updated test docs

tests/debug.py

@@ -0,0 +1,66 @@
+import pytest
+from pytorch_lightning import Trainer
+from pytorch_lightning.examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from argparse import Namespace
+from test_tube import Experiment
+import numpy as np
+import warnings
+import torch
+import os
+import shutil
+
+def get_model():
+    # set up model with these hyperparams
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    hparams = Namespace(**{'drop_prob': 0.2,
+                           'batch_size': 32,
+                           'in_features': 28*28,
+                           'learning_rate': 0.001*8,
+                           'optimizer_name': 'adam',
+                           'data_root': os.path.join(root_dir, 'mnist'),
+                           'out_features': 10,
+                           'hidden_dim': 1000})
+    model = LightningTemplateModel(hparams)
+
+    return model
+
+
+def get_exp():
+    # set up exp object without actually saving logs
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    exp = Experiment(debug=True, save_dir=root_dir, name='tests_tt_dir')
+    return exp
+
+
+def clear_tt_dir():
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    tt_dir = os.path.join(root_dir, 'tests_tt_dir')
+    if os.path.exists(tt_dir):
+        shutil.rmtree(tt_dir)
+
+
+def main():
+
+    clear_tt_dir()
+    model = get_model()
+
+    trainer = Trainer(
+        progress_bar=False,
+        experiment=get_exp(),
+        max_nb_epochs=1,
+        train_percent_check=0.4,
+        val_percent_check=0.4,
+        gpus=[0, 1],
+        distributed_backend='ddp',
+        use_amp=True
+    )
+
+    result = trainer.fit(model)
+
+    # correct result and ok accuracy
+    assert result == 1, 'amp + ddp model failed to complete'
+
+    clear_tt_dir()
+
+if __name__ == '__main__':
+    main()