RC & Docs/changelog (#1776)

* missing

* RC

* tol

* Apply suggestions from code review

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* test

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

CHANGELOG.md

@@ -16,7 +16,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added auto scaling of batch size ([#1638](https://github.com/PyTorchLightning/pytorch-lightning/pull/1638))
 
-- The progress bar metrics now also get updated in `training_epoch_end` ([#1724](https://github.com/PyTorchLightning/pytorch-lightning/pull/1724)).
+- The progress bar metrics now also get updated in `training_epoch_end` ([#1724](https://github.com/PyTorchLightning/pytorch-lightning/pull/1724))
+
+- Enable `NeptuneLogger` to work with `distributed_backend=ddp` ([#1753](https://github.com/PyTorchLightning/pytorch-lightning/pull/1753))
 
 ### Changed
 
@@ -24,15 +26,25 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Updated LightningTemplateModel to look more like Colab example ([#1577](https://github.com/PyTorchLightning/pytorch-lightning/pull/1577))
 
+- Don't convert `namedtuple` to `tuple` when transferring the batch to target device ([#1589](https://github.com/PyTorchLightning/pytorch-lightning/pull/1589))
+
+- Allow passing hparams as keyword argument to LightningModule when loading from checkpoint ([#1639](https://github.com/PyTorchLightning/pytorch-lightning/pull/1639))
+
 ### Deprecated
 
 ### Removed
 
 ### Fixed
 
-- Fixed ModelCheckpoint not None checking filepath ([1654](https://github.com/PyTorchLightning/pytorch-lightning/pull/1654))
+- Fixed broken link in PR template ([#1675](https://github.com/PyTorchLightning/pytorch-lightning/pull/1675))
 
-- Trainer now calls `on_load_checkpoint()` when resuming from a checkpoint ([1666](https://github.com/PyTorchLightning/pytorch-lightning/pull/1666))
+- Fixed ModelCheckpoint not None checking filepath ([#1654](https://github.com/PyTorchLightning/pytorch-lightning/pull/1654))
+
+- Trainer now calls `on_load_checkpoint()` when resuming from a checkpoint ([#1666](https://github.com/PyTorchLightning/pytorch-lightning/pull/1666))
+
+- Fixed sampler logic for ddp with iterable dataset ([#1734](https://github.com/PyTorchLightning/pytorch-lightning/pull/1734))
+
+- Fixed `_reset_eval_dataloader()` for IterableDataset ([#1560](https://github.com/PyTorchLightning/pytorch-lightning/pull/1560))
 
 - Fixed Horovod distributed backend to set the `root_gpu` property ([#1669](https://github.com/PyTorchLightning/pytorch-lightning/pull/1669))
 

pytorch_lightning/__init__.py

@@ -1,6 +1,6 @@
 """Root package info."""
 
-__version__ = '0.7.5'
+__version__ = '0.7.6rc1'
 __author__ = 'William Falcon et al.'
 __author_email__ = 'waf2107@columbia.edu'
 __license__ = 'Apache-2.0'

tests/models/data/horovod/train_default_model.py

@@ -45,15 +45,16 @@ def run_test_from_config(trainer_options):
     ckpt_path = trainer_options['default_root_dir']
     trainer_options.update(checkpoint_callback=ModelCheckpoint(ckpt_path))
 
-    model = EvalModelTemplate(EvalModelTemplate.get_default_hparams())
+    model = EvalModelTemplate()
     run_model_test(trainer_options, model, on_gpu=args.on_gpu, version=0, with_hpc=False)
 
     # Horovod should be initialized following training. If not, this will raise an exception.
     assert hvd.size() == 2
 
     if args.on_gpu:
+        trainer = Trainer(gpus=1, distributed_backend='horovod', max_epochs=1)
         # Test the root_gpu property
-        assert Trainer(gpus=1, distributed_backend='horovod', max_epochs=1).root_gpu == hvd.local_rank()
+        assert trainer.root_gpu == hvd.local_rank()
 
 
 if __name__ == "__main__":

tests/test_profiler.py

@@ -7,7 +7,7 @@ import pytest
 
 from pytorch_lightning.profiler import AdvancedProfiler, SimpleProfiler
 
-PROFILER_OVERHEAD_MAX_TOLERANCE = 0.0001
+PROFILER_OVERHEAD_MAX_TOLERANCE = 0.0005
 
 
 def _get_python_cprofile_total_duration(profile):