Source code for ray.serve.deployment

import inspect
import logging
import warnings
from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

from ray.dag.class_node import ClassNode
from ray.dag.dag_node import DAGNodeBase
from ray.dag.function_node import FunctionNode
from ray.serve._private.config import DeploymentConfig, ReplicaConfig
from ray.serve._private.constants import SERVE_LOGGER_NAME
from ray.serve._private.utils import DEFAULT, Default
from ray.serve.config import AutoscalingConfig
from ray.serve.context import _get_global_client
from ray.serve.handle import RayServeHandle, RayServeSyncHandle
from ray.serve.schema import DeploymentSchema, LoggingConfig, RayActorOptionsSchema
from ray.util.annotations import Deprecated, PublicAPI

logger = logging.getLogger(SERVE_LOGGER_NAME)

[docs]@PublicAPI(stability="stable") class Application(DAGNodeBase): """One or more deployments bound with arguments that can be deployed together. Can be passed into another `Deployment.bind()` to compose multiple deployments in a single application, passed to ``, or deployed via a Serve config file. For example, to define an Application and run it in Python: .. code-block:: python from ray import serve from ray.serve import Application @serve.deployment class MyDeployment: pass app: Application = MyDeployment.bind(OtherDeployment.bind()) To run the same app using the command line interface (CLI): .. code-block:: bash serve run python_file:app To deploy the same app via a config file: .. code-block:: yaml applications: my_app: import_path: python_file:app """ def __init__( self, *, _internal_dag_node: Optional[Union[ClassNode, FunctionNode]] = None ): if _internal_dag_node is None: raise RuntimeError("This class should not be constructed directly.") self._internal_dag_node = _internal_dag_node def _get_internal_dag_node(self) -> Union[ClassNode, FunctionNode]: if self._internal_dag_node is None: raise RuntimeError("Application object should not be constructed directly.") return self._internal_dag_node @classmethod def _from_internal_dag_node(cls, dag_node: Union[ClassNode, FunctionNode]): return cls(_internal_dag_node=dag_node) # Proxy all method calls to the underlying DAG node. This allows this class to be # passed in place of the ClassNode or FunctionNode in the DAG building code. def __getattr__(self, name: str) -> Any: return getattr(self._get_internal_dag_node(), name)
[docs]@PublicAPI(stability="stable") class Deployment: """Class (or function) decorated with the `@serve.deployment` decorator. This is run on a number of replica actors. Requests to those replicas call this class. One or more deployments can be composed together into an `Application` which is then run via `` or a config file. Example: .. code-block:: python @serve.deployment class MyDeployment: def __init__(self, name: str): self._name = name def __call__(self, request): return "Hello world!" app = MyDeployment.bind() # Run via `` or the `serve run` CLI command. """ def __init__( self, name: str, deployment_config: DeploymentConfig, replica_config: ReplicaConfig, version: Optional[str] = None, route_prefix: Union[str, None, DEFAULT] = DEFAULT.VALUE, _internal=False, ) -> None: if not _internal: raise RuntimeError( "The Deployment constructor should not be called " "directly. Use `@serve.deployment` instead." ) if not isinstance(name, str): raise TypeError("name must be a string.") if not (version is None or isinstance(version, str)): raise TypeError("version must be a string.") if route_prefix is not DEFAULT.VALUE and route_prefix is not None: if not isinstance(route_prefix, str): raise TypeError("route_prefix must be a string.") if not route_prefix.startswith("/"): raise ValueError("route_prefix must start with '/'.") if route_prefix != "/" and route_prefix.endswith("/"): raise ValueError( "route_prefix must not end with '/' unless it's the root." ) if "{" in route_prefix or "}" in route_prefix: raise ValueError("route_prefix may not contain wildcards.") docs_path = None if ( inspect.isclass(replica_config.deployment_def) and hasattr(replica_config.deployment_def, "__module__") and replica_config.deployment_def.__module__ == "ray.serve.api" and hasattr(replica_config.deployment_def, "__fastapi_docs_path__") ): docs_path = replica_config.deployment_def.