release v0.5.1.2

README.md

@@ -17,8 +17,6 @@
 [![ReadTheDocs](https://readthedocs.org/projects/pytorch-lightning/badge/?version=latest)](https://pytorch-lightning.readthedocs.io/en/latest)
 [![Gitter](https://badges.gitter.im/PyTorch-Lightning/community.svg)](https://gitter.im/PyTorch-Lightning/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
 [![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/williamFalcon/pytorch-lightning/blob/master/LICENSE)
-[![Next Release](https://img.shields.io/badge/Next%20Release-Nov%206-<COLOR>.svg)](https://shields.io/)
-
 <!-- 
 removed until codecov badge isn't empy. likely a config error showing nothing on master.
 [![codecov](https://codecov.io/gh/Borda/pytorch-lightning/branch/master/graph/badge.svg)](https://codecov.io/gh/Borda/pytorch-lightning)

pytorch_lightning/testing/lm_test_module_base.py

@@ -104,8 +104,7 @@ class LightningTestModelBase(LightningModule):
         if self.trainer.batch_nb % 1 == 0:
             output = OrderedDict({
                 'loss': loss_val,
-                'progress_bar': {'some_val': loss_val * loss_val},
-                'log': {'train_some_val': loss_val * loss_val},
+                'progress_bar': {'some_val': loss_val * loss_val}
             })
 
             return output

pytorch_lightning/testing/lm_test_module_mixins.py

@@ -105,7 +105,7 @@ class LightningValidationMixin(LightningValidationStepMixin):
         val_acc_mean /= len(outputs)
 
         tqdm_dict = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
-        results = {'progress_bar': tqdm_dict, 'log': tqdm_dict}
+        results = {'progress_bar': tqdm_dict}
         return results
 
 

pytorch_lightning/trainer/trainer.py

@@ -183,7 +183,6 @@ class Trainer(TrainerIO):
                 version=self.slurm_job_id,
                 name='lightning_logs'
             )
-        self.logger.rank = 0
 
         # configure checkpoint callback
         self.checkpoint_callback = checkpoint_callback
@@ -1160,14 +1159,12 @@ class Trainer(TrainerIO):
     def __metrics_to_scalars(self, metrics):
         new_metrics = {}
         for k, v in metrics.items():
-            if isinstance(v, torch.Tensor):
+            if type(v) is torch.Tensor:
                 v = v.item()
 
             if type(v) is dict:
                 v = self.__metrics_to_scalars(v)
 
-            new_metrics[k] = v
-
         return new_metrics
 
     def __log_vals_blacklist(self):
@@ -1338,6 +1335,7 @@ class Trainer(TrainerIO):
 
                 # track progress bar metrics
                 self.__add_tqdm_metrics(progress_bar_metrics)
+
                 all_log_metrics.append(log_metrics)
 
                 # accumulate loss
@@ -1404,6 +1402,7 @@ class Trainer(TrainerIO):
 
         # collapse all metrics into one dict
         all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()}
+
         return 0, grad_norm_dic, all_log_metrics
 
     def __run_evaluation(self, test=False):
@@ -1444,6 +1443,7 @@ class Trainer(TrainerIO):
                                          dataloaders,
                                          max_batches,
                                          test)
+
             _, progress_bar_metrics, log_metrics = self.__process_output(eval_results)
 
             # add metrics to prog bar

pytorch_lightning/trainer/trainer_io.py

@@ -5,7 +5,7 @@ import pdb
 from subprocess import call
 
 import torch
-import torch.distributed as dist
+
 from pytorch_lightning.pt_overrides.override_data_parallel import (
     LightningDistributedDataParallel, LightningDataParallel)
 
@@ -35,11 +35,6 @@ class TrainerIO(object):
         # if script called from hpc resubmit, load weights
         self.restore_hpc_weights_if_needed(model)
 
