Serve API Reference¶
Start or Connect to a Cluster¶
-
ray.serve.
start
(detached: bool = False, http_host: str = '127.0.0.1', http_port: int = 8000, http_middlewares: List[Any] = []) → ray.serve.api.Client[source]¶ Initialize a serve instance.
By default, the instance will be scoped to the lifetime of the returned Client object (or when the script exits). If detached is set to True, the instance will instead persist until client.shutdown() is called and clients to it can be connected using serve.connect(). This is only relevant if connecting to a long-running Ray cluster (e.g., with address=”auto”).
- Parameters
detached (bool) – Whether not the instance should be detached from this script.
http_host (str) – Host for HTTP servers to listen on. Defaults to “127.0.0.1”. To expose Serve publicly, you probably want to set this to “0.0.0.0”. One HTTP server will be started on each node in the Ray cluster.
http_port (int) – Port for HTTP server. Defaults to 8000.
http_middleswares (list) – A list of Starlette middlewares that will be applied to the HTTP servers in the cluster.
-
ray.serve.
connect
() → ray.serve.api.Client[source]¶ Connect to an existing Serve instance on this Ray cluster.
If calling from the driver program, the Serve instance on this Ray cluster must first have been initialized using serve.start(detached=True).
If called from within a backend, will connect to the same Serve instance that the backend is running in.
Client API¶
-
class
ray.serve.api.
Client
(controller: ray.actor.ActorHandle, controller_name: str, detached: bool = False)[source]¶ -
shutdown
() → None[source]¶ Completely shut down the connected Serve instance.
Shuts down all processes and deletes all state associated with the instance.
-
create_endpoint
(endpoint_name: str, *, backend: str = None, route: Optional[str] = None, methods: List[str] = ['GET']) → None[source]¶ Create a service endpoint given route_expression.
- Parameters
endpoint_name (str) – A name to associate to with the endpoint.
backend (str, required) – The backend that will serve requests to this endpoint. To change this or split traffic among backends, use serve.set_traffic.
route (str, optional) – A string begin with “/”. HTTP server will use the string to match the path.
methods (List[str], optional) – The HTTP methods that are valid for this endpoint.
-
delete_endpoint
(endpoint: str) → None[source]¶ Delete the given endpoint.
Does not delete any associated backends.
-
list_endpoints
() → Dict[str, Dict[str, Any]][source]¶ Returns a dictionary of all registered endpoints.
The dictionary keys are endpoint names and values are dictionaries of the form: {“methods”: List[str], “traffic”: Dict[str, float]}.
-
update_backend_config
(backend_tag: str, config_options: Union[ray.serve.config.BackendConfig, Dict[str, Any]]) → None[source]¶ Update a backend configuration for a backend tag.
Keys not specified in the passed will be left unchanged.
- Parameters
backend_tag (str) – A registered backend.
config_options (dict, serve.BackendConfig) – Backend config options to update. Either a BackendConfig object or a dict mapping strings to values for the following supported options: - “num_replicas”: number of worker processes to start up that will handle requests to this backend. - “max_batch_size”: the maximum number of requests that will be processed in one batch by this backend. - “batch_wait_timeout”: time in seconds that backend replicas will wait for a full batch of requests before processing a partial batch. - “max_concurrent_queries”: the maximum number of queries that will be sent to a replica of this backend without receiving a response.
-
get_backend_config
(backend_tag: str) → ray.serve.config.BackendConfig[source]¶ Get the backend configuration for a backend tag.
- Parameters
backend_tag (str) – A registered backend.
-
create_backend
(backend_tag: str, func_or_class: Union[Callable, Type[Callable]], *actor_init_args: Any, ray_actor_options: Optional[Dict] = None, config: Union[ray.serve.config.BackendConfig, Dict[str, Any], None] = None) → None[source]¶ Create a backend with the provided tag.
The backend will serve requests with func_or_class.
- Parameters
backend_tag (str) – a unique tag assign to identify this backend.
func_or_class (callable, class) – a function or a class implementing __call__.
actor_init_args (optional) – the arguments to pass to the class. initialization method.
ray_actor_options (optional) – options to be passed into the @ray.remote decorator for the backend actor.
config (dict, serve.BackendConfig, optional) – configuration options for this backend. Either a BackendConfig, or a dictionary mapping strings to values for the following supported options: - “num_replicas”: number of worker processes to start up that will handle requests to this backend. - “max_batch_size”: the maximum number of requests that will be processed in one batch by this backend. - “batch_wait_timeout”: time in seconds that backend replicas will wait for a full batch of requests before processing a partial batch. - “max_concurrent_queries”: the maximum number of queries that will be sent to a replica of this backend without receiving a response.
-
list_backends
() → Dict[str, Dict[str, Any]][source]¶ Returns a dictionary of all registered backends.
Dictionary maps backend tags to backend configs.
-
delete_backend
(backend_tag: str) → None[source]¶ Delete the given backend.