__fastapi_docs_path__ self._name = name self._version = version self._deployment_config = deployment_config self._replica_config = replica_config self._route_prefix = route_prefix self._docs_path = docs_path @property def name(self) -> str: """Unique name of this deployment.""" return self._name @property def version(self) -> Optional[str]: return self._version @property def func_or_class(self) -> Union[Callable, str]: """Underlying class or function that this deployment wraps.""" return self._replica_config.deployment_def @property def num_replicas(self) -> int: """Current target number of replicas.""" return self._deployment_config.num_replicas @property def user_config(self) -> Any: """Current dynamic user-provided config options.""" return self._deployment_config.user_config @property def max_concurrent_queries(self) -> int: """Current max outstanding queries from each handle.""" return self._deployment_config.max_concurrent_queries @property def route_prefix(self) -> Optional[str]: """HTTP route prefix that this deployment is exposed under.""" if self._route_prefix is DEFAULT.VALUE: return f"/{self._name}" return self._route_prefix @property def ray_actor_options(self) -> Optional[Dict]: """Actor options such as resources required for each replica.""" return self._replica_config.ray_actor_options @property def init_args(self) -> Tuple[Any]: return self._replica_config.init_args @property def init_kwargs(self) -> Tuple[Any]: return self._replica_config.init_kwargs @property def url(self) -> Optional[str]: if self._route_prefix is None: # this deployment is not exposed over HTTP return None return _get_global_client().root_url + self.route_prefix @property def logging_config(self) -> Dict: return self._deployment_config.logging_config def set_logging_config(self, logging_config: Dict): self._deployment_config.logging_config = logging_config def __call__(self): raise RuntimeError( "Deployments cannot be constructed directly. " "Use `deployment.deploy() instead.`" )
[docs] def bind(self, *args, **kwargs) -> Application: """Bind the arguments to the deployment and return an Application. The returned Application can be deployed using `` (or via config file) or bound to another deployment for composition. """ schema_shell = deployment_to_schema(self) if inspect.isfunction(self.func_or_class): dag_node = FunctionNode( self.func_or_class, args, # Used to bind and resolve DAG only, can take user input kwargs, # Used to bind and resolve DAG only, can take user input self._replica_config.ray_actor_options or dict(), other_args_to_resolve={ "deployment_schema": schema_shell, "is_from_serve_deployment": True, }, ) else: dag_node = ClassNode( self.func_or_class, args, kwargs, cls_options=self._replica_config.ray_actor_options or dict(), other_args_to_resolve={ "deployment_schema": schema_shell, "is_from_serve_deployment": True, }, ) return Application._from_internal_dag_node(dag_node)
def deploy(self, *init_args, _blocking=True, **init_kwargs): raise ValueError( "This API has been fully deprecated. Please use instead." ) def _deploy(self, *init_args, _blocking=True, **init_kwargs): """Deploy or update this deployment. Args: init_args: args to pass to the class __init__ method. Not valid if this deployment wraps a function. init_kwargs: kwargs to pass to the class __init__ method. Not valid if this deployment wraps a function. """ if len(init_args) == 0 and self._replica_config.init_args is not None: init_args = self._replica_config.init_args if len(init_kwargs) == 0 and self._replica_config.init_kwargs is not None: init_kwargs = self._replica_config.init_kwargs replica_config = ReplicaConfig.create( self._replica_config.deployment_def, init_args=init_args, init_kwargs=init_kwargs, ray_actor_options=self._replica_config.ray_actor_options, placement_group_bundles=self._replica_config.placement_group_bundles, placement_group_strategy=self._replica_config.placement_group_strategy, max_replicas_per_node=self._replica_config.max_replicas_per_node, ) return _get_global_client().deploy( self._name, replica_config=replica_config, deployment_config=self._deployment_config, version=self._version, route_prefix=self.route_prefix, url=self.url, _blocking=_blocking, ) def delete(self): raise ValueError( "This API has