async def test_router_use_max_concurrency(serve_instance):
signal = SignalActor.remote()
@ray.remote
class MockWorker:
async def handle_request(self, request):
await signal.wait.remote()
return "DONE"
def ready(self):
pass
class VisibleRouter(Router):
def get_queues(self):
return self.queries_counter, self.backend_queues
worker = MockWorker.remote()
q = ray.remote(VisibleRouter).remote()
BACKEND_NAME = "max-concurrent-test"
config = BackendConfig({"max_concurrent_queries": 1})
await q.set_traffic.remote("svc", TrafficPolicy({BACKEND_NAME: 1.0}))
await q.add_new_worker.remote(BACKEND_NAME, "replica-tag", worker)
await q.set_backend_config.remote(BACKEND_NAME, config)
# We send over two queries
first_query = q.enqueue_request.remote(RequestMetadata("svc", None), 1)
second_query = q.enqueue_request.remote(RequestMetadata("svc", None), 1)
# Neither queries should be available
with pytest.raises(ray.exceptions.RayTimeoutError):
ray.get([first_query, second_query], timeout=0.2)
# Let's retrieve the router internal state
queries_counter, backend_queues = await q.get_queues.remote()
# There should be just one inflight request
assert queries_counter["max-concurrent-test:replica-tag"] == 1
# The second query is buffered
assert len(backend_queues["max-concurrent-test"]) == 1
# Let's unblock the first query
await signal.send.remote(clear=True)
assert await first_query == "DONE"
# The internal state of router should have changed.
queries_counter, backend_queues = await q.get_queues.remote()
# There should still be one inflight request
assert queries_counter["max-concurrent-test:replica-tag"] == 1
# But there shouldn't be any queries in the queue
assert len(backend_queues["max-concurrent-test"]) == 0
# Unblocking the second query
await signal.send.remote(clear=True)
assert await second_query == "DONE"
# Checking the internal state of the router one more time
queries_counter, backend_queues = await q.get_queues.remote()
assert queries_counter["max-concurrent-test:replica-tag"] == 0
assert len(backend_queues["max-concurrent-test"]) == 0
def test_worker_replica_failure(serve_instance):
@ray.remote
class Counter:
def __init__(self):
self.count = 0
def inc_and_get(self):
self.count += 1
return self.count
class Worker:
# Assumes that two replicas are started. Will hang forever in the
# constructor for any workers that are restarted.
def __init__(self, counter):
self.should_hang = False
self.index = ray.get(counter.inc_and_get.remote())
if self.index > 2:
while True:
pass
def __call__(self, *args):
return self.index
counter = Counter.remote()
serve.create_backend("replica_failure", Worker, counter)
serve.update_backend_config(
"replica_failure", BackendConfig(num_replicas=2))
serve.create_endpoint(
"replica_failure", backend="replica_failure", route="/replica_failure")
# Wait until both replicas have been started.
responses = set()
start = time.time()
while time.time() - start < 30:
time.sleep(0.1)
response = request_with_retries("/replica_failure", timeout=1).text
assert response in ["1", "2"]
responses.add(response)
if len(responses) > 1:
break
else:
raise TimeoutError("Timed out waiting for replicas after 30s.")
# Kill one of the replicas.
handles = _get_worker_handles("replica_failure")
assert len(handles) == 2
ray.kill(handles[0], no_restart=False)
# Check that the other replica still serves requests.
for _ in range(10):
while True:
try:
