def test_window_start_timestamp(self):
s = InMemoryMetricsStore()
assert s.window_average("m1", window_start_timestamp_s=0) is None
s.add_metrics_point({"m1": 1}, timestamp=2)
assert s.window_average("m1", window_start_timestamp_s=0) == 1
assert s.window_average(
"m1", window_start_timestamp_s=10, do_compact=False) is None
def test_compaction_max(self):
s = InMemoryMetricsStore()
s.add_metrics_point({"m1": 1}, timestamp=2)
s.add_metrics_point({"m1": 2}, timestamp=1)
assert s.max("m1", window_start_timestamp_s=0, do_compact=False) == 2
s.window_average("m1", window_start_timestamp_s=1.1, do_compact=True)
assert s.window_average("m1",
window_start_timestamp_s=0,
do_compact=False) == 1
def test_compaction(self):
s = InMemoryMetricsStore()
s.add_metrics_point({"m1": 1}, timestamp=1)
s.add_metrics_point({"m1": 2}, timestamp=2)
assert s.window_average("m1",
window_start_timestamp_s=0,
do_compact=False) == 1.5
s.window_average("m1", window_start_timestamp_s=1.1, do_compact=True)
# First record should be removed.
assert s.window_average("m1",
window_start_timestamp_s=0,
do_compact=False) == 2
def test_multiple_metrics(self):
s = InMemoryMetricsStore()
s.add_metrics_point({"m1": 1, "m2": -1}, timestamp=1)
s.add_metrics_point({"m1": 2, "m2": -2}, timestamp=2)
assert s.window_average("m1", window_start_timestamp_s=0) == 1.5
assert s.max("m1", window_start_timestamp_s=0) == 2
assert s.max("m2", window_start_timestamp_s=0) == -1
def test_out_of_order_insert(self):
s = InMemoryMetricsStore()
s.add_metrics_point({"m1": 1}, timestamp=1)
s.add_metrics_point({"m1": 5}, timestamp=5)
s.add_metrics_point({"m1": 3}, timestamp=3)
s.add_metrics_point({"m1": 2}, timestamp=2)
s.add_metrics_point({"m1": 4}, timestamp=4)
assert s.window_average("m1", window_start_timestamp_s=0) == 3
class ServeController:
"""Responsible for managing the state of the serving system.
The controller implements fault tolerance by persisting its state in
a new checkpoint each time a state change is made. If the actor crashes,
the latest checkpoint is loaded and the state is recovered. Checkpoints
are written/read using a provided KV-store interface.
All hard state in the system is maintained by this actor and persisted via
these checkpoints. Soft state required by other components is fetched by
those actors from this actor on startup and updates are pushed out from
this actor.
All other actors started by the controller are named, detached actors
so they will not fate share with the controller if it crashes.
The following guarantees are provided for state-changing calls to the
controller:
- If the call succeeds, the change was made and will be reflected in
the system even if the controller or other actors die unexpectedly.
- If the call fails, the change may have been made but isn't guaranteed
to have been. The client should retry in this case. Note that this
requires all implementations here to be idempotent.
"""
async def __init__(self,
controller_name: str,
http_config: HTTPOptions,
checkpoint_path: str,
detached: bool = False):
# Used to read/write checkpoints.
self.controller_namespace = ray.get_runtime_context().namespace
self.controller_name = controller_name
self.checkpoint_path = checkpoint_path
kv_store_namespace = (
f"{self.controller_name}-{self.controller_namespace}")
self.kv_store = make_kv_store(checkpoint_path,
namespace=kv_store_namespace)
self.snapshot_store = RayInternalKVStore(namespace=kv_store_namespace)
# Dictionary of deployment_name -> proxy_name -> queue length.
self.deployment_stats = defaultdict(lambda: defaultdict(dict))
# Used to ensure that only a single state-changing operation happens
# at any given time.
self.write_lock = asyncio.Lock()
self.long_poll_host = LongPollHost()
self.goal_manager = AsyncGoalManager()
self.http_state = HTTPState(controller_name, detached, http_config)
self.endpoint_state = EndpointState(self.kv_store, self.long_poll_host)
# Fetch all running actors in current cluster as source of current
# replica state for controller failure recovery
all_current_actor_names = ray.util.list_named_actors()
self.deployment_state_manager = DeploymentStateManager(
controller_name, detached, self.kv_store, self.long_poll_host,
self.goal_manager, all_current_actor_names)
