coverage increase (#1167)

* fixed docs * Docs (#1164) * fixed docs * fixed docs * fixed docs * fixing Win failed import (#1163) * version * try fix distrib * update try import * fixed docs Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Lightning-AI · Mar 17, 2020 · 8de7b40 · 8de7b40
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -24,11 +24,11 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
--
+- Removed duplicated module `pytorch_lightning.utilities.arg_parse` for loading CLI arguments ([#1167](https://github.com/PyTorchLightning/pytorch-lightning/issues/1167))
 
 ### Fixed
 
-- Fixed bug related to type cheking of `ReduceLROnPlateau` lr schedulers([#1114](https://github.com/PyTorchLightning/pytorch-lightning/issues/1114))
+- Fixed bug related to type cheking of `ReduceLROnPlateau` lr schedulers ([#1114](https://github.com/PyTorchLightning/pytorch-lightning/issues/1114))
 
 ## [0.7.1] - 2020-03-07
 

diff --git a/pytorch_lightning/utilities/arg_parse.py b/pytorch_lightning/utilities/arg_parse.py
diff --git a/pytorch_lightning/utilities/debugging.py b/pytorch_lightning/utilities/debugging.py
@@ -1,78 +1,2 @@
-"""
-These flags are useful to help debug a model.
-
-Fast dev run
-------------
-
-This flag is meant for debugging a full train/val/test loop.
- It'll activate callbacks, everything but only with 1 training and 1 validation batch.
- Use this to debug a full run of your program quickly
-
-.. code-block:: python
-
-    # DEFAULT
-    trainer = Trainer(fast_dev_run=False)
-
-
-Inspect gradient norms
-----------------------
-
-Looking at grad norms can help you figure out where training might be going wrong.
-
-.. code-block:: python
-
-    # DEFAULT (-1 doesn't track norms)
-    trainer = Trainer(track_grad_norm=-1)
-
-    # track the LP norm (P=2 here)
-    trainer = Trainer(track_grad_norm=2)
-
-
-Make model overfit on subset of data
-------------------------------------
-
-A useful debugging trick is to make your model overfit a tiny fraction of the data.
-
-setting `overfit_pct > 0` will overwrite train_percent_check, val_percent_check, test_percent_check
-
-.. code-block:: python
-
-    # DEFAULT don't overfit (ie: normal training)
-    trainer = Trainer(overfit_pct=0.0)
-
-    # overfit on 1% of data
-    trainer = Trainer(overfit_pct=0.01)
-
-
-Print the parameter count by layer
-----------------------------------
-
-By default lightning prints a list of parameters *and submodules* when it starts training.
-
-.. code-block:: python
-
-    # DEFAULT print a full list of all submodules and their parameters.
-    trainer = Trainer(weights_summary='full')
-
-    # only print the top-level modules (i.e. the children of LightningModule).
-    trainer = Trainer(weights_summary='top')
-
-Print which gradients are nan
------------------------------
-
-This option prints a list of tensors with nan gradients::
-
-    # DEFAULT
-    trainer = Trainer(print_nan_grads=False)
-
-Log GPU usage
--------------
-
-Lightning automatically logs gpu usage to the test tube logs.
- It'll only do it at the metric logging interval, so it doesn't slow down training.
-
-"""
-
-
 class MisconfigurationException(Exception):
     pass