[App] Implement `ready` for components (#16129)

src/lightning_app/components/serve/auto_scaler.py

@@ -212,6 +212,8 @@ class _LoadBalancer(LightningWork):
             else:
                 raise ValueError("cold_start_proxy must be of type ColdStartProxy or str")
 
+        self.ready = False
+
     async def send_batch(self, batch: List[Tuple[str, _BatchRequestModel]], server_url: str):
         request_data: List[_LoadBalancer._input_type] = [b[1] for b in batch]
         batch_request_data = _BatchRequestModel(inputs=request_data)
@@ -410,6 +412,7 @@ class _LoadBalancer(LightningWork):
             )
 
         logger.info(f"Your load balancer has started. The endpoint is 'http://{self.host}:{self.port}{self.endpoint}'")
+        self.ready = True
 
         uvicorn.run(
             fastapi_app,
@@ -641,6 +644,10 @@ class AutoScaler(LightningFlow):
     def workers(self) -> List[LightningWork]:
         return [self.get_work(i) for i in range(self.num_replicas)]
 
+    @property
+    def ready(self) -> bool:
+        return self.load_balancer.ready
+
     def create_work(self) -> LightningWork:
         """Replicates a LightningWork instance with args and kwargs provided via ``__init__``."""
         cloud_compute = self._work_kwargs.get("cloud_compute", None)

src/lightning_app/components/serve/gradio.py

@@ -42,6 +42,8 @@ class ServeGradio(LightningWork, abc.ABC):
         assert self.outputs
         self._model = None
 
+        self.ready = False
+
     @property
     def model(self):
         return self._model
@@ -62,6 +64,7 @@ class ServeGradio(LightningWork, abc.ABC):
             self._model = self.build_model()
         fn = partial(self.predict, *args, **kwargs)
         fn.__name__ = self.predict.__name__
+        self.ready = True
         gradio.Interface(
             fn=fn,
             inputs=self.inputs,

src/lightning_app/components/serve/python_server.py

@@ -193,6 +193,8 @@ class PythonServer(LightningWork, abc.ABC):
         self._input_type = input_type
         self._output_type = output_type
 
+        self.ready = False
+
     def setup(self, *args, **kwargs) -> None:
         """This method is called before the server starts. Override this if you need to download the model or
         initialize the weights, setting up pipelines etc.
@@ -300,6 +302,7 @@ class PythonServer(LightningWork, abc.ABC):
         fastapi_app = FastAPI()
         self._attach_predict_fn(fastapi_app)
 
+        self.ready = True
         logger.info(
             f"Your {self.__class__.__qualname__} has started. View it in your browser: http://{self.host}:{self.port}"
         )

src/lightning_app/components/serve/serve.py

@@ -64,6 +64,8 @@ class ModelInferenceAPI(LightningWork, abc.ABC):
         self.workers = workers
         self._model = None
 
+        self.ready = False
+
     @property
     def model(self):
         return self._model
@@ -108,9 +110,11 @@ class ModelInferenceAPI(LightningWork, abc.ABC):
                 "serve:fastapi_service",
             ]
             process = subprocess.Popen(command, env=env, cwd=os.path.dirname(__file__))
+            self.ready = True
             process.wait()
         else:
             self._populate_app(fastapi_service)
+            self.ready = True
             self._launch_server(fastapi_service)
 
     def _populate_app(self, fastapi_service: FastAPI):

src/lightning_app/core/flow.py

@@ -797,7 +797,7 @@ class _RootFlow(LightningFlow):
     @property
     def ready(self) -> bool:
         ready = getattr(self.work, "ready", None)
-        if ready:
+        if ready is not None:
             return ready
         return self.work.url != ""
 

tests/tests_app/core/test_lightning_flow.py

@@ -12,7 +12,7 @@ from deepdiff import DeepDiff, Delta
 
 import lightning_app
 from lightning_app import CloudCompute, LightningApp
-from lightning_app.core.flow import LightningFlow
+from lightning_app.core.flow import _RootFlow, LightningFlow
 from lightning_app.core.work import LightningWork
 from lightning_app.runners import MultiProcessRuntime
 from lightning_app.storage import Path
@@ -868,10 +868,10 @@ def test_lightning_flow_flows_and_works():
 class WorkReady(LightningWork):
     def __init__(self):
         super().__init__(parallel=True)
-        self.counter = 0
+        self.ready = False
 
     def run(self):
-        self.counter += 1
+        self.ready = True
 
 
 class FlowReady(LightningFlow):
@@ -890,7 +890,13 @@ class FlowReady(LightningFlow):
             self._exit()
 
 
-def test_flow_ready():
+class RootFlowReady(_RootFlow):
+    def __init__(self):
+        super().__init__(WorkReady())
+
+
+@pytest.mark.parametrize("flow", [FlowReady, RootFlowReady])
+def test_flow_ready(flow):
     """This test validates that the app status queue is populated correctly."""
 
     mock_queue = _MockQueue("api_publish_state_queue")
@@ -910,7 +916,7 @@ def test_flow_ready():
         state["done"] = new_done
         return False
 
-    app = LightningApp(FlowReady())
+    app = LightningApp(flow())
     app._run = partial(run_patch, method=app._run)
     app.run_once = partial(lagged_run_once, method=app.run_once)
     MultiProcessRuntime(app, start_server=False).dispatch()