been fully deprecated. Please use and " "serve.delete() instead." ) def _delete(self): """Delete this deployment.""" return _get_global_client().delete_deployments([self._name]) def get_handle( self, sync: Optional[bool] = True ) -> Union[RayServeHandle, RayServeSyncHandle]: raise ValueError( "This API has been fully deprecated. Please use serve.get_app_handle() or " "serve.get_deployment_handle() instead." ) def _get_handle( self, sync: Optional[bool] = True, ) -> Union[RayServeHandle, RayServeSyncHandle]: """Get a ServeHandle to this deployment to invoke it from Python. Args: sync: If true, then Serve will return a ServeHandle that works everywhere. Otherwise, Serve will return an asyncio-optimized ServeHandle that's only usable in an asyncio loop. Returns: ServeHandle """ return _get_global_client().get_handle( self._name, app_name="", missing_ok=True, sync=sync, use_new_handle_api=False, )
[docs] def options( self, func_or_class: Optional[Callable] = None, name: Default[str] = DEFAULT.VALUE, version: Default[str] = DEFAULT.VALUE, num_replicas: Default[Optional[int]] = DEFAULT.VALUE, route_prefix: Default[Union[str, None]] = DEFAULT.VALUE, ray_actor_options: Default[Optional[Dict]] = DEFAULT.VALUE, placement_group_bundles: Optional[List[Dict[str, float]]] = DEFAULT.VALUE, placement_group_strategy: Optional[str] = DEFAULT.VALUE, max_replicas_per_node: Optional[int] = DEFAULT.VALUE, user_config: Default[Optional[Any]] = DEFAULT.VALUE, max_concurrent_queries: Default[int] = DEFAULT.VALUE, autoscaling_config: Default[ Union[Dict, AutoscalingConfig, None] ] = DEFAULT.VALUE, graceful_shutdown_wait_loop_s: Default[float] = DEFAULT.VALUE, graceful_shutdown_timeout_s: Default[float] = DEFAULT.VALUE, health_check_period_s: Default[float] = DEFAULT.VALUE, health_check_timeout_s: Default[float] = DEFAULT.VALUE, logging_config: Default[Union[Dict, LoggingConfig, None]] = DEFAULT.VALUE, _init_args: Default[Tuple[Any]] = DEFAULT.VALUE, _init_kwargs: Default[Dict[Any, Any]] = DEFAULT.VALUE, _internal: bool = False, ) -> "Deployment": """Return a copy of this deployment with updated options. Only those options passed in will be updated, all others will remain unchanged from the existing deployment. Refer to the `@serve.deployment` decorator docs for available arguments. """ # NOTE: The user_configured_option_names should be the first thing that's # defined in this method. It depends on the locals() dictionary storing # only the function args/kwargs. # Create list of all user-configured options from keyword args user_configured_option_names = [ option for option, value in locals().items() if option not in {"self", "func_or_class", "_internal"} and value is not DEFAULT.VALUE ] new_deployment_config = deepcopy(self._deployment_config) if not _internal: new_deployment_config.user_configured_option_names.update( user_configured_option_names ) if num_replicas not in [DEFAULT.VALUE, None] and autoscaling_config not in [ DEFAULT.VALUE, None, ]: raise ValueError( "Manually setting num_replicas is not allowed when " "autoscaling_config is provided." ) if num_replicas == 0: raise ValueError("num_replicas is expected to larger than 0") if not _internal and version is not DEFAULT.VALUE: logger.warning( "DeprecationWarning: `version` in `Deployment.options()` has been " "deprecated. Explicitly specifying version will raise an error in the " "future!" ) if not _internal and route_prefix is not DEFAULT.VALUE: logger.warning( "DeprecationWarning: `route_prefix` in `@serve.deployment` has been " "deprecated. To specify a route prefix for an application, pass it " "into `` instead." ) if num_replicas not in [DEFAULT.VALUE, None]: new_deployment_config.num_replicas = num_replicas if user_config is not DEFAULT.VALUE: new_deployment_config.user_config = user_config if max_concurrent_queries is not DEFAULT.VALUE: new_deployment_config.max_concurrent_queries = max_concurrent_queries if func_or_class is None: func_or_class = self._replica_config.deployment_def if name is DEFAULT.VALUE: name = self._name if version is DEFAULT.VALUE: version = self._version if _init_args is DEFAULT.VALUE: _init_args = self._replica_config.init_args if _init_kwargs is DEFAULT.VALUE: _init_kwargs = self._replica_config.init_kwargs if route_prefix is DEFAULT.VALUE: # Default is to keep the previous value route_prefix = self._route_prefix if ray_actor_options is DEFAULT.VALUE: ray_actor_options = self._replica_config.ray_actor_options if placement_group_bundles is DEFAULT.VALUE: placement_group_bundles = self._replica_config.placement_group_bundles if placement_group_strategy is DEFAULT.VALUE: placement_group_strategy = self._replica_config.placement_group_strategy if max_replicas_per_node is DEFAULT.VALUE: max_replicas_per_node = self._replica_config.max_replicas_per_node if autoscaling_config is not DEFAULT.VALUE: new_deployment_config.autoscaling_config = autoscaling_config if graceful_shutdown_wait_loop_s is not DEFAULT.VALUE: new_deployment_config.graceful_shutdown_wait_loop_s = ( graceful_shutdown_wait_loop_s ) if graceful_shutdown_timeout_s is not DEFAULT.VALUE: new_deployment_config.graceful_shutdown_timeout_s = ( graceful_shutdown_timeout_s ) if health_check_period_s is not DEFAULT.VALUE: new_deployment_config.health_check_period_s = health_check_period_s if health_check_timeout_s is not DEFAULT.VALUE: new_deployment_config.health_check_timeout_s = health_check_timeout_s if logging_config is not DEFAULT.VALUE: if isinstance(logging_config, LoggingConfig): logging_config = logging_config.dict() new_deployment_config.logging_config = logging_config new_replica_config = ReplicaConfig.create( func_or_class, init_args=_init_args, init_kwargs=_init_kwargs, ray_actor_options=ray_actor_options, placement_group_bundles=placement_group_bundles, placement_group_strategy=placement_group_strategy, max_replicas_per_node=max_replicas_per_node, ) return Deployment( name, new_deployment_config, new_replica_config, version=version, route_prefix=route_prefix, _internal=True, )
[docs] @Deprecated( message=( "This was intended for use with the `` Python API " "(which has been deprecated). Use `.options()` instead." ) ) def set_options( self, func_or_class: Optional[Callable] = None, name: Default[str] = DEFAULT.VALUE, version: Default[str] = DEFAULT.VALUE, num_replicas: Default[Optional[int]] = DEFAULT.VALUE, route_prefix: Default[Union[str, None]] = DEFAULT.VALUE, ray_actor_options: Default[Optional[Dict]] = DEFAULT.VALUE, user_config: Default[Optional[Any]] = DEFAULT.VALUE, max_concurrent_queries: Default[int] = DEFAULT.VALUE, autoscaling_config: Default[ Union[Dict, AutoscalingConfig, None] ] = DEFAULT.VALUE, graceful_shutdown_wait_loop_s: Default[float] = DEFAULT.VALUE, graceful_shutdown_timeout_s: Default[float] = DEFAULT.VALUE, health_check_period_s: Default[float] = DEFAULT.VALUE, health_check_timeout_s: Default[float] = DEFAULT.VALUE, _internal: bool = False, ) -> None: """Overwrite this deployment's options in-place. Only those options passed in will be updated, all others will remain unchanged. Refer to the @serve.deployment decorator docstring for all non-private arguments. """ if not _internal: warnings.warn( "`.set_options()` is deprecated. " "Use `.options()` or an application builder function instead." ) validated = self.options( func_or_class=func_or_class, name=name, version=version, route_prefix=route_prefix, num_replicas=num_replicas, ray_actor_options=ray_actor_options, user_config=user_config, max_concurrent_queries=max_concurrent_queries, autoscaling_config=autoscaling_config, graceful_shutdown_wait_loop_s=graceful_shutdown_wait_loop_s, graceful_shutdown_timeout_s=graceful_shutdown_timeout_s, health_check_period_s=health_check_period_s, health_check_timeout_s=health_check_timeout_s, _internal=_internal, ) self._name = validated._name self._version = validated._version self._route_prefix = validated._route_prefix self._deployment_config = validated._deployment_config self._replica_config = validated._replica_config
def __eq__(self, other): return all( [ self._name == other._name, self._version == other._version, self._deployment_config == other._deployment_config, self._replica_config.init_args == other._replica_config.init_args, self._replica_config.init_kwargs == other._replica_config.init_kwargs, # compare route prefix with default value resolved self.route_prefix == other.route_prefix, self._replica_config.ray_actor_options == self._replica_config.ray_actor_options, ] ) def __str__(self): return ( f"Deployment(name={self._name}," f"version={self._version}," f"route_prefix={self.route_prefix})" ) def __repr__(self): return str(self)