# The timeout needs to be small here because the request to
# the restarting worker will hang.
request_with_retries("/replica_failure", timeout=0.1)
break
except TimeoutError:
time.sleep(0.1)
async def test_task_runner_custom_method_batch(serve_instance):
q = RoundRobinPolicyQueueActor.remote()
@serve.accept_batch
class Batcher:
def a(self, _):
return ["a-{}".format(i) for i in range(serve.context.batch_size)]
def b(self, _):
return ["b-{}".format(i) for i in range(serve.context.batch_size)]
def error_different_size(self, _):
return [""] * (serve.context.batch_size * 2)
def error_non_iterable(self, _):
return 42
def return_np_array(self, _):
return np.array([1] * serve.context.batch_size).astype(np.int32)
CONSUMER_NAME = "runner"
PRODUCER_NAME = "producer"
worker = setup_worker(CONSUMER_NAME, Batcher)
await q.set_traffic.remote(PRODUCER_NAME, {CONSUMER_NAME: 1.0})
await q.set_backend_config.remote(
CONSUMER_NAME,
BackendConfig({
"max_batch_size": 10
}, accepts_batches=True))
def make_request_param(call_method):
return RequestMetadata(
PRODUCER_NAME, context.TaskContext.Python, call_method=call_method)
a_query_param = make_request_param("a")
b_query_param = make_request_param("b")
futures = [q.enqueue_request.remote(a_query_param) for _ in range(2)]
futures += [q.enqueue_request.remote(b_query_param) for _ in range(2)]
await q.add_new_worker.remote(CONSUMER_NAME, "replica1", worker)
gathered = await asyncio.gather(*futures)
assert set(gathered) == {"a-0", "a-1", "b-0", "b-1"}
with pytest.raises(RayServeException, match="doesn't preserve batch size"):
different_size = make_request_param("error_different_size")
await q.enqueue_request.remote(different_size)
with pytest.raises(RayServeException, match="iterable"):
non_iterable = make_request_param("error_non_iterable")
await q.enqueue_request.remote(non_iterable)
np_array = make_request_param("return_np_array")
result_np_value = await q.enqueue_request.remote(np_array)
assert isinstance(result_np_value, np.int32)
def test_worker_replica_failure(serve_instance):
client = serve_instance
class Worker:
# Assumes that two replicas are started. Will hang forever in the
# constructor for any workers that are restarted.
def __init__(self, path):
self.should_hang = False
if not os.path.exists(path):
with open(path, "w") as f:
f.write("1")
else:
with open(path, "r") as f:
num = int(f.read())
with open(path, "w") as f:
if num == 2:
self.should_hang = True
else:
f.write(str(num + 1))
if self.should_hang:
while True:
pass
def __call__(self):
pass
temp_path = os.path.join(tempfile.gettempdir(),
serve.utils.get_random_letters())
client.create_backend("replica_failure", Worker, temp_path)
client.update_backend_config("replica_failure",
BackendConfig(num_replicas=2))
client.create_endpoint("replica_failure",
backend="replica_failure",
route="/replica_failure")
# Wait until both replicas have been started.
responses = set()
while len(responses) == 1:
responses.add(request_with_retries("/replica_failure", timeout=1).text)
time.sleep(0.1)
# Kill one of the replicas.
handles = _get_worker_handles(client, "replica_failure")
assert len(handles) == 2
ray.kill(handles[0], no_restart=False)
# Check that the other replica still serves requests.
for _ in range(10):
while True:
try:
# The timeout needs to be small here because the request to
# the restarting worker will hang.
request_with_retries("/replica_failure", timeout=0.1)
break
except TimeoutError:
time.sleep(0.1)
def deploy(self,
name: str,
backend_def: Union[Callable, Type[Callable], str],
*init_args: Any,
ray_actor_options: Optional[Dict] = None,
config: Optional[Union[BackendConfig, Dict[str, Any]]] = None,
version: Optional[str] = None,
_blocking: Optional[bool] = True) -> Optional[GoalId]:
if config is None:
config = {}
if ray_actor_options is None:
