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tests/test_ws_dependencies.py
def test_index(): client = TestClient(app) with client.websocket_connect("/") as websocket: data = json.loads(websocket.receive_text()) assert data == ["app", "index"] def test_routerindex(): client = TestClient(app) with client.websocket_connect("/router") as websocket: data = json.loads(websocket.receive_text()) assert data == ["app", "router2", "router", "routerindex"]
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Sun Jun 11 20:35:39 GMT 2023 - 2.1K bytes - Viewed (0) -
docs_src/path_operation_advanced_configuration/tutorial007.py
"required": True, }, }, ) async def create_item(request: Request): raw_body = await request.body() try: data = yaml.safe_load(raw_body) except yaml.YAMLError: raise HTTPException(status_code=422, detail="Invalid YAML") try: item = Item.model_validate(data) except ValidationError as e:
Python - Registered: Sun May 05 07:19:11 GMT 2024 - Last Modified: Thu Apr 18 19:40:57 GMT 2024 - 822 bytes - Viewed (0) -
docs/de/docs/project-generation.md
* **PGAdmin** für die PostgreSQL-Datenbank, können Sie problemlos ändern, sodass PHPMyAdmin und MySQL verwendet wird. * **Flower** für die Überwachung von Celery-Jobs. * Load Balancing zwischen Frontend und Backend mit **Traefik**, sodass Sie beide unter derselben Domain haben können, getrennt durch den Pfad, aber von unterschiedlichen Containern ausgeliefert.
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tests/test_tutorial/test_body/test_tutorial001_py310.py
"type": "type_error.dict", } ] } ) @needs_py310 def test_other_exceptions(client: TestClient): with patch("json.loads", side_effect=Exception): response = client.post("/items/", json={"test": "test2"}) assert response.status_code == 400, response.text @needs_py310 def test_openapi_schema(client: TestClient):
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Thu Apr 18 19:40:57 GMT 2024 - 15K bytes - Viewed (2) -
tests/test_tutorial/test_body/test_tutorial001.py
"type": "type_error.dict", } ] } ) def test_other_exceptions(client: TestClient): with patch("json.loads", side_effect=Exception): response = client.post("/items/", json={"test": "test2"}) assert response.status_code == 400, response.text def test_openapi_schema(client: TestClient):
Python - Registered: Sun Apr 28 07:19:10 GMT 2024 - Last Modified: Thu Apr 18 19:40:57 GMT 2024 - 14.7K bytes - Viewed (7) -
docs/en/docs/deployment/concepts.md
### Server Memory For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**. And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨
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docs/pt/docs/deployment.md
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docs/en/docs/release-notes.md
from fastapi import FastAPI def fake_answer_to_everything_ml_model(x: float): return x * 42 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()
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docs_src/events/tutorial003.py
from fastapi import FastAPI def fake_answer_to_everything_ml_model(x: float): return x * 42 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)
Python - Registered: Sun May 05 07:19:11 GMT 2024 - Last Modified: Tue Mar 07 15:46:00 GMT 2023 - 569 bytes - Viewed (0) -
docs_src/path_operation_advanced_configuration/tutorial007_pv1.py
"required": True, }, }, ) async def create_item(request: Request): raw_body = await request.body() try: data = yaml.safe_load(raw_body) except yaml.YAMLError: raise HTTPException(status_code=422, detail="Invalid YAML") try: item = Item.parse_obj(data) except ValidationError as e:
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