt_vs)
-
-
-def load_hparams_from_tags_csv(tags_csv):
-    from argparse import Namespace
-    import pandas as pd
-
-    tags_df = pd.read_csv(tags_csv)
-    dic = tags_df.to_dict(orient='records')
-
-    ns_dict = {row['key']: convert(row['value']) for row in dic}
-
-    ns = Namespace(**ns_dict)
-    return ns
-
-
-def convert(val):
-    constructors = [int, float, str]
-
-    if type(val) is str:
-        if val.lower() == 'true':
-            return True
-        if val.lower() == 'false':
-            return False
-
-    for c in constructors:
-        try:
-            return c(val)
-        except ValueError:
-            pass
-    return val
@@ -1,22 +0,0 @@
-from torch import nn
-from torch import optim
-
-
-class OptimizerConfig(nn.Module):
-
-    def choose_optimizer(self, optimizer, params, optimizer_params, opt_name_key):
-        if optimizer == 'adam':
-            optimizer = optim.Adam(params, **optimizer_params)
-        if optimizer == 'sparse_adam':
-            optimizer = optim.SparseAdam(params, **optimizer_params)
-        if optimizer == 'sgd':
-            optimizer = optim.SGD(params, **optimizer_params)
-        if optimizer == 'adadelta':
-            optimizer = optim.Adadelta(params, **optimizer_params)
-
-        # transfer opt state if loaded
-        if opt_name_key in self.loaded_optimizer_states_dict:
-            state = self.loaded_optimizer_states_dict[opt_name_key]
-            optimizer.load_state_dict(state)
-
-        return optimizer
@@ -1,178 +0,0 @@
-import os
-import torch
-import math
-
-from pytorch_lightning.root_module.memory import ModelSummary
-from pytorch_lightning.root_module.grads import GradInformation
-from pytorch_lightning.root_module.model_saving import ModelIO, load_hparams_from_tags_csv
-from pytorch_lightning.root_module.optimization import OptimizerConfig
-from pytorch_lightning.root_module.hooks import ModelHooks
-
-
-class RootModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
-
-    def __init__(self, hparams):
-        super(RootModule, self).__init__()
-        self.hparams = hparams
-
-        self.dtype = torch.FloatTensor
-        self.exp_save_path = None
-        self.current_epoch = 0
-        self.global_step = 0
-        self.loaded_optimizer_states_dict = {}
-        self.fast_dev_run = hparams.fast_dev_run
-        self.overfit = hparams.overfit
-        self.gradient_clip = hparams.gradient_clip
-        self.num = 2
-        self.trainer = None
-
-        # track if gpu was requested for checkpointing
-        self.on_gpu = False
-        try:
-            self.on_gpu = hparams.on_gpu
-        except Exception as e:
-            pass
-
-        # computed vars for the dataloaders
-        self._tng_dataloader = None
-        self._val_dataloader = None
-        self._test_dataloader = None
-
-        if self.on_gpu:
-            print('running on gpu...')
-            torch.set_default_tensor_type(hparams.default_tensor_type)
-
-    def forward(self, *args, **kwargs):
-        """
-        Expand model in into whatever you need.
-        Also need to return the target
-        :param x:
-        :return:
-        """
-        raise NotImplementedError
-
-    def validation_step(self, data_batch, batch_nb):
-        """
-        return whatever outputs will need to be aggregated in validation_end
-        :param data_batch:
-        :return:
-        """
-        raise NotImplementedError
-
-    def validation_end(self, outputs):
-        """
-        Outputs has the appended output after each validation step
-        :param outputs:
-        :return: dic_with_metrics for tqdm
-        """
-        raise NotImplementedError
-
-    def training_step(self, data_batch, batch_nb):
-        """
-        return loss, dict with metrics for tqdm
-        :param data_batch:
-        :return:
-        """
-        raise NotImplementedError
-
-    def configure_optimizers(self):
-        """
-        Return array of optimizers
-        :return:
-        """
-        raise NotImplementedError
-
-    def update_tng_log_metrics(self, logs):
-        """
-        Chance to update metrics to be logged for training step.
-        For example, add music, images, etc... to log
-        :param logs:
-        :return:
-        """
-        raise NotImplementedError
-
-    def loss(self, *args, **kwargs):
-        """
-        Expand model_out into your components
-        :param model_out:
-        :return:
-        """
-        raise NotImplementedError
-
-    def summarize(self):
-        model_summary = ModelSummary(self)
-        print(model_summary)
-
-    def nb_batches(self, dataloader):
-        a = math.ceil(float(len(dataloader.dataset) / self.batch_size))
-        return int(a)
-
-    def freeze(self):
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def unfreeze(self):
-        for param in self.parameters():
-            param.requires_grad = True
-
-    @property
-    def tng_dataloader(self):
-        """
-        Implement a function to load an h5py of this data
-        :return:
-        """
-        raise NotImplementedError
-
-    @property
-    def test_dataloader(self):
-        """
-        Implement a function to load an h5py of this data
-        :return:
-        """
-        raise NotImplementedError
-
-    @property
-    def val_dataloader(self):
-        """
-        Implement a function to load an h5py of this data
-        :return:
-        """
-        raise NotImplementedError
-
-    @staticmethod
-    def get_process_position(gpus):
-        try:
-            current_gpu = os.environ["CUDA_VISIBLE_DEVICES"]
-            gpu_ids = gpus.split(';')
-            process_position = gpu_ids.index(current_gpu)
-            return process_position, current_gpu