# TODO(simon): move autoscaling related stuff into a manager.
self.autoscaling_metrics_store = InMemoryMetricsStore()
asyncio.get_event_loop().create_task(self.run_control_loop())
def record_autoscaling_metrics(self, data: Dict[str, float],
send_timestamp: float):
self.autoscaling_metrics_store.add_metrics_point(data, send_timestamp)
def _dump_autoscaling_metrics_for_testing(self):
return self.autoscaling_metrics_store.data
def _dump_replica_states_for_testing(self, deployment_name):
return self.deployment_state_manager._deployment_states[
deployment_name]._replicas
def _stop_one_running_replica_for_testing(self, deployment_name):
self.deployment_state_manager._deployment_states[
deployment_name]._stop_one_running_replica_for_testing()
async def wait_for_goal(self, goal_id: GoalId) -> Optional[Exception]:
return await self.goal_manager.wait_for_goal(goal_id)
async def _num_pending_goals(self) -> int:
return self.goal_manager.num_pending_goals()
async def listen_for_change(self, keys_to_snapshot_ids: Dict[str, int]):
"""Proxy long pull client's listen request.
Args:
keys_to_snapshot_ids (Dict[str, int]): Snapshot IDs are used to
determine whether or not the host should immediately return the
data or wait for the value to be changed.
"""
return await (
self.long_poll_host.listen_for_change(keys_to_snapshot_ids))
def get_checkpoint_path(self) -> str:
return self.checkpoint_path
def get_all_endpoints(self) -> Dict[EndpointTag, Dict[str, Any]]:
"""Returns a dictionary of deployment name to config."""
return self.endpoint_state.get_endpoints()
def get_http_proxies(self) -> Dict[NodeId, ActorHandle]:
"""Returns a dictionary of node ID to http_proxy actor handles."""
return self.http_state.get_http_proxy_handles()
def autoscale(self) -> None:
"""Updates autoscaling deployments with calculated num_replicas."""
for deployment_name, (deployment_info,
route_prefix) in self.list_deployments().items():
deployment_config = deployment_info.deployment_config
autoscaling_policy = deployment_info.autoscaling_policy
if autoscaling_policy is None:
continue
replicas = self.deployment_state_manager._deployment_states[
deployment_name]._replicas
running_replicas = replicas.get([ReplicaState.RUNNING])
current_num_ongoing_requests = []
for replica in running_replicas:
replica_tag = replica.replica_tag
num_ongoing_requests = (
self.autoscaling_metrics_store.window_average(
replica_tag,
time.time() -
autoscaling_policy.config.look_back_period_s))
if num_ongoing_requests is not None:
current_num_ongoing_requests.append(num_ongoing_requests)
if len(current_num_ongoing_requests) == 0:
continue
new_deployment_config = deployment_config.copy()
decision_num_replicas = (
autoscaling_policy.get_decision_num_replicas(
current_num_ongoing_requests=current_num_ongoing_requests,
curr_target_num_replicas=deployment_config.num_replicas))
new_deployment_config.num_replicas = decision_num_replicas
new_deployment_info = copy(deployment_info)
new_deployment_info.deployment_config = new_deployment_config
goal_id, updating = self.deployment_state_manager.deploy(
deployment_name, new_deployment_info)
async def run_control_loop(self) -> None:
# NOTE(edoakes): we catch all exceptions here and simply log them,
# because an unhandled exception would cause the main control loop to
# halt, which should *never* happen.
while True:
try:
self.autoscale()
except Exception:
logger.exception("Exception in autoscaling.")
async with self.write_lock:
try:
self.http_state.update()
except Exception:
logger.exception("Exception updating HTTP state.")
try:
self.deployment_state_manager.update()
except Exception:
logger.exception("Exception updating deployment state.")
try:
self._put_serve_snapshot()
except Exception:
logger.exception("Exception putting serve snapshot.")
