import logging
import warnings
from enum import Enum
from typing import Any, Callable, List, Optional, Union
from ray import cloudpickle
from ray._private.pydantic_compat import (
BaseModel,
Field,
NonNegativeFloat,
NonNegativeInt,
PositiveFloat,
PositiveInt,
PrivateAttr,
validator,
)
from ray._private.utils import import_attr
from ray.serve._private.constants import (
DEFAULT_AUTOSCALING_POLICY,
DEFAULT_GRPC_PORT,
DEFAULT_HTTP_HOST,
DEFAULT_HTTP_PORT,
DEFAULT_TARGET_ONGOING_REQUESTS,
DEFAULT_UVICORN_KEEP_ALIVE_TIMEOUT_S,
SERVE_LOGGER_NAME,
)
from ray.util.annotations import Deprecated, PublicAPI
logger = logging.getLogger(SERVE_LOGGER_NAME)
[docs]
@PublicAPI(stability="stable")
class AutoscalingConfig(BaseModel):
"""Config for the Serve Autoscaler."""
# Please keep these options in sync with those in
# `src/ray/protobuf/serve.proto`.
# Publicly exposed options
min_replicas: NonNegativeInt = 1
initial_replicas: Optional[NonNegativeInt] = None
max_replicas: PositiveInt = 1
target_ongoing_requests: PositiveFloat = DEFAULT_TARGET_ONGOING_REQUESTS
# How often to scrape for metrics
metrics_interval_s: PositiveFloat = 10.0
# Time window to average over for metrics.
look_back_period_s: PositiveFloat = 30.0
# DEPRECATED
smoothing_factor: PositiveFloat = 1.0
# DEPRECATED: replaced by `downscaling_factor`
upscale_smoothing_factor: Optional[PositiveFloat] = Field(
default=None, description="[DEPRECATED] Please use `upscaling_factor` instead."
)
# DEPRECATED: replaced by `upscaling_factor`
downscale_smoothing_factor: Optional[PositiveFloat] = Field(
default=None,
description="[DEPRECATED] Please use `downscaling_factor` instead.",
)
# Multiplicative "gain" factor to limit scaling decisions
upscaling_factor: Optional[PositiveFloat] = None
downscaling_factor: Optional[PositiveFloat] = None
# How frequently to make autoscaling decisions
# loop_period_s: float = CONTROL_LOOP_PERIOD_S
# How long to wait before scaling down replicas
downscale_delay_s: NonNegativeFloat = 600.0
# How long to wait before scaling up replicas
upscale_delay_s: NonNegativeFloat = 30.0
# Cloudpickled policy definition.
_serialized_policy_def: bytes = PrivateAttr(default=b"")
# Custom autoscaling config. Defaults to the request-based autoscaler.
_policy: Union[str, Callable] = PrivateAttr(default=DEFAULT_AUTOSCALING_POLICY)
@validator("max_replicas", always=True)
def replicas_settings_valid(cls, max_replicas, values):
min_replicas = values.get("min_replicas")
initial_replicas = values.get("initial_replicas")
if min_replicas is not None and max_replicas < min_replicas:
raise ValueError(
f"max_replicas ({max_replicas}) must be greater than "
f"or equal to min_replicas ({min_replicas})!"
)
if initial_replicas is not None:
if initial_replicas < min_replicas:
raise ValueError(
f"min_replicas ({min_replicas}) must be less than "
f"or equal to initial_replicas ({initial_replicas})!"
)
elif initial_replicas > max_replicas:
raise ValueError(
f"max_replicas ({max_replicas}) must be greater than "
f"or equal to initial_replicas ({initial_replicas})!"
)
return max_replicas
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.serialize_policy()
[docs]
def serialize_policy(self) -> None:
"""Serialize policy with cloudpickle.
Import the policy if it's passed in as a string import path. Then cloudpickle
the policy and set `serialized_policy_def` if it's empty.
"""
values = self.dict()
policy = values.get("_policy")
if isinstance(policy, Callable):
policy = f"{policy.__module__}.{policy.__name__}"
if not policy:
policy = DEFAULT_AUTOSCALING_POLICY
policy_path = policy
policy = import_attr(policy)
if not values.get("_serialized_policy_def"):
self._serialized_policy_def = cloudpickle.dumps(policy)
self._policy = policy_path
@classmethod
def default(cls):
return cls(
target_ongoing_requests=DEFAULT_TARGET_ONGOING_REQUESTS,
min_replicas=1,
max_replicas=100,
)
[docs]
def get_policy(self) -> Callable:
"""Deserialize policy from cloudpickled bytes."""
return cloudpickle.loads(self._serialized_policy_def)
def get_upscaling_factor(self) -> PositiveFloat:
if self.upscaling_factor:
return self.upscaling_factor
return self.upscale_smoothing_factor or self.smoothing_factor
def get_downscaling_factor(self) -> PositiveFloat:
if self.downscaling_factor:
return self.downscaling_factor
return self.downscale_smoothing_factor or self.smoothing_factor
def get_target_ongoing_requests(self) -> PositiveFloat:
return self.target_ongoing_requests
# Keep in sync with ServeDeploymentMode in dashboard/client/src/type/serve.ts
@Deprecated
class DeploymentMode(str, Enum):
NoServer = "NoServer"
HeadOnly = "HeadOnly"
EveryNode = "EveryNode"
[docs]
@PublicAPI(stability="stable")
class ProxyLocation(str, Enum):
"""Config for where to run proxies to receive ingress traffic to the cluster.