-        except Exception as e:
-            return 0, 0
-
-    @classmethod
-    def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None):
-        """
-        Primary way of loading model from csv weights path
-        :param weights_path:
-        :param tags_csv:
-        :param on_gpu:
-        :param map_location: dic for mapping storage {'cuda:1':'cuda:0'}
-        :return:
-        """
-        hparams = load_hparams_from_tags_csv(tags_csv)
-        hparams.__setattr__('on_gpu', on_gpu)
-
-        if on_gpu:
-            if map_location is not None:
-                checkpoint = torch.load(weights_path, map_location=map_location)
-            else:
-                checkpoint = torch.load(weights_path)
-        else:
-            checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)
-
-        model = cls(hparams)
-
-        # allow model to load
-        model.load_model_specific(checkpoint)
-        model.load_state_dict(checkpoint['state_dict'], strict=False)
-        return model
@@ -1,214 +0,0 @@
-import os
-import sys
-
-import torch
-import numpy as np
-from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
-from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.utils.arg_parse import add_default_args
-from time import sleep
-
-from pytorch_lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-# ---------------------
-# DEFINE MODEL HERE
-# ---------------------
-from pytorch_lightning.models.sample_model_template.model_template import ExampleModel1
-# ---------------------
-
-AVAILABLE_MODELS = {
-    'model_1': ExampleModel1
-}
-
-
-"""
-Allows training by using command line arguments
-
-Run by: 
-# TYPE YOUR RUN COMMAND HERE
-"""
-
-
-def main_local(hparams):
-    main(hparams, None, None)
-
-
-def main(hparams, cluster, results_dict):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    on_gpu = torch.cuda.is_available()
-    if hparams.disable_cuda:
-        on_gpu = False
-
-    device = 'cuda' if on_gpu else 'cpu'
-    hparams.__setattr__('device', device)
-    hparams.__setattr__('on_gpu', on_gpu)
-    hparams.__setattr__('nb_gpus', torch.cuda.device_count())
-    hparams.__setattr__('inference_mode', hparams.model_load_weights_path is not None)
-
-    # delay each training start to not overwrite logs
-    process_position, current_gpu = TRAINING_MODEL.get_process_position(hparams.gpus)
-    sleep(process_position + 1)
-
-    # init experiment
-    exp = Experiment(
-        name=hparams.tt_name,
-        debug=hparams.debug,
-        save_dir=hparams.tt_save_path,
-        version=hparams.hpc_exp_number,
-        autosave=False,
-        description=hparams.tt_description
-    )
-
-    exp.argparse(hparams)
-    exp.save()
-
-    # build model
-    print('loading model...')
-    model = TRAINING_MODEL(hparams)
-    print('model built')
-
-    # callbacks
-    early_stop = EarlyStopping(
-        monitor=hparams.early_stop_metric,
-        patience=hparams.early_stop_patience,
-        verbose=True,
-        mode=hparams.early_stop_mode
-    )
-
-    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
-    checkpoint = ModelCheckpoint(
-        filepath=model_save_path,
-        save_function=None,
-        save_best_only=True,
-        verbose=True,
-        monitor=hparams.model_save_monitor_value,
-        mode=hparams.model_save_monitor_mode
-    )
-
-    # configure trainer
-    trainer = Trainer(
-        experiment=exp,
-        on_gpu=on_gpu,
-        cluster=cluster,
-        enable_tqdm=hparams.enable_tqdm,
-        overfit_pct=hparams.overfit,
-        track_grad_norm=hparams.track_grad_norm,
-        fast_dev_run=hparams.fast_dev_run,
-        check_val_every_n_epoch=hparams.check_val_every_n_epoch,
-        accumulate_grad_batches=hparams.accumulate_grad_batches,
-        process_position=process_position,
-        current_gpu_name=current_gpu,
-        checkpoint_callback=checkpoint,
-        early_stop_callback=early_stop,
-        enable_early_stop=hparams.enable_early_stop,
-        max_nb_epochs=hparams.max_nb_epochs,
-        min_nb_epochs=hparams.min_nb_epochs,
-        train_percent_check=hparams.train_percent_check,
-        val_percent_check=hparams.val_percent_check,
-        test_percent_check=hparams.test_percent_check,
-        val_check_interval=hparams.val_check_interval,
-        log_save_interval=hparams.log_save_interval,
-        add_log_row_interval=hparams.add_log_row_interval,
-        lr_scheduler_milestones=hparams.lr_scheduler_milestones
-    )
-
-    # train model
-    trainer.fit(model)
-
-
-def get_default_parser(strategy, root_dir):
-
-    possible_model_names = list(AVAILABLE_MODELS.keys())
-    parser = HyperOptArgumentParser(strategy=strategy, add_help=False)
-    add_default_args(parser, root_dir, possible_model_names, SEED)
-    return parser
-
-
-def get_model_name(args):
-    for i, arg in enumerate(args):
-        if 'model_name' in arg:
-            return args[i+1]
-
-
-def optimize_on_cluster(hyperparams):
-    # enable cluster training
-    cluster = SlurmCluster(
-        hyperparam_optimizer=hyperparams,
-        log_path=hyperparams.tt_save_path,
-        test_tube_exp_name=hyperparams.tt_name
-    )
-
-    # email for cluster coms
-    cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
-
-    # configure cluster
-    cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
-    cluster.job_time = '48:00:00'
-    cluster.gpu_type = '1080ti'
-    cluster.memory_mb_per_node = 48000
-
-    # any modules for code to run in env
-    cluster.add_command('source activate pytorch_lightning')
-
-    # name of exp
-    job_display_name = hyperparams.tt_name.split('_')[0]
-    job_display_name = job_display_name[0:3]
-
-    # run hopt
-    print('submitting jobs...')