ray_actor_options = {}
# If conda is activated and a conda env is not specified in runtime_env
# in ray_actor_options, default to conda env of this process (client).
# Without this code, the backend would run in the controller's conda
# env, which is likely different from that of the client.
# If using Ray client, skip this convenience feature because the local
# client env doesn't create the Ray cluster (so the client env is
# likely not present on the cluster.)
if not ray.util.client.ray.is_connected():
if ray_actor_options.get("runtime_env") is None:
ray_actor_options["runtime_env"] = {}
if ray_actor_options["runtime_env"].get("conda") is None:
current_env = os.environ.get("CONDA_DEFAULT_ENV")
if current_env is not None and current_env != "":
ray_actor_options["runtime_env"]["conda"] = current_env
replica_config = ReplicaConfig(backend_def,
*init_args,
ray_actor_options=ray_actor_options)
metadata = BackendMetadata(
accepts_batches=replica_config.accepts_batches,
is_blocking=replica_config.is_blocking,
is_asgi_app=replica_config.is_asgi_app,
path_prefix=replica_config.path_prefix,
)
if isinstance(config, dict):
backend_config = BackendConfig.parse_obj({
**config, "internal_metadata":
metadata
})
elif isinstance(config, BackendConfig):
backend_config = config.copy(
update={"internal_metadata": metadata})
else:
raise TypeError("config must be a BackendConfig or a dictionary.")
backend_config._validate_complete()
goal_ref = self._controller.deploy.remote(name, backend_config,
replica_config, version)
if _blocking:
self._wait_for_goal(goal_ref)
else:
return goal_ref
async def test_graceful_shutdown(serve_instance, router,
mock_controller_with_name):
class KeepInflight:
def __init__(self):
self.events = []
def reconfigure(self, config):
if config["release"]:
[event.set() for event in self.events]
async def __call__(self, _):
e = asyncio.Event()
self.events.append(e)
await e.wait()
backend_worker = await add_servable_to_router(
KeepInflight,
router,
mock_controller_with_name[0],
backend_config=BackendConfig(
num_replicas=1,
internal_metadata=BackendMetadata(is_blocking=False),
user_config={"release": False}))
query_param = make_request_param()
refs = [(await router.assign_request.remote(query_param))
for _ in range(6)]
shutdown_ref = backend_worker.drain_pending_queries.remote()
with pytest.raises(ray.exceptions.GetTimeoutError):
# Shutdown should block because there are still inflight queries.
ray.get(shutdown_ref, timeout=2)
config = BackendConfig()
config.user_config = {"release": True}
await mock_controller_with_name[1].update_backend.remote("backend", config)
# All queries should complete successfully
ray.get(refs)
# The draining operation should be completed.
ray.get(shutdown_ref)
async def do_autoscale(self) -> None:
for backend, info in self.configuration_store.backends.items():
if backend not in self.autoscaling_policies:
continue
new_num_replicas = self.autoscaling_policies[backend].scale(
self.backend_stats[backend], info.backend_config.num_replicas)
if new_num_replicas > 0:
await self.update_backend_config(
backend, BackendConfig(num_replicas=new_num_replicas))
def test_scaling_replicas(serve_instance, use_legacy_config):
client = serve_instance
class Counter:
def __init__(self):
self.count = 0
def __call__(self, _):
self.count += 1
return self.count
config = {
"num_replicas": 2
} if use_legacy_config else BackendConfig(num_replicas=2)
client.create_backend("counter:v1", Counter, config=config)
client.create_endpoint("counter", backend="counter:v1", route="/increment")
# Keep checking the routing table until /increment is populated
while "/increment" not in requests.get(
"http://127.0.0.1:8000/-/routes").json():
time.sleep(0.2)
counter_result = []
for _ in range(10):
resp = requests.get("http://127.0.0.1:8000/increment").json()
counter_result.append(resp)
# If the load is shared among two replicas. The max result cannot be 10.
assert max(counter_result) < 10
update_config = {
"num_replicas": 1
} if use_legacy_config else BackendConfig(num_replicas=1)
client.update_backend_config("counter:v1", update_config)
counter_result = []
for _ in range(10):
resp = requests.get("http://127.0.0.1:8000/increment").json()
counter_result.append(resp)
