Deprecate LightningLoggerBase.close

CHANGELOG.md

@@ -218,6 +218,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Deprecated passing `flush_logs_every_n_steps` as a Trainer argument, instead pass it to the logger init if supported ([#9366](https://github.com/PyTorchLightning/pytorch-lightning/pull/9366))
 
 
+- Deprecated `LightningLoggerBase.close`, `LoggerCollection.close` in favor of `LightningLoggerBase.finalize`, `LoggerCollection.finalize` ([#9422](https://github.com/PyTorchLightning/pytorch-lightning/pull/9422))
+
+
 
 ### Removed
 

pytorch_lightning/loggers/base.py

@@ -28,6 +28,7 @@
 import pytorch_lightning as pl
 from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
 from pytorch_lightning.utilities import rank_zero_only
+from pytorch_lightning.utilities.warnings import rank_zero_deprecation
 
 
 def rank_zero_experiment(fn: Callable) -> Callable:
@@ -310,7 +311,18 @@ def finalize(self, status: str) -> None:
         self.save()
 
     def close(self) -> None:
-        """Do any cleanup that is necessary to close an experiment."""
+        """Do any cleanup that is necessary to close an experiment.
+
+        See deprecation warning below.
+
+        .. deprecated:: v1.5
+            This method is deprecated in v1.5 and will be removed in v1.7.
+            Please use `LightningLoggerBase.finalize` instead.
+        """
+        rank_zero_deprecation(
+            "`LightningLoggerBase.close` method is deprecated in v1.5 and will be removed in v1.7."
+            " Please use `LightningLoggerBase.finalize` instead."
+        )
         self.save()
 
     @property
@@ -392,6 +404,15 @@ def finalize(self, status: str) -> None:
             logger.finalize(status)
 
     def close(self) -> None:
+        """
+        .. deprecated:: v1.5
+            This method is deprecated in v1.5 and will be removed in v1.7.
+            Please use `LoggerCollection.finalize` instead.
+        """
+        rank_zero_deprecation(
+            "`LoggerCollection.close` method is deprecated in v1.5 and will be removed in v1.7."
+            " Please use `LoggerCollection.finalize` instead."
+        )
         for logger in self._logger_iterable:
             logger.close()
 

tests/deprecated_api/test_remove_1-7.py

@@ -18,11 +18,12 @@
 import torch
 
 from pytorch_lightning import Callback, LightningDataModule, Trainer
-from pytorch_lightning.loggers import TestTubeLogger
+from pytorch_lightning.loggers import LoggerCollection, TestTubeLogger
 from tests.deprecated_api import _soft_unimport_module
 from tests.helpers import BoringModel
 from tests.helpers.datamodules import MNISTDataModule
 from tests.helpers.runif import RunIf
+from tests.loggers.test_base import CustomLogger
 
 
 def test_v1_7_0_deprecated_lightning_module_summarize(tmpdir):
@@ -224,3 +225,16 @@ def test_v1_7_0_deprecate_add_get_queue(tmpdir):
 
     with pytest.deprecated_call(match=r"`LightningModule.get_from_queue` method was deprecated in v1.5"):
         trainer.fit(model)
+
+
+def test_v1_7_0_lightning_logger_base_close(tmpdir):
+    logger = CustomLogger()
+    with pytest.deprecated_call(
+        match="`LightningLoggerBase.close` method is deprecated in v1.5 and will be removed in v1.7."
+    ):
+        logger.close()
+    with pytest.deprecated_call(
+        match="`LoggerCollection.close` method is deprecated in v1.5 and will be removed in v1.7."
+    ):
+        logger = LoggerCollection([logger])
+        logger.close()

tests/loggers/test_base.py

@@ -48,9 +48,9 @@ def test_logger_collection():
     mock1.agg_and_log_metrics.assert_called_once_with({"test": 2.0}, 4)
     mock2.agg_and_log_metrics.assert_called_once_with({"test": 2.0}, 4)
 
-    logger.close()
-    mock1.close.assert_called_once()
-    mock2.close.assert_called_once()
+    logger.finalize("success")
+    mock1.finalize.assert_called_once()
+    mock2.finalize.assert_called_once()
 
 
 class CustomLogger(LightningLoggerBase):
@@ -208,7 +208,7 @@ def log_metrics(self, metrics, step):
         logger.agg_and_log_metrics({"loss": loss}, step=int(i / 5))
 
     assert logger.history == {0: {"loss": 0.5623850983416314}}
-    logger.close()
+    logger.save()
     assert logger.history == {0: {"loss": 0.5623850983416314}, 1: {"loss": 0.4778883735637184}}