-    cluster.optimize_parallel_cluster_gpu(
-        main,
-        nb_trials=hyperparams.nb_hopt_trials,
-        job_name=job_display_name
-    )
-
-
-if __name__ == '__main__':
-
-    model_name = get_model_name(sys.argv)
-
-    # use default args
-    root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
-    parent_parser = get_default_parser(strategy='random_search', root_dir=root_dir)
-
-    # allow model to overwrite or extend args
-    TRAINING_MODEL = AVAILABLE_MODELS[model_name]
-    parser = TRAINING_MODEL.add_model_specific_args(parent_parser)
-    parser.json_config('-c', '--config', default=root_dir + '/run_configs/local.json')
-    hyperparams = parser.parse_args()
-
-    # format GPU layout
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-    gpu_ids = hyperparams.gpus.split(';')
-
-    # RUN TRAINING
-    if hyperparams.on_cluster:
-        print('RUNNING ON SLURM CLUSTER')
-        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpu_ids)
-        optimize_on_cluster(hyperparams)
-
-    elif hyperparams.single_run_gpu:
-        print(f'RUNNING 1 TRIAL ON GPU. gpu: {gpu_ids[0]}')
-        os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids[0]
-        main(hyperparams, None, None)
-
-    elif hyperparams.local or hyperparams.single_run:
-        os.environ["CUDA_VISIBLE_DEVICES"] = '0'
-        print('RUNNING LOCALLY')
-        main(hyperparams, None, None)
-
-    else:
-        print(f'RUNNING MULTI GPU. GPU ids: {gpu_ids}')
-        hyperparams.optimize_parallel_gpu(
-            main_local,
-            gpu_ids=gpu_ids,
-            nb_trials=hyperparams.nb_hopt_trials,
-            nb_workers=len(gpu_ids)
-        )
@@ -1,76 +0,0 @@
-def add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None):
-
-    # tng, test, val check intervals
-    parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true', help='true = run test set also')
-    parser.add_argument('--check_val_every_n_epoch', default=1, type=int, help='check val every n epochs')
-    parser.opt_list('--accumulate_grad_batches', default=1, type=int, tunable=False,
-                    help='accumulates gradients k times before applying update. Simulates huge batch size')
-    parser.add_argument('--max_nb_epochs', default=200, type=int, help='cap epochs')
-    parser.add_argument('--min_nb_epochs', default=2, type=int, help='min epochs')
-    parser.add_argument('--train_percent_check', default=1.0, type=float, help='how much of tng set to check')
-    parser.add_argument('--val_percent_check', default=1.0, type=float, help='how much of val set to check')
-    parser.add_argument('--test_percent_check', default=1.0, type=float, help='how much of test set to check')
-
-    parser.add_argument('--val_check_interval', default=0.95, type=float, help='how much within 1 epoch to check val')
-    parser.add_argument('--log_save_interval', default=100, type=int, help='how many batches between log saves')
-    parser.add_argument('--add_log_row_interval', default=100, type=int, help='add log every k batches')
-
-    # early stopping
-    parser.add_argument('--disable_early_stop', dest='enable_early_stop', action='store_false')
-    parser.add_argument('--early_stop_metric', default='val_acc', type=str)
-    parser.add_argument('--early_stop_mode', default='min', type=str)
-    parser.add_argument('--early_stop_patience', default=3, type=int, help='number of epochs until stop')
-
-    # gradient handling
-    parser.add_argument('--gradient_clip', default=-1, type=int)
-    parser.add_argument('--track_grad_norm', default=-1, type=int, help='if > 0, will track this grad norm')
-
-    # model saving
-    parser.add_argument('--model_save_path', default=root_dir + '/model_weights')
-    parser.add_argument('--model_save_monitor_value', default='val_acc')
-    parser.add_argument('--model_save_monitor_mode', default='max')
-
-    # model paths
-    parser.add_argument('--model_load_weights_path', default=None, type=str)
-
-    if possible_model_names is not None:
-        parser.add_argument('--model_name', default='', help=','.join(possible_model_names))
-
-    # test_tube settings
-    parser.add_argument('-en', '--tt_name', default='r_lib_')
-    parser.add_argument('-td', '--tt_description', default='test research  lib')
-    parser.add_argument('--tt_save_path', default=root_dir + '/test_tube_logs', help='logging dir')
-    parser.add_argument('--enable_single_run', dest='single_run', action='store_true')
-    parser.add_argument('--nb_hopt_trials', default=1, type=int)
-    parser.add_argument('--log_stdout', dest='log_stdout', action='store_true')
-
-    # GPU
-    parser.add_argument('--per_experiment_nb_gpus', default=1, type=int)
-    parser.add_argument('--gpus', default='0', type=str)
-    parser.add_argument('--single_run_gpu', dest='single_run_gpu', action='store_true')
-    parser.add_argument('--disable_cuda', dest='disable_cuda', action='store_true')
-    parser.add_argument('--default_tensor_type', default='torch.cuda.FloatTensor', type=str)
-    parser.add_argument('--use_amp', dest='use_amp', action='store_true')
-    parser.add_argument('--check_grad_nans', dest='check_grad_nans', action='store_true')
-    parser.add_argument('--amp_level', default='O2',type=str)
-
-
-    # run on hpc
-    parser.add_argument('--on_cluster', dest='on_cluster', action='store_true')
-
-    # FAST training
-    # use these settings to make sure network has no bugs without running a full dataset
-    parser.add_argument('--fast_dev_run', dest='fast_dev_run', default=False, action='store_true', help='runs validation after 1 tng step')
-    parser.add_argument('--enable_tqdm', dest='enable_tqdm', default=False, action='store_true', help='false removes the prog bar')
-    parser.add_argument('--overfit', default=-1, type=float, help='% of dataset to use with this option. float, or -1 for none')
-
-    # debug args
-    if rand_seed is not None:
-        parser.add_argument('--random_seed', default=rand_seed, type=int)
-
-    parser.add_argument('--live', dest='live', action='store_true', help='runs on gpu without cluster')
-    parser.add_argument('--enable_debug', dest='debug', action='store_true', help='enables/disables test tube')
-    parser.add_argument('--enable_local', dest='local', action='store_true', help='enables local tng')
-
-    # optimizer
-    parser.add_argument('--lr_scheduler_milestones', default=None, type=str)
@@ -1,104 +0,0 @@
-import torch
-import numpy as np
-from copy import deepcopy
-
-
-class PretrainedEmbedding(torch.nn.Embedding):
-
-    def __init__(self, embedding_path, embedding_dim, task_vocab, freeze=True, *args, **kwargs):
-        """
-        Loads a prebuilt pytorch embedding from any embedding formated file.
-        Padding=0 by default.