await asyncio.sleep(CONTROL_LOOP_PERIOD_S)
def _put_serve_snapshot(self) -> None:
val = dict()
for deployment_name, (deployment_info,
route_prefix) in self.list_deployments(
include_deleted=True).items():
entry = dict()
entry["name"] = deployment_name
entry["namespace"] = ray.get_runtime_context().namespace
entry["ray_job_id"] = deployment_info.deployer_job_id.hex()
entry["class_name"] = (
deployment_info.replica_config.func_or_class_name)
entry["version"] = deployment_info.version
entry["http_route"] = route_prefix
entry["start_time"] = deployment_info.start_time_ms
entry["end_time"] = deployment_info.end_time_ms or 0
entry["status"] = ("DELETED"
if deployment_info.end_time_ms else "RUNNING")
entry["actors"] = dict()
if entry["status"] == "RUNNING":
replicas = self.deployment_state_manager._deployment_states[
deployment_name]._replicas
running_replicas = replicas.get([ReplicaState.RUNNING])
for replica in running_replicas:
try:
actor_handle = replica.actor_handle
except ValueError:
# Actor died or hasn't yet been created.
continue
actor_id = actor_handle._ray_actor_id.hex()
replica_tag = replica.replica_tag
replica_version = (None if (replica.version is None
or replica.version.unversioned)
else replica.version.code_version)
entry["actors"][actor_id] = {
"replica_tag": replica_tag,
"version": replica_version
}
val[deployment_name] = entry
self.snapshot_store.put(SNAPSHOT_KEY, json.dumps(val).encode("utf-8"))
def _all_running_replicas(self) -> Dict[str, List[RunningReplicaInfo]]:
"""Used for testing."""
return self.deployment_state_manager.get_running_replica_infos()
def get_http_config(self):
"""Return the HTTP proxy configuration."""
return self.http_state.get_config()
def get_root_url(self):
"""Return the root url for the serve instance."""
http_config = self.get_http_config()
if http_config.root_url == "":
if SERVE_ROOT_URL_ENV_KEY in os.environ:
return os.environ[SERVE_ROOT_URL_ENV_KEY]
else:
return f"http://{http_config.host}:{http_config.port}"
return http_config.root_url
async def shutdown(self) -> List[GoalId]:
"""Shuts down the serve instance completely."""
async with self.write_lock:
goal_ids = self.deployment_state_manager.shutdown()
self.endpoint_state.shutdown()
self.http_state.shutdown()
return goal_ids
def deploy(
self,
name: str,
deployment_config_proto_bytes: bytes,
replica_config: ReplicaConfig,
version: Optional[str],
prev_version: Optional[str],
route_prefix: Optional[str],
deployer_job_id: "ray._raylet.JobID",
) -> Tuple[Optional[GoalId], bool]:
if route_prefix is not None:
assert route_prefix.startswith("/")
deployment_config = DeploymentConfig.from_proto_bytes(
deployment_config_proto_bytes)
if prev_version is not None:
existing_deployment_info = (
self.deployment_state_manager.get_deployment(name))
if (existing_deployment_info is None
or not existing_deployment_info.version):
raise ValueError(
f"prev_version '{prev_version}' is specified but "
"there is no existing deployment.")
if existing_deployment_info.version != prev_version:
raise ValueError(
f"prev_version '{prev_version}' "
"does not match with the existing "
f"version '{existing_deployment_info.version}'.")
autoscaling_config = deployment_config.autoscaling_config
if autoscaling_config is not None:
# TODO: is this the desired behaviour? Should this be a setting?
deployment_config.num_replicas = autoscaling_config.min_replicas
autoscaling_policy = BasicAutoscalingPolicy(autoscaling_config)
else:
autoscaling_policy = None
deployment_info = DeploymentInfo(
actor_name=name,
serialized_deployment_def=replica_config.serialized_deployment_def,
version=version,
deployment_config=deployment_config,
replica_config=replica_config,
deployer_job_id=deployer_job_id,
start_time_ms=int(time.time() * 1000),
autoscaling_policy=autoscaling_policy)
# TODO(architkulkarni): When a deployment is redeployed, even if
# the only change was num_replicas, the start_time_ms is refreshed.
# Is this the desired behaviour?
goal_id, updating = self.deployment_state_manager.deploy(
name, deployment_info)
if route_prefix is not None:
endpoint_info = EndpointInfo(route=route_prefix)
self.endpoint_state.update_endpoint(name, endpoint_info)
return goal_id, updating
def delete_deployment(self, name: str) -> Optional[GoalId]:
self.endpoint_state.delete_endpoint(name)
return self.deployment_state_manager.delete_deployment(name)
def get_deployment_info(self, name: str) -> Tuple[DeploymentInfo, str]:
"""Get the current information about a deployment.
