You can read more about the Starlette `lifespan` handlers in <a href="https://www.starlette.io/lifespan/" class="external-link" target="_blank">Starlette's  Lifespan' docs</a>.

Including how to handle lifespan state that can be used in other areas of your code.

///

## Sub Applications

///

/// warning | "Achtung"

Falls Ihre Anwendung auf Lifespan-Events angewiesen ist, der `AsyncClient` löst diese Events nicht aus. Um sicherzustellen, dass sie ausgelöst werden, verwenden Sie `LifespanManager` von <a href="https://github.com/florimondmanca/asgi-lifespan#usage" class="external-link" target="_blank">florimondmanca/asgi-lifespan</a>.

///

## Andere asynchrone Funktionsaufrufe

///

/// warning

If your application relies on lifespan events, the `AsyncClient` won't trigger these events. To ensure they are triggered, use `LifespanManager` from <a href="https://github.com/florimondmanca/asgi-lifespan#usage" class="external-link" target="_blank">florimondmanca/asgi-lifespan</a>.

///

## Other Asynchronous Function Calls

ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):