Default argparser for Trainer

pytorch_lightning/trainer/trainer.py

@@ -2,6 +2,7 @@
 import sys
 import warnings
 import logging as log
+from argparse import ArgumentParser
 from typing import Union, Optional, List, Dict, Tuple, Iterable
 
 import torch
@@ -116,6 +117,7 @@ def __init__(
             profiler: Optional[BaseProfiler] = None,
             benchmark: bool = False,
             reload_dataloaders_every_epoch: bool = False,
+            **kwargs
     ):
         r"""
 
@@ -626,6 +628,7 @@ def on_train_end(self):
 
         # Transfer params
         # Backward compatibility
+        self.num_nodes = num_nodes
         if nb_gpu_nodes is not None:
             warnings.warn("`nb_gpu_nodes` has renamed to `num_nodes` since v0.5.0"
                           " and this method will be removed in v0.8.0", DeprecationWarning)
@@ -746,10 +749,12 @@ def on_train_end(self):
         self.weights_save_path = weights_save_path
 
         # accumulated grads
+        self.accumulate_grad_batches = accumulate_grad_batches
         self.configure_accumulated_gradients(accumulate_grad_batches)
 
         # allow int, string and gpu list
-        self.data_parallel_device_ids = parse_gpu_ids(gpus)
+        self.gpus = gpus
+        self.data_parallel_device_ids = parse_gpu_ids(self.gpus)
         self.root_gpu = determine_root_gpu_device(self.data_parallel_device_ids)
 
         # tpu state flags
@@ -796,6 +801,7 @@ def on_train_end(self):
         self.row_log_interval = row_log_interval
 
         # how much of the data to use
+        self.overfit_pct = overfit_pct
         self.determine_data_use_amount(train_percent_check, val_percent_check,
                                        test_percent_check, overfit_pct)
 
@@ -818,6 +824,29 @@ def slurm_job_id(self) -> int:
             job_id = None
         return job_id
 
+    @property
+    @classmethod
+    def default_attributes(cls):
+        return vars(cls())
+
+    @classmethod
+    def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:
+
+        parser = ArgumentParser(parents=[parent_parser])
+
+        trainer_default_params = Trainer.default_attributes
+
+        for arg in trainer_default_args:
+            parser.add_argument('--{0}'.format(arg), default=trainer_default_params[arg], dest=arg)
+
+        return parser
+
+    @classmethod
+    def from_argparse_args(cls, args) -> Trainer:
+
+        params = vars(args)
+        return cls(**params)
+
     def __parse_gpu_ids(self, gpus):
         """Parse GPUs id.
 

pytorch_lightning/trainer/training_tricks.py

@@ -30,8 +30,6 @@ def print_nan_gradients(self):
                 log.info(param, param.grad)
 
     def configure_accumulated_gradients(self, accumulate_grad_batches):
-        self.accumulate_grad_batches = None
-
         if isinstance(accumulate_grad_batches, dict):
             self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)
         elif isinstance(accumulate_grad_batches, int):

tests/test_trainer.py

@@ -1,10 +1,11 @@
 import math
 import os
-
 import pytest
 import torch
+import argparse
 
 import tests.models.utils as tutils
+from unittest import mock
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import (
     EarlyStopping,
@@ -856,6 +857,26 @@ def test_end(self, outputs):
     Trainer().test(model)
 
 
+@mock.patch('argparse.ArgumentParser.parse_args',
+            return_value=argparse.Namespace(**(Trainer.get_default_args())))
+def test_default_args(tmpdir):
+    """Tests default argument parser for Trainer"""
+    tutils.reset_seed()
+
+    # logger file to get meta
+    logger = tutils.get_test_tube_logger(tmpdir, False)
+
+    parser = argparse.ArgumentParser(add_help=False)
+    args = parser.parse_args()
+    args.logger = logger
+
+    args.max_epochs = 5
+    trainer = Trainer.from_argparse_args(args)
+
+    assert isinstance(trainer, Trainer)
+    assert trainer.max_epochs == 5
+
+
 def test_trainer_callback_system(tmpdir):
     """Test the callback system."""