def deployment_to_schema( d: Deployment, include_route_prefix: bool = True ) -> DeploymentSchema: """Converts a live deployment object to a corresponding structured schema. Args: d: Deployment object to convert include_route_prefix: Whether to include the route_prefix in the returned schema. This should be set to False if the schema will be included in a higher-level object describing an application, and you want to place route_prefix at the application level. """ if d.ray_actor_options is not None: ray_actor_options_schema = RayActorOptionsSchema.parse_obj(d.ray_actor_options) else: ray_actor_options_schema = None deployment_options = { "name":, "num_replicas": None if d._deployment_config.autoscaling_config else d.num_replicas, "max_concurrent_queries": d.max_concurrent_queries, "user_config": d.user_config, "autoscaling_config": d._deployment_config.autoscaling_config, "graceful_shutdown_wait_loop_s": d._deployment_config.graceful_shutdown_wait_loop_s, # noqa: E501 "graceful_shutdown_timeout_s": d._deployment_config.graceful_shutdown_timeout_s, "health_check_period_s": d._deployment_config.health_check_period_s, "health_check_timeout_s": d._deployment_config.health_check_timeout_s, "ray_actor_options": ray_actor_options_schema, "placement_group_strategy": d._replica_config.placement_group_strategy, "placement_group_bundles": d._replica_config.placement_group_bundles, "max_replicas_per_node": d._replica_config.max_replicas_per_node, "logging_config": d._deployment_config.logging_config, } if include_route_prefix: deployment_options["route_prefix"] = d.route_prefix # Let non-user-configured options be set to defaults. If the schema # is converted back to a deployment, this lets Serve continue tracking # which options were set by the user. Name is a required field in the # schema, so it should be passed in explicitly. for option in list(deployment_options.keys()): if ( option != "name" and option not in d._deployment_config.user_configured_option_names ): del deployment_options[option] # TODO(Sihan) DeploymentConfig num_replicas and auto_config can be set together # because internally we use these two field for autoscale and deploy. # We can improve the code after we separate the user faced deployment config and # internal deployment config. return DeploymentSchema(**deployment_options) def schema_to_deployment(s: DeploymentSchema) -> Deployment: """Creates a deployment with parameters specified in schema. The returned deployment CANNOT be deployed immediately. It's func_or_class value is an empty string (""), which is not a valid import path. The func_or_class value must be overwritten with a valid function or class before the deployment can be deployed. """ if s.ray_actor_options is DEFAULT.VALUE: ray_actor_options = None else: ray_actor_options = s.ray_actor_options.dict(exclude_unset=True) if s.placement_group_bundles is DEFAULT.VALUE: placement_group_bundles = None else: placement_group_bundles = s.placement_group_bundles if s.placement_group_strategy is DEFAULT.VALUE: placement_group_strategy = None else: placement_group_strategy = s.placement_group_strategy if s.max_replicas_per_node is DEFAULT.VALUE: max_replicas_per_node = None else: max_replicas_per_node = s.max_replicas_per_node deployment_config = DeploymentConfig.from_default( num_replicas=s.num_replicas, user_config=s.user_config, max_concurrent_queries=s.max_concurrent_queries, autoscaling_config=s.autoscaling_config, graceful_shutdown_wait_loop_s=s.graceful_shutdown_wait_loop_s, graceful_shutdown_timeout_s=s.graceful_shutdown_timeout_s, health_check_period_s=s.health_check_period_s, health_check_timeout_s=s.health_check_timeout_s, logging_config=s.logging_config, ) deployment_config.user_configured_option_names = ( s.get_user_configured_option_names() ) replica_config = ReplicaConfig.create( deployment_def="", init_args=(), init_kwargs={}, ray_actor_options=ray_actor_options, placement_group_bundles=placement_group_bundles, placement_group_strategy=placement_group_strategy, max_replicas_per_node=max_replicas_per_node, ) return Deployment(, deployment_config=deployment_config, replica_config=replica_config, route_prefix=s.route_prefix, _internal=True, )