def test_backend_config_update():
b = BackendConfig(num_replicas=1, max_concurrent_queries=1)
# Test updating a key works.
b.num_replicas = 2
assert b.num_replicas == 2
# Check that not specifying a key doesn't update it.
assert b.max_concurrent_queries == 1
# Check that input is validated.
with pytest.raises(ValidationError):
b.num_replicas = "Hello"
with pytest.raises(ValidationError):
b.num_replicas = -1
def test_backend_config_update():
b = BackendConfig(num_replicas=1, max_batch_size=1)
# Test updating a key works.
b.num_replicas = 2
assert b.num_replicas == 2
# Check that not specifying a key doesn't update it.
assert b.max_batch_size == 1
# Check that input is validated.
with pytest.raises(ValidationError):
b.num_replicas = "Hello"
with pytest.raises(ValidationError):
b.num_replicas = -1
# Test batch validation.
b = BackendConfig(internal_metadata=BackendMetadata(accepts_batches=False))
b.max_batch_size = 1
with pytest.raises(ValueError):
b.max_batch_size = 2
b._validate_complete()
b = BackendConfig(internal_metadata=BackendMetadata(accepts_batches=True))
b.max_batch_size = 2
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 deployment(
_func_or_class: Optional[Callable] = None,
name: Optional[str] = None,
version: Optional[str] = None,
prev_version: Optional[str] = None,
num_replicas: Optional[int] = None,
init_args: Optional[Tuple[Any]] = None,
route_prefix: Optional[str] = None,
ray_actor_options: Optional[Dict] = None,
user_config: Optional[Any] = None,
max_concurrent_queries: Optional[int] = None,
_autoscaling_config: Optional[Union[Dict, AutoscalingConfig]] = None,
) -> Callable[[Callable], Deployment]:
"""Define a Serve deployment.
Args:
name (Optional[str]): Globally-unique name identifying this deployment.
If not provided, the name of the class or function will be used.
version (Optional[str]): Version of the deployment. This is used to
indicate a code change for the deployment; when it is re-deployed
with a version change, a rolling update of the replicas will be
performed. If not provided, every deployment will be treated as a
new version.
prev_version (Optional[str]): Version of the existing deployment which
is used as a precondition for the next deployment. If prev_version
does not match with the existing deployment's version, the
deployment will fail. If not provided, deployment procedure will
not check the existing deployment's version.
num_replicas (Optional[int]): The number of processes to start up that
will handle requests to this deployment. Defaults to 1.
init_args (Optional[Tuple]): Arguments to be passed to the class
constructor when starting up deployment replicas. These can also be
passed when you call `.deploy()` on the returned Deployment.
route_prefix (Optional[str]): Requests to paths under this HTTP path
prefix will be routed to this deployment. Defaults to '/{name}'.
Routing is done based on longest-prefix match, so if you have
deployment A with a prefix of '/a' and deployment B with a prefix
of '/a/b', requests to '/a', '/a/', and '/a/c' go to A and requests
to '/a/b', '/a/b/', and '/a/b/c' go to B. Routes must not end with
a '/' unless they're the root (just '/'), which acts as a
catch-all.
ray_actor_options (dict): Options to be passed to the Ray actor
constructor such as resource requirements.
user_config (Optional[Any]): [experimental] Config to pass to the
reconfigure method of the deployment. This can be updated
dynamically without changing the version of the deployment and
restarting its replicas. The user_config needs to be hashable to
keep track of updates, so it must only contain hashable types, or
hashable types nested in lists and dictionaries.
max_concurrent_queries (Optional[int]): The maximum number of queries
that will be sent to a replica of this deployment without receiving
a response. Defaults to 100.
Example:
>>> @serve.deployment(name="deployment1", version="v1")
class MyDeployment:
pass
>>> MyDeployment.deploy(*init_args)
>>> MyDeployment.options(num_replicas=2, init_args=init_args).deploy()
Returns:
Deployment
"""
config = BackendConfig()
if num_replicas is not None:
config.num_replicas = num_replicas
if user_config is not None:
config.user_config = user_config
if max_concurrent_queries is not None:
config.max_concurrent_queries = max_concurrent_queries
if _autoscaling_config is not None:
config.autoscaling_config = _autoscaling_config
def decorator(_func_or_class):
return Deployment(
_func_or_class,
name if name is not None else _func_or_class.__name__,
config,
version=version,
prev_version=prev_version,
init_args=init_args,
route_prefix=route_prefix,
ray_actor_options=ray_actor_options,
_internal=True,
)
# This handles both parametrized and non-parametrized usage of the
# decorator. See the @serve.batch code for more details.
return decorator(_func_or_class) if callable(_func_or_class) else decorator