The backend must not currently be used by any endpoints.
-
set_traffic
(endpoint_name: str, traffic_policy_dictionary: Dict[str, float]) → None[source]¶ Associate a service endpoint with traffic policy.
Example:
>>> serve.set_traffic("service-name", { "backend:v1": 0.5, "backend:v2": 0.5 })
- Parameters
endpoint_name (str) – A registered service endpoint.
traffic_policy_dictionary (dict) – a dictionary maps backend names to their traffic weights. The weights must sum to 1.
-
shadow_traffic
(endpoint_name: str, backend_tag: str, proportion: float) → None[source]¶ Shadow traffic from an endpoint to a backend.
The specified proportion of requests will be duplicated and sent to the backend. Responses of the duplicated traffic will be ignored. The backend must not already be in use.
To stop shadowing traffic to a backend, call shadow_traffic with proportion equal to 0.
- Parameters
endpoint_name (str) – A registered service endpoint.
backend_tag (str) – A registered backend.
proportion (float) – The proportion of traffic from 0 to 1.
-
get_handle
(endpoint_name: str) → ray.serve.handle.RayServeHandle[source]¶ Retrieve RayServeHandle for service endpoint to invoke it from Python.
- Parameters
endpoint_name (str) – A registered service endpoint.
- Returns
RayServeHandle
-
Backend Configuration¶
-
class
ray.serve.
BackendConfig
[source]¶ Configuration options for a backend, to be set by the user.
- Parameters
num_replicas (int, optional) – The number of worker processes to start up that will handle requests to this backend. Defaults to 0.
max_batch_size (int, optional) – The maximum number of requests that will be processed in one batch by this backend. Defaults to None (no maximium).
batch_wait_timeout (float, optional) – The time in seconds that backend replicas will wait for a full batch of requests before processing a partial batch. Defaults to 0.
max_concurrent_queries (int, optional) – The maximum number of queries that will be sent to a replica of this backend without receiving a response. Defaults to None (no maximum).
Handle API¶
-
class
ray.serve.handle.
RayServeHandle
(router_handle, endpoint_name, *, method_name=None, shard_key=None, http_method=None, http_headers=None)[source]¶ A handle to a service endpoint.
Invoking this endpoint with .remote is equivalent to pinging an HTTP endpoint.
Example
>>> handle = serve.get_handle("my_endpoint") >>> handle RayServeHandle( Endpoint="my_endpoint", Traffic=... ) >>> handle.remote(my_request_content) ObjectRef(...) >>> ray.get(handle.remote(...)) # result >>> ray.get(handle.remote(let_it_crash_request)) # raises RayTaskError Exception
-
remote
(request_data: Union[Dict, Any, None] = None, **kwargs)[source]¶ Issue an asynchrounous request to the endpoint.
Returns a Ray ObjectRef whose results can be waited for or retrieved using ray.wait or ray.get, respectively.
- Returns
ray.ObjectRef
- Input:
- request_data(dict, Any): If it’s a dictionary, the data will be
available in
request.json()
orrequest.form()
. Otherwise, it will be available inrequest.data
.**kwargs
: All keyword arguments will be available inrequest.args
.
-
options
(method_name: Optional[str] = None, *, shard_key: Optional[str] = None, http_method: Optional[str] = None, http_headers: Optional[Dict[str, str]] = None)[source]¶ Set options for this handle.
- Parameters
method_name (str) – The method to invoke on the backend.
http_method (str) – The HTTP method to use for the request.
shard_key (str) – A string to use to deterministically map this request to a backend if there are multiple for this endpoint.
-
When calling from Python, the backend implementation will receive ServeRequest
objects instead of Flask requests.
-
class
ray.serve.utils.
ServeRequest
(data, kwargs, headers, method)[source]¶ The request object used in Python context.
ServeRequest is built to have similar API as Flask.Request. You only need to write your model serving code once; it can be queried by both HTTP and Python.
-
property
headers
¶ The HTTP headers from
handle.option(http_headers=...)
.
-
property
method
¶ The HTTP method data from
handle.option(http_method=...)
.
-
property
args
¶ The keyword arguments from
handle.remote(**kwargs)
.
-
property
json
¶ The request dictionary, from
handle.remote(dict)
.
-
property
form
¶ The request dictionary, from
handle.remote(dict)
.
-
property
data
¶ The request data from
handle.remote(obj)
.
-
property
Batching Requests¶
-
ray.serve.
accept_batch
(f: Callable) → Callable[source]¶ Annotation to mark that a serving function accepts batches of requests.
In order to accept batches of requests as input, the implementation must handle a list of requests being passed in rather than just a single request.
This must be set on any backend implementation that will have max_batch_size set to greater than 1.
Example:
>>> @serve.accept_batch def serving_func(requests): assert isinstance(requests, list) ...
>>> class ServingActor: @serve.accept_batch def __call__(self, requests): assert isinstance(requests, list)