-
-        >>> emb = PretrainedEmbedding(embedding_path='glove.840B.300d.txt',embedding_dim=300, task_vocab={'hello': 1, 'world': 2})
-        >>> data = torch.Tensor([[0, 1], [0, 2]]).long()
-        >>> embedded = emb(data)
-        
-
-
-        :param embedding_path:
-        :param emb_dim:
-        :param task_vocab:
-        :param freeze:
-        :return:
-        """
-        # count the vocab
-        self.vocab_size = max(task_vocab.values()) + 1
-        super(PretrainedEmbedding, self).__init__(self.vocab_size, embedding_dim, padding_idx=0, *args, **kwargs)
-
-        # load pretrained embeddings
-        new_emb = self.__load_task_specific_embeddings(deepcopy(task_vocab), embedding_path, embedding_dim, freeze)
-
-        # transfer weights
-        self.weight = new_emb.weight
-
-        # apply freeze
-        should_freeze = not freeze
-        self.weight.requires_grad = should_freeze
-
-    def __load_task_specific_embeddings(self, vocab_words, embedding_path, emb_dim, freeze):
-        """
-        Iterates embedding file to only pull out task specific embeddings
-        :param vocab_words:
-        :param embedding_path:
-        :param emb_dim:
-        :param freeze:
-        :return:
-        """
-
-        # holds final embeddings for relevant words
-        embeddings = np.zeros(shape=(self.vocab_size, emb_dim))
-
-        # load embedding line by line and extract relevant embeddings
-        with open(embedding_path, encoding='utf-8') as f:
-            for line in f:
-                tokens = line.split(' ')
-                word = tokens[0]
-                embedding = tokens[1:]
-                embedding[-1] = embedding[-1][:-1]  # remove last new line
-
-                if word in vocab_words:
-                    vocab_word_i = vocab_words[word]
-
-                    # skip words that try to overwrite pad idx
-                    if vocab_word_i == 0:
-                        del vocab_words[word]
-                        continue
-
-                    emb_vals = np.asarray([float(x) for x in embedding])
-                    embeddings[vocab_word_i] = emb_vals
-
-                    # remove vocab word to early terminate
-                    del vocab_words[word]
-
-                # early break
-                if len(vocab_words) == 0:
-                    break
-
-        # add random vectors for the non-pretrained words
-        # these are vocab words NOT found in the pretrained embeddings
-        for w, i in vocab_words.items():
-            # skip words that try to overwrite pad idx
-            if i == 0:
-                continue
-
-            embedding = np.random.normal(size=emb_dim)
-            embeddings[i] = embedding
-
-        # turn into pt embedding
-        embeddings = torch.FloatTensor(embeddings)
-        embeddings = torch.nn.Embedding.from_pretrained(embeddings, freeze=freeze)
-
-        return embeddings
-
-
-if __name__ == '__main__':
-    emb = PretrainedEmbedding(
-        embedding_path='/Users/waf/Developer',
-        embedding_dim=300,
-        task_vocab={'hello': 1, 'world': 2}
-    )
-
-    data = torch.Tensor([[0, 1], [0, 2]]).long()
-    embedded = emb(data)
-    print(embedded)
@@ -1,28 +0,0 @@
-from matplotlib import pyplot as plt
-import numpy as np
-np.seterr(divide='ignore', invalid='ignore')
-
-
-def plot_confusion_matrix(cm,
-                          save_path,
-                          normalize=False,
-                          title='Confusion matrix',
-                          ylabel='y',
-                          xlabel='x'):
-    """
-    This function prints and plots the confusion matrix.
-    Normalization can be applied by setting `normalize=True`.
-    """
-    if normalize:
-        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
-        print("Normalized confusion matrix")
-    else:
-        print('Confusion matrix, without normalization')
-
-    fig = plt.figure()
-    plt.matshow(cm)
-    plt.title(title)
-    plt.colorbar()
-    plt.ylabel(ylabel)
-    plt.xlabel(xlabel)
-    plt.savefig(save_path)
@@ -1,261 +0,0 @@
-import numpy as np
-import os, shutil
-
-
-class Callback(object):
-    """Abstract base class used to build new callbacks.
-    # Properties
-        params: dict. Training parameters
-            (eg. verbosity, batch size, number of epochs...).
-        model: instance of `keras.models.Model`.
-            Reference of the model being trained.
-    The `logs` dictionary that callback methods
-    take as argument will contain keys for quantities relevant to
-    the current batch or epoch.
-    Currently, the `.fit()` method of the `Sequential` model class
-    will include the following quantities in the `logs` that
-    it passes to its callbacks:
-        on_epoch_end: logs include `acc` and `loss`, and
-            optionally include `val_loss`
-            (if validation is enabled in `fit`), and `val_acc`
-            (if validation and accuracy monitoring are enabled).
-        on_batch_begin: logs include `size`,
-            the number of samples in the current batch.
-        on_batch_end: logs include `loss`, and optionally `acc`
-            (if accuracy monitoring is enabled).
-    """
-
-    def __init__(self):
-        self.validation_data = None
-        self.model = None
-
-    def set_params(self, params):
-        self.params = params
-
-    def set_model(self, model):
-        self.model = model
-
-    def on_epoch_begin(self, epoch, logs=None):
-        pass
-
-    def on_epoch_end(self, epoch, logs=None):
-        pass
-
-    def on_batch_begin(self, batch, logs=None):
-        pass
-
-    def on_batch_end(self, batch, logs=None):
-        pass
-
-    def on_train_begin(self, logs=None):
-        pass
-
-    def on_train_end(self, logs=None):
-        pass
-
-
-class EarlyStopping(Callback):
-    """Stop training when a monitored quantity has stopped improving.
-    # Arguments
-        monitor: quantity to be monitored.
-        min_delta: minimum change in the monitored quantity
-            to qualify as an improvement, i.e. an absolute
-            change of less than min_delta, will count as no
-            improvement.
-        patience: number of epochs with no improvement
-            after which training will be stopped.
-        verbose: verbosity mode.
-        mode: one of {auto, min, max}. In `min` mode,
-            training will stop when the quantity
-            monitored has stopped decreasing; in `max`
-            mode it will stop when the quantity
-            monitored has stopped increasing; in `auto`
-            mode, the direction is automatically inferred
-            from the name of the monitored quantity.
