Installation Quick Install Baetyl Install from source Guides Baetyl Configuration Interpretation Device connect to Hub Service Message transferring among devices with Hub Service Message handling with Function edge devices. It can provide temporary offline, low-latency computing services, and include device connect, message routing, remote synchronization, function computing, video access pre-processing, AI inference DDRESS: The address of the instance of the service The official function manager service is to connect to the Baetyl Master by reading BAETYL_MASTER_API_ADDRESS. For example, the BAETYL_MASTER_API_ADDRESS

baetyl-function-python Configuration baetyl-remote-mqtt Configuration baetyl-timer Configuration Device connect to Baetyl with Hub service Workflow Connection Test Message transferring among devices with Local edge devices. It can provide temporary offline, low-latency computing services, and include device connect, message routing, remote synchronization, function computing, video access pre-processing, AI inference DDRESS: The address of the instance of the service The official function manager service is to connect to the Baetyl Master by reading BAETYL_MASTER_API_ADDRESS. For example, the BAETYL_MASTER_API_ADDRESS

to explicitly connect to the database before using it, managing connections explicitly is a good practice. This way if the connection fails, the exception can be caught during the “connect” step, rather arbitrary time later when a query is executed. >>> database = SqliteDatabase('stats.db') >>> database.connect() To use this database with your models, specify it in an inner “Meta” class: class MyModel(Model): = peewee.CharField() ... ... class Meta: ... database = custom_db ... >>> custom_db.connect() >>> CustomModel.create_table() Best practice: define a base model class that points at the database

database = db # this model uses the "people.db" database Now that we have our models, let’s connect to the database. Although it’s not necessary to open the connection explicitly, it is good practice connection when it receives a request, and close the connection when it sends the response. >>> db.connect() We’ll begin by creating the tables in the database that will store our data. This will create interpreter. We can create a small helper function to accomplish this: def create_tables(): database.connect() database.create_tables([User, Relationship, Message]) Open a python shell in the directory

database = db # this model uses the "people.db" database Now that we have our models, let’s connect to the database. Although it’s not necessary to open the connection explicitly, it is good practice connection when it receives a request, and close the connection when it sends the response. db.connect() We’ll begin by creating the tables in the database that will store our data. This will create instance, if you are using a connection pool, connections will only be recycled correctly if you call connect() and close(). We will tell flask that during the request/response cycle we need to create a connection

database = db # this model uses the "people.db" database Now that we have our models, let’s connect to the database. Although it’s not necessary to open the connection explicitly, it is good practice connection when it receives a request, and close the connection when it sends the response. db.connect() We’ll begin by creating the tables in the database that will store our data. This will create instance, if you are using a connection pool, connections will only be recycled correctly if you call connect() and close(). We will tell flask that during the request/response cycle we need to create a connection

database = db # this model uses the "people.db" database Now that we have our models, let’s connect to the database. Although it’s not necessary to open the connection explicitly, it is good practice connection when it receives a request, and close the connection when it sends the response. db.connect() We’ll begin by creating the tables in the database that will store our data. This will create instance, if you are using a connection pool, connections will only be recycled correctly if you call connect() and close(). We will tell flask that during the request/response cycle we need to create a connection

database = db # this model uses the "people.db" database Now that we have our models, let’s connect to the database. Although it’s not necessary to open the connection explicitly, it is good practice connection when it receives a request, and close the connection when it sends the response. db.connect() We’ll begin by creating the tables in the database that will store our data. This will create instance, if you are using a connection pool, connections will only be recycled correctly if you call connect() and close(). We will tell flask that during the request/response cycle we need to create a connection

tornado.websocket.websocket_connect(url: Union[str, tornado.httpclient.HTTPRequest], callback: Optional[Callable[[Future[WebSocketClientConnection]], None]] = None, connect_timeout: Optional[float] = None websocket_connect(url) while True: msg = yield conn.read_message() if msg is None: break # Do something with msg In the callback style, pass an on_message_callback to websocket_connect. In both WebSocket client connection. This class should not be instantiated directly; use the websocket_connect function instead. close(code: Optional[int] = None, reason: Optional[str] = None) → None[source]

support tornado.websocket.websocket_connect(url: Union[str, tornado.httpclient.HTTPRequest], callback: Callable[[Future[WebSocketClientConnection]], None] = None, connect_timeout: float = None, on_message_callback: websocket_connect(url) while True: msg = yield conn.read_message() if msg is None: break # Do something with msg In the callback style, pass an on_message_callback to websocket_connect. In both WebSocket client connection. This class should not be instantiated directly; use the websocket_connect function instead. close(code: int = None, reason: str = None) → None[source] Closes the websocket