ray.serve.schema.ServeDeploySchema

pydantic model ray.serve.schema.ServeDeploySchema

Multi-application config for deploying a list of Serve applications to the Ray cluster.

This is the request JSON schema for the v2 REST API PUT "/api/serve/applications/".

NOTE: This config allows extra parameters to make it forward-compatible (ie

older versions of Serve are able to accept configs from a newer versions, simply ignoring new parameters)

field applications: List[ServeApplicationSchema] [Required]

The set of applications to run on the Ray cluster.

field grpc_options: gRPCOptionsSchema = gRPCOptionsSchema(port=9000, grpc_servicer_functions=[], request_timeout_s=None)

Options to start the gRPC Proxy with.

field http_options: HTTPOptionsSchema = HTTPOptionsSchema(host='0.0.0.0', port=8000, root_path='', request_timeout_s=None, keep_alive_timeout_s=90, ssl_keyfile=None, ssl_certfile=None, ssl_keyfile_password=None, ssl_ca_certs=None)

Options to start the HTTP Proxy with.

field logging_config: LoggingConfig | None = None

Logging config for configuring serve components logs.

field proxy_location: ProxyLocation = ProxyLocation.EveryNode

Config for where to run proxies for ingress traffic to the cluster.

field target_capacity: float | None = None

[EXPERIMENTAL]: the target capacity percentage for all replicas across the cluster. The num_replicas, min_replicas, max_replicas, and initial_replicas for each deployment will be scaled by this percentage.

Constraints:

• ge = 0

• le = 100

validator application_names_nonempty  »  applications

validator application_names_unique  »  applications

validator application_routes_unique  »  applications

static get_empty_schema_dict() → Dict

Returns an empty deploy schema dictionary.

Schema can be used as a representation of an empty Serve deploy config.

validator nested_host_and_port  »  all fields