Args:
name(str): the name of the deployment.
Returns:
(DeploymentInfo, route)
Raises:
KeyError if the deployment doesn't exist.
"""
deployment_info = self.deployment_state_manager.get_deployment(name)
if deployment_info is None:
raise KeyError(f"Deployment {name} does not exist.")
route = self.endpoint_state.get_endpoint_route(name)
return deployment_info, route
def list_deployments(
self,
include_deleted: Optional[bool] = False
) -> Dict[str, Tuple[DeploymentInfo, str]]:
"""Gets the current information about all deployments.
Args:
include_deleted(bool): Whether to include information about
deployments that have been deleted.
Returns:
Dict(deployment_name, (DeploymentInfo, route))
Raises:
KeyError if the deployment doesn't exist.
"""
return {
name: (self.deployment_state_manager.get_deployment(
name, include_deleted=include_deleted),
self.endpoint_state.get_endpoint_route(name))
for name in self.deployment_state_manager.get_deployment_configs(
include_deleted=include_deleted)
}
def test_basics(self):
s = InMemoryMetricsStore()
s.add_metrics_point({"m1": 1}, timestamp=1)
s.add_metrics_point({"m1": 2}, timestamp=2)
assert s.window_average("m1", window_start_timestamp_s=0) == 1.5
class ServeController:
"""Responsible for managing the state of the serving system.
The controller implements fault tolerance by persisting its state in
a new checkpoint each time a state change is made. If the actor crashes,
the latest checkpoint is loaded and the state is recovered. Checkpoints
are written/read using a provided KV-store interface.
All hard state in the system is maintained by this actor and persisted via
these checkpoints. Soft state required by other components is fetched by
those actors from this actor on startup and updates are pushed out from
this actor.
All other actors started by the controller are named, detached actors
so they will not fate share with the controller if it crashes.
The following guarantees are provided for state-changing calls to the
controller:
- If the call succeeds, the change was made and will be reflected in
the system even if the controller or other actors die unexpectedly.
- If the call fails, the change may have been made but isn't guaranteed
to have been. The client should retry in this case. Note that this
requires all implementations here to be idempotent.
"""
async def __init__(
self,
controller_name: str,
http_config: HTTPOptions,
checkpoint_path: str,
detached: bool = False,
_override_controller_namespace: Optional[str] = None,
):
configure_component_logger(component_name="controller",
component_id=str(os.getpid()))
# Used to read/write checkpoints.
self.controller_namespace = ray.get_runtime_context().namespace
self.controller_name = controller_name
self.checkpoint_path = checkpoint_path
kv_store_namespace = f"{self.controller_name}-{self.controller_namespace}"
self.kv_store = make_kv_store(checkpoint_path,
namespace=kv_store_namespace)
self.snapshot_store = RayInternalKVStore(namespace=kv_store_namespace)
# Dictionary of deployment_name -> proxy_name -> queue length.
self.deployment_stats = defaultdict(lambda: defaultdict(dict))
# Used to ensure that only a single state-changing operation happens
# at any given time.
self.write_lock = asyncio.Lock()
self.long_poll_host = LongPollHost()
self.http_state = HTTPState(
controller_name,
detached,
http_config,
_override_controller_namespace=_override_controller_namespace,
)
self.endpoint_state = EndpointState(self.kv_store, self.long_poll_host)
# Fetch all running actors in current cluster as source of current
# replica state for controller failure recovery
all_current_actor_names = ray.util.list_named_actors()
self.deployment_state_manager = DeploymentStateManager(
controller_name,
detached,
self.kv_store,
self.long_poll_host,
all_current_actor_names,
_override_controller_namespace=_override_controller_namespace,
)
# Reference to Ray task executing most recent deployment request
self.config_deployment_request_ref: ObjectRef = None
# Unix timestamp of latest config deployment request. Defaults to 0.
self.deployment_timestamp = 0
# TODO(simon): move autoscaling related stuff into a manager.
self.autoscaling_metrics_store = InMemoryMetricsStore()
asyncio.get_event_loop().create_task(self.run_control_loop())
def check_alive(self) -> None:
"""No-op to check if this controller is alive."""