-    """
-
-    def __init__(self, monitor='val_loss',
-                 min_delta=0.0, patience=0, verbose=0, mode='auto'):
-        super(EarlyStopping, self).__init__()
-
-        self.monitor = monitor
-        self.patience = patience
-        self.verbose = verbose
-        self.min_delta = min_delta
-        self.wait = 0
-        self.stopped_epoch = 0
-
-        if mode not in ['auto', 'min', 'max']:
-            print('EarlyStopping mode %s is unknown, fallback to auto mode.' % mode)
-            mode = 'auto'
-
-        if mode == 'min':
-            self.monitor_op = np.less
-        elif mode == 'max':
-            self.monitor_op = np.greater
-        else:
-            if 'acc' in self.monitor:
-                self.monitor_op = np.greater
-            else:
-                self.monitor_op = np.less
-
-        if self.monitor_op == np.greater:
-            self.min_delta *= 1
-        else:
-            self.min_delta *= -1
-
-        self.on_train_begin()
-
-    def on_train_begin(self, logs=None):
-        # Allow instances to be re-used
-        self.wait = 0
-        self.stopped_epoch = 0
-        self.best = np.Inf if self.monitor_op == np.less else -np.Inf
-
-    def on_epoch_end(self, epoch, logs=None):
-        current = logs.get(self.monitor)
-        stop_training = False
-        if current is None:
-            print('Early stopping conditioned on metric `%s` ''which is not available. Available metrics are: %s' %
-                (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning
-            )
-            exit(-1)
-
-        if self.monitor_op(current - self.min_delta, self.best):
-            self.best = current
-            self.wait = 0
-        else:
-            self.wait += 1
-            if self.wait >= self.patience:
-                self.stopped_epoch = epoch
-                stop_training = True
-                self.on_train_end()
-
-        return stop_training
-
-    def on_train_end(self, logs=None):
-        if self.stopped_epoch > 0 and self.verbose > 0:
-            print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))
-
-
-class ModelCheckpoint(Callback):
-    """Save the model after every epoch.
-    `filepath` can contain named formatting options,
-    which will be filled the value of `epoch` and
-    keys in `logs` (passed in `on_epoch_end`).
-    For example: if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`,
-    then the model checkpoints will be saved with the epoch number and
-    the validation loss in the filename.
-    # Arguments
-        filepath: string, path to save the model file.
-        monitor: quantity to monitor.
-        verbose: verbosity mode, 0 or 1.
-        save_best_only: if `save_best_only=True`,
-            the latest best model according to
-            the quantity monitored will not be overwritten.
-        mode: one of {auto, min, max}.
-            If `save_best_only=True`, the decision
-            to overwrite the current save file is made
-            based on either the maximization or the
-            minimization of the monitored quantity. For `val_acc`,
-            this should be `max`, for `val_loss` this should
-            be `min`, etc. In `auto` mode, the direction is
-            automatically inferred from the name of the monitored quantity.
-        save_weights_only: if True, then only the model's weights will be
-            saved (`model.save_weights(filepath)`), else the full model
-            is saved (`model.save(filepath)`).
-        period: Interval (number of epochs) between checkpoints.
-    """
-
-    def __init__(self, filepath, save_function, monitor='val_loss', verbose=0,
-                 save_best_only=False, save_weights_only=False,
-                 mode='auto', period=1, prefix=''):
-        super(ModelCheckpoint, self).__init__()
-        self.monitor = monitor
-        self.save_function = save_function
-        self.verbose = verbose
-        self.filepath = filepath
-        self.save_best_only = save_best_only
-        self.save_weights_only = save_weights_only
-        self.period = period
-        self.epochs_since_last_save = 0
-        self.prefix = prefix
-
-        if mode not in ['auto', 'min', 'max']:
-            print('ModelCheckpoint mode %s is unknown, '
-                          'fallback to auto mode.' % (mode),
-                          RuntimeWarning)
-            mode = 'auto'
-
-        if mode == 'min':
-            self.monitor_op = np.less
-            self.best = np.Inf
-        elif mode == 'max':
-            self.monitor_op = np.greater
-            self.best = -np.Inf
-        else:
-            if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):
-                self.monitor_op = np.greater
-                self.best = -np.Inf
-            else:
-                self.monitor_op = np.less
-                self.best = np.Inf
-
-    def save_model(self, filepath, overwrite):
-        dirpath = '/'.join(filepath.split('/')[:-1])
-
-        # make paths
-        os.makedirs(os.path.dirname(filepath), exist_ok=True)
-
-        if overwrite:
-            for filename in os.listdir(dirpath):
-                if self.prefix in filename:
-                    path_to_delete = os.path.join(dirpath, filename)
-                    try:
-                        shutil.rmtree(path_to_delete)