Options:
- Disabled: don't run proxies at all. This should be used if you are only
making calls to your applications via deployment handles.
- HeadOnly: only run a single proxy on the head node.
- EveryNode: run a proxy on every node in the cluster that has at least one
replica actor. This is the default.
"""
Disabled = "Disabled"
HeadOnly = "HeadOnly"
EveryNode = "EveryNode"
@classmethod
def _to_deployment_mode(
cls, proxy_location: Union["ProxyLocation", str]
) -> DeploymentMode:
if isinstance(proxy_location, str):
proxy_location = ProxyLocation(proxy_location)
elif not isinstance(proxy_location, ProxyLocation):
raise TypeError(
f"Must be a `ProxyLocation` or str, got: {type(proxy_location)}."
)
if proxy_location == ProxyLocation.Disabled:
return DeploymentMode.NoServer
else:
return DeploymentMode(proxy_location.value)
@classmethod
def _from_deployment_mode(
cls, deployment_mode: Optional[Union[DeploymentMode, str]]
) -> Optional["ProxyLocation"]:
"""Converts DeploymentMode enum into ProxyLocation enum.
DeploymentMode is a deprecated version of ProxyLocation that's still
used internally throughout Serve.
"""
if deployment_mode is None:
return None
elif isinstance(deployment_mode, str):
deployment_mode = DeploymentMode(deployment_mode)
elif not isinstance(deployment_mode, DeploymentMode):
raise TypeError(
f"Must be a `DeploymentMode` or str, got: {type(deployment_mode)}."
)
if deployment_mode == DeploymentMode.NoServer:
return ProxyLocation.Disabled
else:
return ProxyLocation(deployment_mode.value)
[docs]
@PublicAPI(stability="stable")
class HTTPOptions(BaseModel):
"""HTTP options for the proxies. Supported fields:
- host: Host that the proxies listens for HTTP on. Defaults to
"127.0.0.1". To expose Serve publicly, you probably want to set
this to "0.0.0.0".
- port: Port that the proxies listen for HTTP on. Defaults to 8000.
- root_path: An optional root path to mount the serve application
(for example, "/prefix"). All deployment routes are prefixed
with this path.
- request_timeout_s: End-to-end timeout for HTTP requests.
- keep_alive_timeout_s: Duration to keep idle connections alive when no
requests are ongoing.
- location: [DEPRECATED: use `proxy_location` field instead] The deployment
location of HTTP servers:
- "HeadOnly": start one HTTP server on the head node. Serve
assumes the head node is the node you executed serve.start
on. This is the default.
- "EveryNode": start one HTTP server per node.
- "NoServer": disable HTTP server.
- num_cpus: [DEPRECATED] The number of CPU cores to reserve for each
internal Serve HTTP proxy actor.
"""
host: Optional[str] = DEFAULT_HTTP_HOST
port: int = DEFAULT_HTTP_PORT
middlewares: List[Any] = []
location: Optional[DeploymentMode] = DeploymentMode.HeadOnly
num_cpus: int = 0
root_url: str = ""
root_path: str = ""
request_timeout_s: Optional[float] = None
keep_alive_timeout_s: int = DEFAULT_UVICORN_KEEP_ALIVE_TIMEOUT_S
@validator("location", always=True)
def location_backfill_no_server(cls, v, values):
if values["host"] is None or v is None:
return DeploymentMode.NoServer
return v
@validator("middlewares", always=True)
def warn_for_middlewares(cls, v, values):
if v:
warnings.warn(
"Passing `middlewares` to HTTPOptions is deprecated and will be "
"removed in a future version. Consider using the FastAPI integration "
"to configure middlewares on your deployments: "
"https://docs.ray.io/en/latest/serve/http-guide.html#fastapi-http-deployments" # noqa 501
)
return v
@validator("num_cpus", always=True)
def warn_for_num_cpus(cls, v, values):
if v:
warnings.warn(
"Passing `num_cpus` to HTTPOptions is deprecated and will be "
"removed in a future version."
)
return v
class Config:
validate_assignment = True
arbitrary_types_allowed = True
[docs]
@PublicAPI(stability="alpha")
class gRPCOptions(BaseModel):
"""gRPC options for the proxies. Supported fields:
Args:
port (int):
Port for gRPC server if started. Default to 9000. Cannot be
updated once Serve has started running. Serve must be shut down and
restarted with the new port instead.
grpc_servicer_functions (List[str]):
List of import paths for gRPC `add_servicer_to_server` functions to add to
Serve's gRPC proxy. Default to empty list, which means no gRPC methods will
be added and no gRPC server will be started. The servicer functions need to
be importable from the context of where Serve is running.
"""
port: int = DEFAULT_GRPC_PORT
grpc_servicer_functions: List[str] = []
@property
def grpc_servicer_func_callable(self) -> List[Callable]:
"""Return a list of callable functions from the grpc_servicer_functions.
If the function is not callable or not found, it will be ignored and a warning
will be logged.
"""
callables = []
for func in self.grpc_servicer_functions:
try:
imported_func = import_attr(func)
if callable(imported_func):
callables.append(imported_func)
else:
message = (
f"{func} is not a callable function! Please make sure "
"the function is imported correctly."
)
raise ValueError(message)
except ModuleNotFoundError as e:
message = (
f"{func} can't be imported! Please make sure there are no typo "
"in those functions. Or you might want to rebuild service "
"definitions if .proto file is changed."
)
raise ModuleNotFoundError(message) from e
return callables