added example and verified

README.md

@@ -53,51 +53,82 @@ To use lightning do 2 things:
 ```python
 # trainer.py
 
-from pytorch_lightning.models.trainer import Trainer   
+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 my_project import My_Model   
-from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
+from demo.example_model import ExampleModel
 
-# --------------
-# TEST TUBE INIT
-exp = Experiment(
-    name='my_exp',
-    debug=True,
-    save_dir='/some/path',
-    autosave=False,
-    description='my desc'
-)
 
-# --------------------
-# CALLBACKS
-early_stop = EarlyStopping(
-    monitor='val_loss',
-    patience=3,
-    verbose=True,
-    mode='min'
-)
+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
+    )
 
-model_save_path = 'PATH/TO/SAVE'
-checkpoint = ModelCheckpoint(
-    filepath=model_save_path,
-    save_function=None,
-    save_best_only=True,
-    verbose=True,
-    monitor='val_acc',
-    mode='min'
-)
+    exp.argparse(hparams)
+    exp.save()
 
-# configure trainer
-trainer = Trainer(
-    experiment=experiment,
-    cluster=cluster,
-    checkpoint_callback=checkpoint,
-    early_stop_callback=early_stop
-)  
+    # build model
+    print('loading model...')
+    model = ExampleModel(hparams)
+    print('model built')
 
-# init model and train
-model = My_Model()
-trainer.fit(model)
+    # 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,
+        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)
 ```
 
 #### Define the model