Split callbacks (#849)

* add .vscode in .gitignore

* Split callbacks in individual files + add a  property to Callback for easy trainer instance access

* formatting

* Add a conda env file for quick and easy env setup to develop on PL

* Adress comments

* add fix to kth_best_model

* add some typing to callbacks

* fix typo

* add autopep8 config to pyproject.toml

* format again

* format

* fix toml

* fix toml again

* consistent max line length in all config files

* remove conda env file

* Update pytorch_lightning/callbacks/early_stopping.py

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* Update pytorch_lightning/callbacks/model_checkpoint.py

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* docstring

* Update pytorch_lightning/callbacks/model_checkpoint.py

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* Update pytorch_lightning/callbacks/model_checkpoint.py

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>

* fix logic error

* format

* simplify if/else

* format

* fix linting issue in changelog

* edit changelog about new callback mechanism

* fix remaining formating issue on CHANGELOG

* remove lambda function because it's compatible with pickle (used during ddp)

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

pytorch_lightning/callbacks/base.py

@@ -0,0 +1,68 @@
+"""
+Callbacks
+=========
+
+Callbacks supported by Lightning
+"""
+
+import abc
+
+
+_NO_TRAINER_ERROR_MSG = ".set_trainer() should be called after the callback initialization"
+
+
+class Callback(abc.ABC):
+    """Abstract base class used to build new callbacks."""
+
+    def __init__(self):
+        self._trainer = None
+
+    @property
+    def trainer(self):
+        assert self._trainer is not None, _NO_TRAINER_ERROR_MSG
+        return self._trainer
+
+    def set_trainer(self, trainer):
+        """Make a link to the trainer, so different things like `trainer.current_epoch`,
+        `trainer.batch_idx`, `trainer.global_step` can be used."""
+        self._trainer = trainer
+
+    def on_epoch_begin(self):
+        """Called when the epoch begins."""
+        pass
+
+    def on_epoch_end(self):
+        """Called when the epoch ends."""
+        pass
+
+    def on_batch_begin(self):
+        """Called when the training batch begins."""
+        pass
+
+    def on_batch_end(self):
+        """Called when the training batch ends."""
+        pass
+
+    def on_train_begin(self):
+        """Called when the train begins."""
+        pass
+
+    def on_train_end(self):
+        """Called when the train ends."""
+        pass
+
+    def on_validation_begin(self):
+        """Called when the validation loop begins."""
+        pass
+
+    def on_validation_end(self):
+        """Called when the validation loop ends."""
+        pass
+
+    def on_test_begin(self):
+        """Called when the test begins."""
+        pass
+
+    def on_test_end(self):
+        """Called when the test ends."""
+        pass