-        # wait for all models to restore weights
-        if self.use_ddp or self.use_ddp2:
-            # wait for all processes to catch up
-            dist.barrier()
-
     def restore_state_if_checkpoint_exists(self, model):
         # do nothing if there's not dir or callback
         no_ckpt_callback = self.checkpoint_callback is None

setup.py

@@ -14,7 +14,7 @@ from setuptools import setup, find_packages
 # engineer specific practices
 setup(
     name='pytorch-lightning',
-    version='0.5.1.3',
+    version='0.5.1.2',
     description='The Keras for ML researchers using PyTorch',
     author='William Falcon',
     author_email='waf2107@columbia.edu',

tests/debug.py

@@ -14,8 +14,7 @@ from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
 import numpy as np
 import pdb
-# from test_models import assert_ok_test_acc, load_model, \
-#     clear_save_dir, get_test_tube_logger, get_hparams, init_save_dir
+from . import test_models
 
 
 class CoolModel(pl.LightningModule):
@@ -59,55 +58,57 @@ class CoolModel(pl.LightningModule):
     @pl.data_loader
     def test_dataloader(self):
         return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
-#
-#
-# def main():
-#     """
-#     Make sure DDP + AMP continue training correctly
-#     :return:
-#     """
-#     """
-#     Make sure DDP2 works
-#     :return:
-#     """
-#     hparams = get_hparams()
-#     model = LightningTestModel(hparams)
-#
-#     save_dir = init_save_dir()
-#
-#     # logger file to get meta
-#     logger = get_test_tube_logger(False)
-#     logger.log_hyperparams(hparams)
-#     logger.save()
-#
-#     # logger file to get weights
-#     checkpoint = ModelCheckpoint(save_dir)
-#
-#     trainer_options = dict(
-#         show_progress_bar=True,
-#         max_nb_epochs=1,
-#         train_percent_check=0.4,
-#         val_percent_check=0.2,
-#         checkpoint_callback=checkpoint,
-#         logger=logger,
-#         gpus=[0, 1],
-#         distributed_backend='dp'
-#     )
-#
-#     # fit model
-#     trainer = Trainer(**trainer_options)
-#     result = trainer.fit(model)
-#
-#     # correct result and ok accuracy
-#     assert result == 1, 'training failed to complete'
-#     pretrained_model = load_model(logger.experiment, save_dir, module_class=LightningTestModel)
-#
-#     new_trainer = Trainer(**trainer_options)
-#     new_trainer.test(pretrained_model)
-#
-#     # test we have good test accuracy
-#     assert_ok_test_acc(new_trainer)
-#     clear_save_dir()
 
-# if __name__ == '__main__':
-#     main()
+
+def main():
+    """
+    Make sure DDP + AMP continue training correctly
+    :return:
+    """
+    """
+    Make sure DDP2 works
+    :return:
+    """
+    hparams = test_models.get_hparams()
+    model = LightningTestModel(hparams)
+
+    save_dir = test_models.init_save_dir()
+
+    # logger file to get meta
+    logger = test_models.get_test_tube_logger(False)
+    logger.log_hyperparams(hparams)
+    logger.save()
+
+    # logger file to get weights
+    checkpoint = ModelCheckpoint(save_dir)
+
+    trainer_options = dict(
+        show_progress_bar=True,
+        max_nb_epochs=1,
+        train_percent_check=0.4,
+        val_percent_check=0.2,
+        checkpoint_callback=checkpoint,
+        logger=logger,
+        gpus=[0, 1],
+        distributed_backend='dp'
+    )
+
+    # fit model
+    trainer = Trainer(**trainer_options)
+    result = trainer.fit(model)
+
+    # correct result and ok accuracy
+    assert result == 1, 'training failed to complete'
+    pretrained_model = test_models.load_model(logger.experiment, save_dir,
+                                              module_class=LightningTestModel)
+
+    new_trainer = Trainer(**trainer_options)
+    new_trainer.test(pretrained_model)
+
+    # test we have good test accuracy
+    test_models.assert_ok_test_acc(new_trainer)
+    test_models.clear_save_dir()
+
+
+if __name__ == '__main__':
+    main()