# Give some time for a replica to spin down. But majority of the request
# should be served by the only remaining replica.
assert max(counter_result) - min(counter_result) > 6
async def test_user_config_update(serve_instance, mock_controller_with_name):
class Customizable:
def __init__(self):
self.reval = ""
def __call__(self, starlette_request):
return self.retval
def reconfigure(self, config):
self.retval = config["return_val"]
config = BackendConfig(num_replicas=2,
user_config={
"return_val": "original",
"b": 2
})
worker, router = await add_servable_to_router(Customizable,
*mock_controller_with_name,
backend_config=config)
query_param = make_request_param()
done = [(await router.assign_request(query_param)) for _ in range(10)]
for i in done:
assert await i == "original"
config = BackendConfig()
config.user_config = {"return_val": "new_val"}
await mock_controller_with_name[1].update_backend.remote("backend", config)
async def new_val_returned():
result = await (await router.assign_request(query_param))
assert "new_val" == result
for _ in range(10):
try:
await new_val_returned()
except AssertionError:
# Wait for config to propogate
await asyncio.sleep(0.5)
new_val_returned()
def add_new_replica(self,
backend_tag,
runner_actor,
backend_config=BackendConfig()):
self.backend_replicas[backend_tag].append(runner_actor)
self.backend_configs[backend_tag] = backend_config
self.host.notify_changed(
"worker_handles",
self.backend_replicas,
)
self.host.notify_changed("backend_configs", self.backend_configs)
async def test_user_config_update(serve_instance, router,
mock_controller_with_name):
class Customizable:
def __init__(self):
self.reval = ""
def __call__(self, starlette_request):
return self.retval
def reconfigure(self, config):
self.retval = config["return_val"]
config = BackendConfig(
num_replicas=2, user_config={
"return_val": "original",
"b": 2
})
await add_servable_to_router(
Customizable,
router,
mock_controller_with_name[0],
backend_config=config)
query_param = make_request_param()
done = [(await router.assign_request.remote(query_param))
for _ in range(10)]
for i in done:
assert await i == "original"
config = BackendConfig()
config.user_config = {"return_val": "new_val"}
await mock_controller_with_name[1].update_backend.remote("backend", config)
done = [(await router.assign_request.remote(query_param))
for _ in range(10)]
for i in done:
assert await i == "new_val"
def add_new_replica(self,
backend_tag,
runner_actor,
backend_config=BackendConfig()):
self.backend_replicas[backend_tag].append(runner_actor)
self.backend_configs[backend_tag] = backend_config
self.host.notify_changed(
LongPollKey.REPLICA_HANDLES,
self.backend_replicas,
)
self.host.notify_changed(LongPollKey.BACKEND_CONFIGS,
self.backend_configs)
def test_updating_config(serve_instance, use_legacy_config):
client = serve_instance
class BatchSimple:
def __init__(self):
self.count = 0
@serve.accept_batch
def __call__(self, request):
return [1] * len(request)
config = {
"max_batch_size": 2,
"num_replicas": 3
} if use_legacy_config else BackendConfig(
max_batch_size=2, num_replicas=3)
client.create_backend("bsimple:v1", BatchSimple, config=config)
client.create_endpoint("bsimple", backend="bsimple:v1", route="/bsimple")
controller = client._controller
old_replica_tag_list = ray.get(
controller._list_replicas.remote("bsimple:v1"))
update_config = {
"max_batch_size": 5
} if use_legacy_config else BackendConfig(max_batch_size=5)
client.update_backend_config("bsimple:v1", update_config)
new_replica_tag_list = ray.get(
controller._list_replicas.remote("bsimple:v1"))
new_all_tag_list = []
for worker_dict in ray.get(
controller.get_all_worker_handles.remote()).values():
new_all_tag_list.extend(list(worker_dict.keys()))
# the old and new replica tag list should be identical
# and should be subset of all_tag_list
assert set(old_replica_tag_list) <= set(new_all_tag_list)
assert set(old_replica_tag_list) == set(new_replica_tag_list)
def test_serve_graceful_shutdown(serve_instance):
client = serve_instance
signal = SignalActor.remote()
class WaitBackend:
@serve.accept_batch
async def __call__(self, requests):
signal_actor = await requests[0].body()
await signal_actor.wait.remote()
return ["" for _ in range(len(requests))]