-                    except OSError:
-                        os.remove(path_to_delete)
-
-        # delegate the saving to the model
-        self.save_function(filepath)
-
-    def on_epoch_end(self, epoch, logs=None):
-        logs = logs or {}
-        self.epochs_since_last_save += 1
-        if self.epochs_since_last_save >= self.period:
-            self.epochs_since_last_save = 0
-            filepath = '{}/{}_ckpt_epoch_{}.ckpt'.format(self.filepath, self.prefix, epoch + 1)
-            if self.save_best_only:
-                current = logs.get(self.monitor)
-                if current is None:
-                    print('Can save best model only with %s available, '
-                                  'skipping.' % (self.monitor), RuntimeWarning)
-                else:
-                    if self.monitor_op(current, self.best):
-                        if self.verbose > 0:
-                            print('\nEpoch %05d: %s improved from %0.5f to %0.5f,'
-                                  ' saving model to %s'
-                                  % (epoch + 1, self.monitor, self.best,
-                                     current, filepath))
-                        self.best = current
-                        self.save_model(filepath, overwrite=True)
-
-                    else:
-                        if self.verbose > 0:
-                            print('\nEpoch %05d: %s did not improve' %
-                                  (epoch + 1, self.monitor))
-            else:
-                if self.verbose > 0:
-                    print('\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))
-                self.save_model(filepath, overwrite=False)
-
-
-if __name__ == '__main__':
-    c = EarlyStopping(min_delta=0.9, patience=2, verbose=True)
-    losses = [10, 9, 8, 8, 6, 4.3, 5, 4.4, 2.8, 2.5]
-    for i, loss in enumerate(losses):
-        should_stop = c.on_epoch_end(i, logs={'val_loss': loss})
-        print(loss)
-        if should_stop:
-            break
-
@@ -1,27 +1,2 @@
-atomicwrites==1.2.1
-attrs==18.2.0
-certifi==2018.11.29
-cffi==1.11.5
-h5py==2.9.0
-imageio==2.4.1
-mkl-fft==1.0.6
-mkl-random==1.0.2
-more-itertools==5.0.0
-numpy==1.15.4
-olefile==0.46
-pandas==0.23.4
-Pillow==5.3.0
-pluggy==0.8.0
-py==1.7.0
-pycparser==2.19
-pytest==4.0.2
-python-dateutil==2.7.5
-pytz==2018.7
-scikit-learn==0.20.2
-scipy==1.2.0
-six==1.12.0
-sklearn==0.0
-test-tube==0.6282
-torch==1.0.0
-torchvision==0.2.1
-tqdm==4.28.1
+mkdocs-material==4.4.0
+mkdocs==1.0.4
@@ -1,21 +0,0 @@
-[tool:pytest]
-norecursedirs =
-    .git
-    dist
-    build
-python_files =
-    test_*.py
-doctest_plus = disabled
-addopts = --strict
-markers =
-    slow
-    remote_data
-    filterwarnings
-
-[pycodestyle]
-ignore = E731,W504
-max-line-length = 120
-
-[flake8]
-ignore = E731,W504,F401,F841
-max-line-length = 120
@@ -1,29 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import setup, find_packages
-
-# https://packaging.python.org/guides/single-sourcing-package-version/
-
-# http://blog.ionelmc.ro/2014/05/25/python-packaging/
-setup(
-    name="pytorch-lightning",
-    version='0.1.dev1832',
-    description="The Keras for ML researchers using PyTorch",
-    author="William Falcon",
-    author_email="waf2107@columbia.edu",
-    url="https://github.com/williamFalcon/pytorch-lightning",
-    download_url="https://github.com/williamFalcon/pytorch-lightning",
-    license="MIT",
-    keywords=["deep learning", "pytorch", "AI"],
-    python_requires=">=3.5",
-    install_requires=[
-        "torch",
-        "tqdm",
-        "test-tube",
-    ],
-    packages=find_packages(),
-    long_description=open("README.md", encoding="utf-8").read(),
-    long_description_content_type='text/markdown',
-    include_package_data=True,
-    zip_safe=False,
-)
@@ -0,0 +1,78 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+    <url>
+     <loc>None</loc>
+     <lastmod>2019-11-06</lastmod>
+     <changefreq>daily</changefreq>
+    </url>
+</urlset>
@@ -0,0 +1,21 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" width="99" height="20">
+    <linearGradient id="b" x2="0" y2="100%">
+        <stop offset="0" stop-color="#bbb" stop-opacity=".1"/>
+        <stop offset="1" stop-opacity=".1"/>
+    </linearGradient>
+    <mask id="a">
+        <rect width="99" height="20" rx="3" fill="#fff"/>
+    </mask>
+    <g mask="url(#a)">
+        <path fill="#555" d="M0 0h63v20H0z"/>
+        <path fill="#4c1" d="M63 0h36v20H63z"/>
+        <path fill="url(#b)" d="M0 0h99v20H0z"/>
+    </g>
+    <g fill="#fff" text-anchor="middle" font-family="DejaVu Sans,Verdana,Geneva,sans-serif" font-size="11">
+        <text x="31.5" y="15" fill="#010101" fill-opacity=".3">coverage</text>
+        <text x="31.5" y="14">coverage</text>
+        <text x="80" y="15" fill="#010101" fill-opacity=".3">99%</text>
+        <text x="80" y="14">99%</text>
+    </g>
+</svg>
@@ -1,20 +0,0 @@
-#!/bin/bash
-
-version=$1
-
-git commit -am "release v$version"
-git tag $version -m "test_tube v$version"
-git push --tags origin master
-
-# push to pypi
-rm -rf ./dist/*
-python3 setup.py sdist
-twine upload dist/*
-
-
-
-# to update docs
-# cd to root dir
-# mkdocs gh-deploy
-
-
Author	SHA1	Message	Date
William Falcon	2cc732ca16	Deployed `b35229d` with MkDocs version: 1.0.4	2019-11-06 15:08:12 -05:00