return
def record_autoscaling_metrics(self, data: Dict[str, float],
send_timestamp: float):
self.autoscaling_metrics_store.add_metrics_point(data, send_timestamp)
def _dump_autoscaling_metrics_for_testing(self):
return self.autoscaling_metrics_store.data
def _dump_replica_states_for_testing(self, deployment_name):
return self.deployment_state_manager._deployment_states[
deployment_name]._replicas
def _stop_one_running_replica_for_testing(self, deployment_name):
self.deployment_state_manager._deployment_states[
deployment_name]._stop_one_running_replica_for_testing()
async def listen_for_change(self, keys_to_snapshot_ids: Dict[str, int]):
"""Proxy long pull client's listen request.
Args:
keys_to_snapshot_ids (Dict[str, int]): Snapshot IDs are used to
determine whether or not the host should immediately return the
data or wait for the value to be changed.
"""
return await (
self.long_poll_host.listen_for_change(keys_to_snapshot_ids))
def get_checkpoint_path(self) -> str:
return self.checkpoint_path
def get_all_endpoints(self) -> Dict[EndpointTag, Dict[str, Any]]:
"""Returns a dictionary of deployment name to config."""
return self.endpoint_state.get_endpoints()
def get_http_proxies(self) -> Dict[NodeId, ActorHandle]:
"""Returns a dictionary of node ID to http_proxy actor handles."""
return self.http_state.get_http_proxy_handles()
def get_http_proxy_names(self) -> bytes:
"""Returns the http_proxy actor name list serialized by protobuf."""
from ray.serve.generated.serve_pb2 import ActorNameList
actor_name_list = ActorNameList(
names=self.http_state.get_http_proxy_names().values())
return actor_name_list.SerializeToString()
def autoscale(self) -> None:
"""Updates autoscaling deployments with calculated num_replicas."""
for deployment_name, (
deployment_info,
route_prefix,
) in self.list_deployments_internal().items():
deployment_config = deployment_info.deployment_config
autoscaling_policy = deployment_info.autoscaling_policy
if autoscaling_policy is None:
continue
replicas = self.deployment_state_manager._deployment_states[
deployment_name]._replicas
running_replicas = replicas.get([ReplicaState.RUNNING])
current_num_ongoing_requests = []
for replica in running_replicas:
replica_tag = replica.replica_tag
num_ongoing_requests = self.autoscaling_metrics_store.window_average(
replica_tag,
time.time() - autoscaling_policy.config.look_back_period_s,
)
if num_ongoing_requests is not None:
current_num_ongoing_requests.append(num_ongoing_requests)
if len(current_num_ongoing_requests) == 0:
continue
new_deployment_config = deployment_config.copy()
decision_num_replicas = autoscaling_policy.get_decision_num_replicas(
current_num_ongoing_requests=current_num_ongoing_requests,
curr_target_num_replicas=deployment_config.num_replicas,
)
new_deployment_config.num_replicas = decision_num_replicas
new_deployment_info = copy(deployment_info)
new_deployment_info.deployment_config = new_deployment_config
self.deployment_state_manager.deploy(deployment_name,
new_deployment_info)
async def run_control_loop(self) -> None:
# NOTE(edoakes): we catch all exceptions here and simply log them,
# because an unhandled exception would cause the main control loop to
# halt, which should *never* happen.
while True:
try:
self.autoscale()
except Exception:
logger.exception("Exception in autoscaling.")
async with self.write_lock:
try:
self.http_state.update()
except Exception:
logger.exception("Exception updating HTTP state.")
try:
self.deployment_state_manager.update()
except Exception:
logger.exception("Exception updating deployment state.")
try:
self._put_serve_snapshot()
except Exception:
logger.exception("Exception putting serve snapshot.")
