[App] Rename to new convention (#15621)

* update

* update

examples/app_multi_node/train_lt.py

@@ -1,9 +1,9 @@
 import lightning as L
-from lightning.app.components import PyTorchLightningMultiNode
+from lightning.app.components import LightningTrainerMultiNode
 from lightning.pytorch.demos.boring_classes import BoringModel
 
 
-class PyTorchLightningDistributed(L.LightningWork):
+class LightningTrainerDistributed(L.LightningWork):
     @staticmethod
     def run():
         model = BoringModel()
@@ -16,8 +16,8 @@ class PyTorchLightningDistributed(L.LightningWork):
 
 # Run over 2 nodes of 4 x V100
 app = L.LightningApp(
-    PyTorchLightningMultiNode(
-        PyTorchLightningDistributed,
+    LightningTrainerMultiNode(
+        LightningTrainerDistributed,
         num_nodes=2,
         cloud_compute=L.CloudCompute("gpu-fast-multi"),  # 4 x V100
     )

pyproject.toml

@@ -62,7 +62,7 @@ module = [
     "lightning_app.components.multi_node.lite",
     "lightning_app.components.multi_node.base",
     "lightning_app.components.multi_node.pytorch_spawn",
-    "lightning_app.components.multi_node.pl",
+    "lightning_app.components.multi_node.trainer",
     "lightning_app.api.http_methods",
     "lightning_app.api.request_types",
     "lightning_app.cli.commands.app_commands",

src/lightning_app/components/__init__.py

@@ -1,9 +1,9 @@
 from lightning_app.components.database.client import DatabaseClient
 from lightning_app.components.database.server import Database
 from lightning_app.components.multi_node import (
+    LightningTrainerMultiNode,
     LiteMultiNode,
     MultiNode,
-    PyTorchLightningMultiNode,
     PyTorchSpawnMultiNode,
 )
 from lightning_app.components.python.popen import PopenPythonScript
@@ -29,5 +29,5 @@ __all__ = [
     "LightningTrainingComponent",
     "PyTorchLightningScriptRunner",
     "PyTorchSpawnMultiNode",
-    "PyTorchLightningMultiNode",
+    "LightningTrainerMultiNode",
 ]

src/lightning_app/components/multi_node/__init__.py

@@ -1,6 +1,6 @@
 from lightning_app.components.multi_node.base import MultiNode
 from lightning_app.components.multi_node.lite import LiteMultiNode
-from lightning_app.components.multi_node.pl import PyTorchLightningMultiNode
 from lightning_app.components.multi_node.pytorch_spawn import PyTorchSpawnMultiNode
+from lightning_app.components.multi_node.trainer import LightningTrainerMultiNode
 
-__all__ = ["LiteMultiNode", "MultiNode", "PyTorchSpawnMultiNode", "PyTorchLightningMultiNode"]
+__all__ = ["LiteMultiNode", "MultiNode", "PyTorchSpawnMultiNode", "LightningTrainerMultiNode"]

src/lightning_app/components/multi_node/trainer.py

@@ -13,14 +13,14 @@ from lightning_app.utilities.tracer import Tracer
 
 
 @runtime_checkable
-class _PyTorchLightningWorkProtocol(Protocol):
+class _LightningTrainerWorkProtocol(Protocol):
     @staticmethod
     def run() -> None:
         ...
 
 
 @dataclass
-class _PyTorchLightningRunExecutor(_PyTorchSpawnRunExecutor):
+class _LightningTrainerRunExecutor(_PyTorchSpawnRunExecutor):
     @staticmethod
     def run(
         local_rank: int,
@@ -71,7 +71,7 @@ class _PyTorchLightningRunExecutor(_PyTorchSpawnRunExecutor):
         tracer._restore()
 
 
-class PyTorchLightningMultiNode(MultiNode):
+class LightningTrainerMultiNode(MultiNode):
     def __init__(
         self,
         work_cls: Type["LightningWork"],
@@ -80,7 +80,7 @@ class PyTorchLightningMultiNode(MultiNode):
         *work_args: Any,
         **work_kwargs: Any,
     ) -> None:
-        assert issubclass(work_cls, _PyTorchLightningWorkProtocol)
+        assert issubclass(work_cls, _LightningTrainerWorkProtocol)
         if not is_static_method(work_cls, "run"):
             raise TypeError(
                 f"The provided {work_cls} run method needs to be static for now."
@@ -89,7 +89,7 @@ class PyTorchLightningMultiNode(MultiNode):
 
         # Note: Private way to modify the work run executor
         # Probably exposed to the users in the future if needed.
-        work_cls._run_executor_cls = _PyTorchLightningRunExecutor
+        work_cls._run_executor_cls = _LightningTrainerRunExecutor
 
         super().__init__(
             work_cls,