William Falcon	65095b6cac	Deployed `58d52c2` with MkDocs version: 1.0.4	2019-10-18 23:57:14 +01:00
William Falcon	0675db8074	Deployed `eca0e7c` with MkDocs version: 1.0.4	2019-10-05 16:34:18 -05:00
William Falcon	9b5ef41046	Deployed `49c7d54` with MkDocs version: 1.0.4	2019-10-05 16:32:22 -05:00
William Falcon	5f6a2c6d65	Deployed `49c7d54` with MkDocs version: 1.0.4	2019-10-05 16:18:33 -05:00
William Falcon	09017dfd67	Deployed `0eb6950` with MkDocs version: 1.0.4	2019-10-05 16:10:29 -05:00
William Falcon	c5d8042012	Deployed `222d7d2` with MkDocs version: 1.0.4	2019-10-02 09:30:25 -05:00
William Falcon	736c747c21	Deployed `133d6b3` with MkDocs version: 1.0.4	2019-10-01 05:38:20 -05:00
William Falcon	3c34e029ed	Deployed `fbc2cfd` with MkDocs version: 1.0.4	2019-10-01 05:34:27 -05:00
William Falcon	e77709ff8b	Deployed `1d7ffd1` with MkDocs version: 1.0.4	2019-09-26 16:54:08 -05:00
William Falcon	6d7eb70130	Deployed `059b2fa` with MkDocs version: 1.0.4	2019-09-26 14:58:07 -05:00
William Falcon	452c972d07	Deployed `acb4ebe` with MkDocs version: 1.0.4	2019-09-26 11:02:20 -05:00
William Falcon	a5cc9ec2dc	Deployed `5a9320d` with MkDocs version: 1.0.4	2019-09-26 10:28:41 -05:00
William Falcon	8f53e506cf	Deployed `2a1bc22` with MkDocs version: 1.0.4	2019-09-17 08:57:25 -05:00
William Falcon	5f229145c7	Deployed `d3afc8a` with MkDocs version: 1.0.4	2019-09-17 08:53:42 -05:00
William Falcon	ab35762fa1	Deployed `4c61d1f` with MkDocs version: 1.0.4	2019-09-16 10:07:42 -05:00
William Falcon	167e9f7aa0	Deployed `4c61d1f` with MkDocs version: 1.0.4	2019-09-16 10:07:22 -05:00
William Falcon	06abe3f1b9	Deployed `e1adbe8` with MkDocs version: 1.0.4	2019-09-16 10:04:47 -05:00
William Falcon	ba18f5f26a	Deployed `286625a` with MkDocs version: 1.0.4	2019-09-16 10:02:11 -05:00
William Falcon	9696342652	Deployed `b354988` with MkDocs version: 1.0.4	2019-09-16 09:59:41 -05:00
William Falcon	2a8ef263a7	Deployed `974afba` with MkDocs version: 1.0.4	2019-09-16 09:55:01 -05:00
William Falcon	f70f01b8a8	Deployed 5d49f1f with MkDocs version: 1.0.4	2019-08-31 02:20:46 -05:00
William Falcon	6104ba74a9	Deployed 622a773 with MkDocs version: 1.0.4	2019-08-31 02:19:30 -05:00
William Falcon	65a4a54205	Deployed `f51b459` with MkDocs version: 1.0.4	2019-08-31 02:03:39 -05:00
William Falcon	55692eb3e3	Deployed `c24599f` with MkDocs version: 1.0.4	2019-08-24 07:14:41 -05:00
William Falcon	56cd3dd176	Deployed `a7a14da` with MkDocs version: 1.0.4	2019-08-18 17:25:20 -05:00
William Falcon	971d59efe8	Deployed `1b7d66d` with MkDocs version: 1.0.4	2019-08-16 16:14:51 -05:00
William Falcon	3e511d129c	Deployed `bdd8608` with MkDocs version: 1.0.4	2019-08-16 09:08:04 -05:00
William Falcon	61dd3eb069	Deployed `bc401d0` with MkDocs version: 1.0.4	2019-08-16 09:02:39 -05:00
William Falcon	a485420572	Deployed `4b97319` with MkDocs version: 1.0.4	2019-08-15 20:29:41 -05:00
William Falcon	9eae41f903	Deployed `0e92a9d` with MkDocs version: 1.0.4	2019-08-15 20:19:40 -05:00
William Falcon	8d6f8d8146	Deployed `8183722` with MkDocs version: 1.0.4	2019-08-15 13:00:00 -05:00
William Falcon	9b8a06bfdb	Deployed `f0af138` with MkDocs version: 1.0.4	2019-08-13 15:21:24 -05:00
William Falcon	d32c548cf8	Deployed `3dea127` with MkDocs version: 1.0.4	2019-08-13 12:05:55 -05:00
William Falcon	bf44c4838d	Deployed `d4b1ac9` with MkDocs version: 1.0.4	2019-08-13 12:03:44 -05:00
William Falcon	5856805d27	Deployed `087be2f` with MkDocs version: 1.0.4	2019-08-13 12:02:33 -05:00
William Falcon	25bf79b9da	Deployed `699fbab` with MkDocs version: 1.0.4	2019-08-13 10:59:40 -05:00
William Falcon	6412446781	Deployed `fd845d4` with MkDocs version: 1.0.4	2019-08-13 10:57:09 -05:00
William Falcon	e4a503a016	Deployed `d7660d3` with MkDocs version: 1.0.4	2019-08-13 10:55:16 -05:00
William Falcon	5a6d112ed6	Deployed `7e38f1f` with MkDocs version: 1.0.4	2019-08-13 10:54:24 -05:00
William Falcon	cc924cd8eb	Deployed `7898d0c` with MkDocs version: 1.0.4	2019-08-13 10:51:37 -05:00
William Falcon	ef857d6f84	Deployed `53ec3bc` with MkDocs version: 1.0.4	2019-08-13 10:47:58 -05:00
William Falcon	589e5381be	Deployed `6f3152b` with MkDocs version: 1.0.4	2019-08-13 05:43:30 -05:00
William Falcon	2b89c3513b	Deployed `190a3a9` with MkDocs version: 1.0.4	2019-08-13 05:39:53 -05:00
William Falcon	71d2fcb7cf	Deployed `a78ee48` with MkDocs version: 1.0.4	2019-08-12 15:13:26 -05:00
William Falcon	c2ee2951c6	Deployed `996b1f9` with MkDocs version: 1.0.4	2019-08-10 16:04:24 -05:00
William Falcon	0bbd238d2d	Deployed `09d4475` with MkDocs version: 1.0.4	2019-08-09 14:04:27 -05:00
William Falcon	caeca5da25	Deployed `09d4475` with MkDocs version: 1.0.4	2019-08-09 14:03:22 -05:00
William Falcon	d43f670f3c	Deployed `2425ef0` with MkDocs version: 1.0.4	2019-08-07 15:02:23 -05:00
William Falcon	6ccd5b10ac	Deployed `b198435` with MkDocs version: 1.0.4	2019-08-07 13:16:49 -05:00
William Falcon	bdda196694	Deployed `cca6d2c` with MkDocs version: 1.0.4	2019-08-07 13:14:31 -05:00
William Falcon	9b7fab5324	Deployed `73b50ab` with MkDocs version: 1.0.4	2019-08-07 12:23:56 -05:00
William Falcon	3d1af47cb7	Deployed `1514810` with MkDocs version: 1.0.4	2019-08-07 12:21:34 -05:00
William Falcon	6f0d078ce3	Deployed `48bc346` with MkDocs version: 1.0.4	2019-08-07 12:15:50 -05:00
William Falcon	4e9d3fc53b	Deployed `b4c14a4` with MkDocs version: 1.0.4	2019-08-07 08:03:37 -05:00
William Falcon	343e3df1c0	Deployed `5f4e5c0` with MkDocs version: 1.0.4	2019-08-05 13:37:37 -05:00