client.create_backend(
"wait",
WaitBackend,
config=BackendConfig(
# Make sure we can queue up queries in the replica side.
max_concurrent_queries=10,
max_batch_size=1,
experimental_graceful_shutdown_wait_loop_s=0.5,
experimental_graceful_shutdown_timeout_s=1000,
))
client.create_endpoint("wait", backend="wait")
handle = client.get_handle("wait")
refs = [handle.remote(signal) for _ in range(10)]
# Wait for all the queries to be enqueued
with pytest.raises(ray.exceptions.GetTimeoutError):
ray.get(refs, timeout=1)
@ray.remote(num_cpus=0)
def do_blocking_delete():
client = serve.connect()
client.delete_endpoint("wait")
client.delete_backend("wait")
# Now delete the backend. This should trigger the shutdown sequence.
delete_ref = do_blocking_delete.remote()
# The queries should be enqueued but not executed becuase they are blocked
# by signal actor.
with pytest.raises(ray.exceptions.GetTimeoutError):
ray.get(refs, timeout=1)
signal.send.remote()
# All the queries should be drained and executed without error.
ray.get(refs)
# Blocking delete should complete.
ray.get(delete_ref)
async def update_backend_config(self, backend_tag: BackendTag,
config_options: BackendConfig) -> GoalId:
"""Set the config for the specified backend."""
async with self.write_lock:
existing_backend_info = self.backend_state.get_backend(backend_tag)
if existing_backend_info is None:
raise ValueError(f"Backend {backend_tag} is not registered.")
existing_replica_config = existing_backend_info.replica_config
new_backend_config = existing_backend_info.backend_config.copy(
update=config_options.dict(exclude_unset=True))
return self.backend_state.deploy_backend(
backend_tag, new_backend_config, existing_replica_config)
async def __init__(self, backend_tag, replica_tag, init_args,
init_kwargs, backend_config_proto_bytes: bytes,
version: BackendVersion, controller_name: str,
detached: bool):
backend = cloudpickle.loads(serialized_backend_def)
backend_config = BackendConfig.from_proto_bytes(
backend_config_proto_bytes)
if inspect.isfunction(backend):
is_function = True
elif inspect.isclass(backend):
is_function = False
else:
assert False, ("backend_def must be function, class, or "
"corresponding import path.")
# Set the controller name so that serve.connect() in the user's
# backend code will connect to the instance that this backend is
# running in.
ray.serve.api._set_internal_replica_context(backend_tag,
replica_tag,
controller_name,
servable_object=None)
if is_function:
_callable = backend
else:
# This allows backends to define an async __init__ method
# (required for FastAPI backend definition).
_callable = backend.__new__(backend)
await sync_to_async(_callable.__init__)(*init_args,
**init_kwargs)
# Setting the context again to update the servable_object.
ray.serve.api._set_internal_replica_context(
backend_tag,
replica_tag,
controller_name,
servable_object=_callable)
assert controller_name, "Must provide a valid controller_name"
controller_namespace = ray.serve.api._get_controller_namespace(
detached)
controller_handle = ray.get_actor(controller_name,
namespace=controller_namespace)
self.backend = RayServeReplica(_callable, backend_tag, replica_tag,
backend_config,
backend_config.user_config, version,
is_function, controller_handle)