await asyncio.sleep(CONTROL_LOOP_PERIOD_S)
def _put_serve_snapshot(self) -> None:
val = dict()
for deployment_name, (
deployment_info,
route_prefix,
) in self.list_deployments_internal(include_deleted=True).items():
entry = dict()
entry["name"] = deployment_name
entry["namespace"] = ray.get_runtime_context().namespace
entry["ray_job_id"] = deployment_info.deployer_job_id.hex()
entry[
"class_name"] = deployment_info.replica_config.func_or_class_name
entry["version"] = deployment_info.version
entry["http_route"] = route_prefix
entry["start_time"] = deployment_info.start_time_ms
entry["end_time"] = deployment_info.end_time_ms or 0
entry[
"status"] = "DELETED" if deployment_info.end_time_ms else "RUNNING"
entry["actors"] = dict()
if entry["status"] == "RUNNING":
replicas = self.deployment_state_manager._deployment_states[
deployment_name]._replicas
running_replicas = replicas.get([ReplicaState.RUNNING])
for replica in running_replicas:
try:
actor_handle = replica.actor_handle
except ValueError:
# Actor died or hasn't yet been created.
continue
actor_id = actor_handle._ray_actor_id.hex()
replica_tag = replica.replica_tag
replica_version = (None if (replica.version is None
or replica.version.unversioned)
else replica.version.code_version)
entry["actors"][actor_id] = {
"replica_tag": replica_tag,
"version": replica_version,
}
val[deployment_name] = entry
self.snapshot_store.put(SNAPSHOT_KEY, json.dumps(val).encode("utf-8"))
def _all_running_replicas(self) -> Dict[str, List[RunningReplicaInfo]]:
"""Used for testing."""
return self.deployment_state_manager.get_running_replica_infos()
def get_http_config(self):
"""Return the HTTP proxy configuration."""
return self.http_state.get_config()
def get_root_url(self):
"""Return the root url for the serve instance."""
http_config = self.get_http_config()
if http_config.root_url == "":
if SERVE_ROOT_URL_ENV_KEY in os.environ:
return os.environ[SERVE_ROOT_URL_ENV_KEY]
else:
return (f"http://{http_config.host}:{http_config.port}"
f"{http_config.root_path}")
return http_config.root_url
async def shutdown(self):
"""Shuts down the serve instance completely."""
async with self.write_lock:
self.deployment_state_manager.shutdown()
self.endpoint_state.shutdown()
self.http_state.shutdown()
def deploy(
self,
name: str,
deployment_config_proto_bytes: bytes,
replica_config_proto_bytes: bytes,
route_prefix: Optional[str],
deployer_job_id: "ray._raylet.JobID",
) -> bool:
if route_prefix is not None:
assert route_prefix.startswith("/")
deployment_config = DeploymentConfig.from_proto_bytes(
deployment_config_proto_bytes)
version = deployment_config.version
prev_version = deployment_config.prev_version
replica_config = ReplicaConfig.from_proto_bytes(
replica_config_proto_bytes, deployment_config.deployment_language)
if prev_version is not None:
existing_deployment_info = self.deployment_state_manager.get_deployment(
name)
if existing_deployment_info is None or not existing_deployment_info.version:
raise ValueError(
f"prev_version '{prev_version}' is specified but "
"there is no existing deployment.")
if existing_deployment_info.version != prev_version:
raise ValueError(
f"prev_version '{prev_version}' "
"does not match with the existing "
f"version '{existing_deployment_info.version}'.")
autoscaling_config = deployment_config.autoscaling_config
if autoscaling_config is not None:
# TODO: is this the desired behaviour? Should this be a setting?
deployment_config.num_replicas = autoscaling_config.min_replicas
autoscaling_policy = BasicAutoscalingPolicy(autoscaling_config)
else:
autoscaling_policy = None
deployment_info = DeploymentInfo(
actor_name=name,
serialized_deployment_def=replica_config.serialized_deployment_def,
version=version,
deployment_config=deployment_config,
replica_config=replica_config,
deployer_job_id=deployer_job_id,
start_time_ms=int(time.time() * 1000),
autoscaling_policy=autoscaling_policy,
)
# TODO(architkulkarni): When a deployment is redeployed, even if
# the only change was num_replicas, the start_time_ms is refreshed.
# Is this the desired behaviour?
updating = self.deployment_state_manager.deploy(name, deployment_info)
if route_prefix is not None:
endpoint_info = EndpointInfo(route=route_prefix)
self.endpoint_state.update_endpoint(name, endpoint_info)
else:
self.endpoint_state.delete_endpoint(name)
return updating
def deploy_group(self, deployment_args_list: List[Dict]) -> List[bool]:
"""
Takes in a list of dictionaries that contain keyword arguments for the
controller's deploy() function. Calls deploy on all the argument
dictionaries in the list. Effectively executes an atomic deploy on a
group of deployments.
