[Feat] Adding PruningCallback

CHANGELOG.md

@@ -59,6 +59,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - `Recall` and `Precision` metrics (and their functional counterparts `recall` and `precision`) can now be generalized to Recall@K and Precision@K with the use of `top_k` parameter ([#4842](https://github.com/PyTorchLightning/pytorch-lightning/pull/4842))
 
 
+- Added `ModelPruning` Callback ([#5618](https://github.com/PyTorchLightning/pytorch-lightning/pull/5618))
+
+
 - Added `PyTorchProfiler` ([#5560](https://github.com/PyTorchLightning/pytorch-lightning/pull/5560))
 
 

docs/source/callbacks.rst

@@ -103,6 +103,7 @@ Lightning has a few built-in callbacks.
     LambdaCallback
     LearningRateMonitor
     ModelCheckpoint
+    ModelPruning
     ProgressBar
     ProgressBarBase
 

pytorch_lightning/callbacks/__init__.py

@@ -20,6 +20,7 @@
 from pytorch_lightning.callbacks.lr_monitor import LearningRateMonitor
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
 from pytorch_lightning.callbacks.progress import ProgressBar, ProgressBarBase
+from pytorch_lightning.callbacks.pruning import ModelPruning
 
 __all__ = [
     'BackboneLambdaFinetuningCallback',
@@ -33,4 +34,5 @@
     'ModelCheckpoint',
     'ProgressBar',
     'ProgressBarBase',
+    'ModelPruning',
 ]