# asyncio.Event used to signal that the replica is shutting down.
self.shutdown_event = asyncio.Event()
def test_backend_user_config(serve_instance):
config = BackendConfig(num_replicas=2, user_config={"count": 123, "b": 2})
@serve.deployment("counter", config=config)
class Counter:
def __init__(self):
self.count = 10
def __call__(self, starlette_request):
return self.count, os.getpid()
def reconfigure(self, config):
self.count = config["count"]
Counter.deploy()
handle = Counter.get_handle()
def check(val, num_replicas):
pids_seen = set()
for i in range(100):
result = ray.get(handle.remote())
if str(result[0]) != val:
return False
pids_seen.add(result[1])
return len(pids_seen) == num_replicas
wait_for_condition(lambda: check("123", 2))
config.num_replicas = 3
Counter = Counter.options(config=config)
Counter.deploy()
wait_for_condition(lambda: check("123", 3))
config.user_config = {"count": 456}
Counter = Counter.options(config=config)
Counter.deploy()
wait_for_condition(lambda: check("456", 3))
def create_backend(backend_tag: str,
func_or_class: Union[Callable, Type[Callable]],
*actor_init_args: Any,
ray_actor_options: Optional[Dict] = None,
config: Optional[Dict[str, Any]] = None) -> None:
"""Create a backend with the provided tag.
The backend will serve requests with func_or_class.
Args:
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 (optional): configuration options for this backend.
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.
"""
if backend_tag in list_backends():
raise ValueError(
"Cannot create backend. "
"Backend '{}' is already registered.".format(backend_tag))
if config is None:
config = {}
if not isinstance(config, dict):
raise TypeError("config must be a dictionary.")
replica_config = ReplicaConfig(func_or_class,
*actor_init_args,
ray_actor_options=ray_actor_options)
backend_config = BackendConfig(config, replica_config.accepts_batches,
replica_config.is_blocking)
ray.get(
controller.create_backend.remote(backend_tag, backend_config,
replica_config))
def test_backend_config_validation():
# Test unknown key.
with pytest.raises(ValueError, match="unknown_key"):
BackendConfig({"unknown_key": -1})
# Test num_replicas validation.
BackendConfig({"num_replicas": 1})
with pytest.raises(TypeError):
BackendConfig({"num_replicas": "hello"})
with pytest.raises(ValueError):
BackendConfig({"num_replicas": -1})
# Test max_batch_size validation.
BackendConfig({"max_batch_size": 10}, accepts_batches=True)
with pytest.raises(ValueError):
BackendConfig({"max_batch_size": 10}, accepts_batches=False)
with pytest.raises(TypeError):
BackendConfig({"max_batch_size": 1.0})
with pytest.raises(TypeError):
BackendConfig({"max_batch_size": "hello"})
with pytest.raises(ValueError):
BackendConfig({"max_batch_size": 0})
with pytest.raises(ValueError):
BackendConfig({"max_batch_size": -1})
def test_imported_backend(serve_instance):
client = serve_instance
backend_class = ImportedBackend("ray.serve.utils.MockImportedBackend")
config = BackendConfig(user_config="config", max_batch_size=2)
client.create_backend(
"imported", backend_class, "input_arg", config=config)
client.create_endpoint("imported", backend="imported")
# Basic sanity check.
handle = client.get_handle("imported")
assert ray.get(handle.remote()) == {"arg": "input_arg", "config": "config"}
# Check that updating backend config works.
client.update_backend_config(
"imported", BackendConfig(user_config="new_config"))
assert ray.get(handle.remote()) == {
"arg": "input_arg",
"config": "new_config"
