added trainer docs

docs/Pytorch-Lightning/Trainer.md

@@ -1 +0,0 @@
-# Trainer

docs/Trainer/index.md

@@ -0,0 +1,95 @@
+# Trainer
+[[Github Code](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/models/trainer.py)]
+
+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.
+
+This is the basic use of the trainer:
+
+``` {.python}
+from pytorch_lightning import Trainer
+
+model = LightningTemplate()
+
+trainer = Trainer()
+trainer.fit(model)
+```
+
+But of course the fun is in all the advanced things it can do:
+
+``` {.python}
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from test_tube import Experiment, SlurmCluster
+
+trainer = Trainer(
+                 experiment=Experiment,
+                 checkpoint_callback=ModelCheckpoint, 
+                 early_stop_callback=EarlyStopping,
+                 cluster=SlurmCluster,
+                 process_position=0,
+                 current_gpu_name=0,
+                 gpus=None,
+                 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):
+)
+```
+
+
+Things you can do with the trainer module:
+
+**Training loop**    
+
+- Accumulate gradients
+- Check GPU usage
+- Check which gradients are nan
+- Check validation every n epochs
+- Display metrics in progress bar
+- Force training for min or max epochs
+- Inspect gradient norms
+- Learning rate annealing
+- Make model overfit on subset of data
+- Multiple optimizers (like GANs)
+- Set how much of the training set to check (1-100%)
+- Show progress bar
+- training_step function
+
+**Validation loop**    
+
+- Display metrics in progress bar
+- Set how much of the validation set to check (1-100%)
+- Set validation check frequency within 1 training epoch (1-100%)
+- validation_step function
+- Why does validation run first for 5 steps?
+
+**Distributed training**    
+
+- Single-gpu      
+- Multi-gpu      
+- Multi-node   
+- 16-bit mixed precision
+
+**Checkpointing**    
+
+- Model saving
+- Model loading 
+
+**Computing cluster (SLURM)**    
+
+- Automatic checkpointing   
+- Automatic saving, loading  
+- Running grid search on a cluster 
+- Walltime auto-resubmit   

docs/source/examples/basic_trainer.py

@@ -4,7 +4,7 @@ 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 pytorch_lightning.callbacks.pt_callbacks import EarlyStopping, ModelCheckpoint
 from docs.source.examples.example_model import ExampleModel
 
 

docs/source/examples/fully_featured_trainer.py

@@ -8,7 +8,7 @@ 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
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 
 SEED = 2334
 torch.manual_seed(SEED)