"""
return [self.deploy(**args) for args in deployment_args_list]
def deploy_app(
self,
import_path: str,
runtime_env: str,
deployment_override_options: List[Dict],
) -> None:
"""Kicks off a task that deploys a Serve application.
Cancels any previous in-progress task that is deploying a Serve
application.
Args:
import_path (str): Serve deployment graph's import path
runtime_env (str): runtime_env to run the deployment graph in
deployment_override_options (List[Dict]): All dictionaries should
contain argument-value options that can be passed directly
into a set_options() call. Overrides deployment options set
in the graph itself.
"""
if self.config_deployment_request_ref is not None:
ray.cancel(self.config_deployment_request_ref)
logger.debug("Canceled existing config deployment request.")
self.config_deployment_request_ref = run_graph.options(
runtime_env=runtime_env).remote(import_path,
deployment_override_options)
self.deployment_timestamp = time.time()
def delete_deployment(self, name: str):
self.endpoint_state.delete_endpoint(name)
return self.deployment_state_manager.delete_deployment(name)
def delete_deployments(self, names: Iterable[str]) -> None:
for name in names:
self.delete_deployment(name)
def get_deployment_info(self, name: str) -> bytes:
"""Get the current information about a deployment.
Args:
name(str): the name of the deployment.
Returns:
DeploymentRoute's protobuf serialized bytes
Raises:
KeyError if the deployment doesn't exist.
"""
deployment_info = self.deployment_state_manager.get_deployment(name)
if deployment_info is None:
raise KeyError(f"Deployment {name} does not exist.")
route = self.endpoint_state.get_endpoint_route(name)
from ray.serve.generated.serve_pb2 import DeploymentRoute
deployment_route = DeploymentRoute(
deployment_info=deployment_info.to_proto(), route=route)
return deployment_route.SerializeToString()
def list_deployments_internal(
self,
include_deleted: Optional[bool] = False
) -> Dict[str, Tuple[DeploymentInfo, str]]:
"""Gets the current information about all deployments.
Args:
include_deleted(bool): Whether to include information about
deployments that have been deleted.
Returns:
Dict(deployment_name, (DeploymentInfo, route))
Raises:
KeyError if the deployment doesn't exist.
"""
return {
name: (
self.deployment_state_manager.get_deployment(
name, include_deleted=include_deleted),
self.endpoint_state.get_endpoint_route(name),
)
for name in self.deployment_state_manager.get_deployment_configs(
include_deleted=include_deleted)
}
def list_deployments(self,
include_deleted: Optional[bool] = False) -> bytes:
"""Gets the current information about all deployments.
Args:
include_deleted(bool): Whether to include information about
deployments that have been deleted.
Returns:
DeploymentRouteList's protobuf serialized bytes
"""
from ray.serve.generated.serve_pb2 import DeploymentRouteList, DeploymentRoute
deployment_route_list = DeploymentRouteList()
for deployment_name, (
deployment_info,
route_prefix,
) in self.list_deployments_internal(
include_deleted=include_deleted).items():
deployment_info_proto = deployment_info.to_proto()
deployment_info_proto.name = deployment_name
deployment_route_list.deployment_routes.append(
DeploymentRoute(deployment_info=deployment_info_proto,
route=route_prefix))
return deployment_route_list.SerializeToString()
async def get_serve_status(self) -> bytes:
serve_app_status = ApplicationStatus.RUNNING
serve_app_message = ""
deployment_timestamp = self.deployment_timestamp
if self.config_deployment_request_ref:
finished, pending = ray.wait([self.config_deployment_request_ref],
timeout=0)
if pending:
serve_app_status = ApplicationStatus.DEPLOYING
else:
try:
await finished[0]
except RayTaskError:
serve_app_status = ApplicationStatus.DEPLOY_FAILED
serve_app_message = f"Deployment failed:\n{traceback.format_exc()}"
app_status = ApplicationStatusInfo(serve_app_status, serve_app_message,
deployment_timestamp)
deployment_statuses = self.deployment_state_manager.get_deployment_statuses(
)
status_info = StatusOverview(
app_status=app_status,
deployment_statuses=deployment_statuses,
)
return status_info.to_proto().SerializeToString()