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				!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r,m,i;e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=(r=e.stemmerSupport.Among,m=e.stemmerSupport.SnowballProgram,i=new function(){var i,t,n,s=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],o=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],a=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],u=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],c=new m;function l(){var e,r=c.limit-c.cursor;c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.find_among_b(o,4)?(c.bra=c.cursor,c.limit_backward=e,c.cursor=c.limit-r,c.cursor>c.limit_backward&&(c.cursor--,c.bra=c.cursor,c.slice_del())):c.limit_backward=e)}this.setCurrent=function(e){c.setCurrent(e)},this.getCurrent=function(){return c.getCurrent()},this.stem=function(){var e,r=c.cursor;return function(){var e,r=c.cursor+3;if(t=c.limit,0<=r&&r<=c.limit){for(i=r;;){if(e=c.cursor,c.in_grouping(d,97,248)){c.cursor=e;break}if((c.cursor=e)>=c.limit)return;c.cursor++}for(;!c.out_grouping(d,97,248);){if(c.cursor>=c.limit)return;c.cursor++}(t=c.cursor)<i&&(t=i)}}(),c.limit_backward=r,c.cursor=c.limit,function(){var e,r;if(c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(s,32),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del();break;case 2:c.in_grouping_b(u,97,229)&&c.slice_del()}}(),c.cursor=c.limit,l(),c.cursor=c.limit,function(){var e,r,i,n=c.limit-c.cursor;if(c.ket=c.cursor,c.eq_s_b(2,"st")&&(c.bra=c.cursor,c.eq_s_b(2,"ig")&&c.slice_del()),c.cursor=c.limit-n,c.cursor>=t&&(r=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,e=c.find_among_b(a,5),c.limit_backward=r,e))switch(c.bra=c.cursor,e){case 1:c.slice_del(),i=c.limit-c.cursor,l(),c.cursor=c.limit-i;break;case 2:c.slice_from("løs")}}(),c.cursor=c.limit,c.cursor>=t&&(e=c.limit_backward,c.limit_backward=t,c.ket=c.cursor,c.out_grouping_b(d,97,248)?(c.bra=c.cursor,n=c.slice_to(n),c.limit_backward=e,c.eq_v_b(n)&&c.slice_del()):c.limit_backward=e),!0}},function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});
				`@@ -0,0 +1 @@`
				!function(e,i){"function"==typeof define&&define.amd?define(i):"object"==typeof exports?module.exports=i():i()(e.lunr)}(this,function(){return function(o){o.multiLanguage=function(){for(var e=Array.prototype.slice.call(arguments),i=e.join("-"),t="",r=[],n=[],s=0;s<e.length;++s)"en"==e[s]?(t+="\\w",r.unshift(o.stopWordFilter),r.push(o.stemmer),n.push(o.stemmer)):(t+=o[e[s]].wordCharacters,r.unshift(o[e[s]].stopWordFilter),r.push(o[e[s]].stemmer),n.push(o[e[s]].stemmer));var p=o.trimmerSupport.generateTrimmer(t);return o.Pipeline.registerFunction(p,"lunr-multi-trimmer-"+i),r.unshift(p),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,r),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,n))}}}});
				`@@ -0,0 +1 @@`
				!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r\|\|!t&&0!=t\|\|!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var b;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(r){b=r,this.cursor=0,this.limit=r.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var r=b;return b=null,r},in_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor++,!0}return!1},in_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(s<=i&&t<=s&&r[(s-=t)>>3]&1<<(7&s))return this.cursor--,!0}return!1},out_grouping:function(r,t,i){if(this.cursor<this.limit){var s=b.charCodeAt(this.cursor);if(i<s\|\|s<t)return this.cursor++,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor++,!0}return!1},out_grouping_b:function(r,t,i){if(this.cursor>this.limit_backward){var s=b.charCodeAt(this.cursor-1);if(i<s\|\|s<t)return this.cursor--,!0;if(!(r[(s-=t)>>3]&1<<(7&s)))return this.cursor--,!0}return!1},eq_s:function(r,t){if(this.limit-this.cursor<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor+i)!=t.charCodeAt(i))return!1;return this.cursor+=r,!0},eq_s_b:function(r,t){if(this.cursor-this.limit_backward<r)return!1;for(var i=0;i<r;i++)if(b.charCodeAt(this.cursor-r+i)!=t.charCodeAt(i))return!1;return this.cursor-=r,!0},find_among:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=r[c],_=f;_<l.s_size;_++){if(e+f==n){a=-1;break}if(a=b.charCodeAt(e+f)-l.s[_])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i\|\|s==i\|\|h)break;h=!0}}for(;;){if(u>=(l=r[i]).s_size){if(this.cursor=e+l.s_size,!l.method)return l.result;var m=l.method();if(this.cursor=e+l.s_size,m)return l.result}if((i=l.substring_i)<0)return 0}},find_among_b:function(r,t){for(var i=0,s=t,e=this.cursor,n=this.limit_backward,u=0,o=0,h=!1;;){for(var c=i+(s-i>>1),a=0,f=u<o?u:o,l=(_=r[c]).s_size-1-f;0<=l;l--){if(e-f==n){a=-1;break}if(a=b.charCodeAt(e-1-f)-_.s[l])break;f++}if(a<0?(s=c,o=f):(i=c,u=f),s-i<=1){if(0<i\|\|s==i\|\|h)break;h=!0}}for(;;){var _;if(u>=(_=r[i]).s_size){if(this.cursor=e-_.s_size,!_.method)return _.result;var m=_.method();if(this.cursor=e-_.s_size,m)return _.result}if((i=_.substring_i)<0)return 0}},replace_s:function(r,t,i){var s=i.length-(t-r);return b=b.substring(0,r)+i+b.substring(t),this.limit+=s,this.cursor>=t?this.cursor+=s:this.cursor>r&&(this.cursor=r),s},slice_check:function(){if(this.bra<0\|\|this.bra>this.ket\|\|this.ket>this.limit\|\|this.limit>b.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),b.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});