}
# Check that other call methods work.
handle = handle.options(method_name="other_method")
assert ray.get(handle.remote("hello")) == "hello"
def test_list_backends(serve_instance):
def f():
pass
config1 = BackendConfig(max_concurrent_queries=10)
serve.create_backend("backend", f, config=config1)
backends = serve.list_backends()
assert len(backends) == 1
assert "backend" in backends
assert backends["backend"].max_concurrent_queries == 10
config2 = BackendConfig(num_replicas=10)
serve.create_backend("backend2", f, config=config2)
backends = serve.list_backends()
assert len(backends) == 2
assert backends["backend2"].num_replicas == 10
serve.delete_backend("backend")
backends = serve.list_backends()
assert len(backends) == 1
assert "backend2" in backends
serve.delete_backend("backend2")
assert len(serve.list_backends()) == 0
async def test_task_runner_perform_batch(serve_instance, router):
def batcher(requests):
batch_size = len(requests)
return [batch_size] * batch_size
config = BackendConfig(
max_batch_size=2,
batch_wait_timeout=10,
internal_metadata=BackendMetadata(accepts_batches=True))
_ = await add_servable_to_router(batcher, router, backend_config=config)
query_param = make_request_param()
my_batch_sizes = await asyncio.gather(
*[router.enqueue_request.remote(query_param) for _ in range(3)])
assert my_batch_sizes == [2, 2, 1]
def deploy(
self,
name: str,
backend_config_proto_bytes: bytes,
replica_config: ReplicaConfig,
version: Optional[str],
prev_version: Optional[str],
route_prefix: Optional[str],
deployer_job_id: "Optional[ray._raylet.JobID]" = None
) -> Tuple[Optional[GoalId], bool]:
if route_prefix is not None:
assert route_prefix.startswith("/")
backend_config = BackendConfig.from_proto_bytes(
backend_config_proto_bytes)
if prev_version is not None:
existing_backend_info = self.backend_state_manager.get_backend(
name)
if (existing_backend_info is None
or not existing_backend_info.version):
raise ValueError(
f"prev_version '{prev_version}' is specified but "
"there is no existing deployment.")
if existing_backend_info.version != prev_version:
raise ValueError(f"prev_version '{prev_version}' "
"does not match with the existing "
f"version '{existing_backend_info.version}'.")
backend_info = BackendInfo(actor_def=ray.remote(
create_replica_wrapper(name,
replica_config.serialized_backend_def)),
version=version,
backend_config=backend_config,
replica_config=replica_config,
deployer_job_id=deployer_job_id,
start_time_ms=int(time.time() * 1000))
# TODO(architkulkarni): When a deployment is redeployed, even if
# the only change was num_replicas, the start_time_ms is refreshed.
# This is probably not the desired behavior for an autoscaling
# deployment, which redeploys very often to change num_replicas.
goal_id, updating = self.backend_state_manager.deploy_backend(
name, backend_info)
endpoint_info = EndpointInfo(route=route_prefix)
self.endpoint_state.update_endpoint(name, endpoint_info)
return goal_id, updating
async def deploy(
self,
name: str,
backend_config_proto_bytes: bytes,
replica_config: ReplicaConfig,
python_methods: List[str],
version: Optional[str],
prev_version: Optional[str],
route_prefix: Optional[str],
deployer_job_id: "Optional[ray._raylet.JobID]" = None
) -> Tuple[Optional[GoalId], bool]:
if route_prefix is not None:
assert route_prefix.startswith("/")
backend_config = BackendConfig.from_proto_bytes(
backend_config_proto_bytes)
async with self.write_lock:
if prev_version is not None:
existing_backend_info = self.backend_state_manager.get_backend(
name)
if (existing_backend_info is None
or not existing_backend_info.version):
raise ValueError(
f"prev_version '{prev_version}' is specified but "
"there is no existing deployment.")
if existing_backend_info.version != prev_version:
raise ValueError(
f"prev_version '{prev_version}' "
"does not match with the existing "
f"version '{existing_backend_info.version}'.")
backend_info = BackendInfo(actor_def=ray.remote(
create_backend_replica(name,
replica_config.serialized_backend_def)),
version=version,
backend_config=backend_config,
replica_config=replica_config,
deployer_job_id=deployer_job_id,
start_time_ms=int(time.time() * 1000))
goal_id, updating = self.backend_state_manager.deploy_backend(
name, backend_info)
endpoint_info = EndpointInfo(route=route_prefix,
python_methods=python_methods)
self.endpoint_state.update_endpoint(name, endpoint_info)
return goal_id, updating
def test_serve_forceful_shutdown(serve_instance):
def sleeper(_):
while True:
time.sleep(1000)
serve.create_backend(
"sleeper",
sleeper,
config=BackendConfig(experimental_graceful_shutdown_timeout_s=1))
serve.create_endpoint("sleeper", backend="sleeper")
handle = serve.get_handle("sleeper")
ref = handle.remote()
serve.delete_endpoint("sleeper")
serve.delete_backend("sleeper")
with pytest.raises(ray.exceptions.RayActorError):
ray.get(ref)
def test_batching_exception(serve_instance):
class NoListReturned:
def __init__(self):
self.count = 0
@serve.accept_batch
def __call__(self, requests):
return len(requests)
# Set the max batch size.
config = BackendConfig(max_batch_size=5)
serve.create_backend("exception:v1", NoListReturned, config=config)
serve.create_endpoint("exception-test", backend="exception:v1")
handle = serve.get_handle("exception-test")
with pytest.raises(ray.exceptions.RayTaskError):
assert ray.get(handle.remote(temp=1))
async def update_backend_config(self, backend_tag: BackendTag,
config_options: BackendConfig) -> GoalId:
"""Set the config for the specified backend."""
async with self.write_lock:
existing_info = self.backend_state.get_backend(backend_tag)
if existing_info is None:
raise ValueError(f"Backend {backend_tag} is not registered.")
backend_info = BackendInfo(
actor_def=existing_info.actor_def,
version=existing_info.version,
backend_config=existing_info.backend_config.copy(
update=config_options.dict(exclude_unset=True)),
replica_config=existing_info.replica_config)
goal_id, _ = self.backend_state.deploy_backend(
backend_tag, backend_info)
return goal_id
def add_new_replica(self,
backend_tag,
runner_actor,
backend_config=BackendConfig()):
self.backend_replicas[backend_tag].append(runner_actor)
self.backend_configs[backend_tag] = backend_config
ray.get(runner_actor.reconfigure.remote(
backend_config.user_config))
self.host.notify_changed(
(LongPollNamespace.REPLICA_HANDLES, backend_tag),
self.backend_replicas[backend_tag],
)
self.host.notify_changed(
(LongPollNamespace.BACKEND_CONFIGS, backend_tag),
self.backend_configs[backend_tag],
)
async def test_task_runner_perform_batch(serve_instance,
mock_controller_with_name):
def batcher(requests):
batch_size = len(requests)
return [batch_size] * batch_size
config = BackendConfig(
max_batch_size=2,
batch_wait_timeout=10,
internal_metadata=BackendMetadata(accepts_batches=True))
worker, router = await add_servable_to_router(batcher,
*mock_controller_with_name,
backend_config=config)
query_param = make_request_param()
my_batch_sizes = await asyncio.gather(
*[(await router.assign_request(query_param)) for _ in range(3)])
assert my_batch_sizes == [2, 2, 1]
def test_batching_exception(serve_instance):
class NoListReturned:
def __init__(self):
self.count = 0
@serve.accept_batch
def __call__(self, flask_request, temp=None):
batch_size = serve.context.batch_size
return batch_size
# set the max batch size
serve.create_backend(
"exception:v1", NoListReturned, config=BackendConfig(max_batch_size=5))
serve.create_endpoint(
"exception-test", backend="exception:v1", route="/noListReturned")
handle = serve.get_handle("exception-test")
with pytest.raises(ray.exceptions.RayTaskError):
assert ray.get(handle.remote(temp=1))
