def create_backend(func_or_class,
backend_tag,
*actor_init_args,
backend_config=None):
"""Create a backend using func_or_class and assign backend_tag.
Args:
func_or_class (callable, class): a function or a class implements
__call__ protocol.
backend_tag (str): a unique tag assign to this backend. It will be used
to associate services in traffic policy.
backend_config (BackendConfig): An object defining backend properties
for starting a backend.
*actor_init_args (optional): the argument to pass to the class
initialization method.
"""
# Configure backend_config
if backend_config is None:
backend_config = BackendConfig()
assert isinstance(backend_config,
BackendConfig), ("backend_config must be"
" of instance BackendConfig")
# Make sure the batch size is correct
should_accept_batch = backend_config.max_batch_size is not None
if should_accept_batch and not _backend_accept_batch(func_or_class):
raise batch_annotation_not_found
if _backend_accept_batch(func_or_class):
backend_config.has_accept_batch_annotation = True
arg_list = []
if inspect.isfunction(func_or_class):
# arg list for a fn is function itself
arg_list = [func_or_class]
# ignore lint on lambda expression
creator = lambda kwrgs: TaskRunnerActor._remote(**kwrgs) # noqa: E731
elif inspect.isclass(func_or_class):
# Python inheritance order is right-to-left. We put RayServeMixin
# on the left to make sure its methods are not overriden.
@ray.remote
class CustomActor(RayServeMixin, func_or_class):
@wraps(func_or_class.__init__)
def __init__(self, *args, **kwargs):
# Initialize serve so it can be used in backends.
init()
super().__init__(*args, **kwargs)
arg_list = actor_init_args
# ignore lint on lambda expression
creator = lambda kwargs: CustomActor._remote(**kwargs) # noqa: E731
else:
raise TypeError(
"Backend must be a function or class, it is {}.".format(
type(func_or_class)))
ray.get(
master_actor.create_backend.remote(backend_tag, creator,
backend_config, arg_list))
def create_backend(func_or_class,
backend_tag,
*actor_init_args,
backend_config=None):
"""Create a backend using func_or_class and assign backend_tag.
Args:
func_or_class (callable, class): a function or a class implementing
__call__.
backend_tag (str): a unique tag assign to this backend. It will be used
to associate services in traffic policy.
backend_config (BackendConfig): An object defining backend properties
for starting a backend.
*actor_init_args (optional): the argument to pass to the class
initialization method.
"""
# Configure backend_config
if backend_config is None:
backend_config = BackendConfig()
assert isinstance(backend_config,
BackendConfig), ("backend_config must be"
" of instance BackendConfig")
# Validate that func_or_class is a function or class.
if inspect.isfunction(func_or_class):
if len(actor_init_args) != 0:
raise ValueError(
"actor_init_args not supported for function backend.")
elif not inspect.isclass(func_or_class):
raise ValueError(
"Backend must be a function or class, it is {}.".format(
type(func_or_class)))
# Make sure the batch size is correct.
should_accept_batch = backend_config.max_batch_size is not None
if should_accept_batch and not _backend_accept_batch(func_or_class):
raise batch_annotation_not_found
if _backend_accept_batch(func_or_class):
backend_config.has_accept_batch_annotation = True
ray.get(
master_actor.create_backend.remote(backend_tag, backend_config,
func_or_class, actor_init_args))
def create_backend(func_or_class,
backend_tag,
*actor_init_args,
backend_config=None):
"""Create a backend using func_or_class and assign backend_tag.
Args:
func_or_class (callable, class): a function or a class implements
__call__ protocol.
backend_tag (str): a unique tag assign to this backend. It will be used
to associate services in traffic policy.
backend_config (BackendConfig): An object defining backend properties
for starting a backend.
*actor_init_args (optional): the argument to pass to the class
initialization method.
"""
# Configure backend_config
if backend_config is None:
backend_config = BackendConfig()
assert isinstance(backend_config,
BackendConfig), ("backend_config must be"
" of instance BackendConfig")
# Make sure the batch size is correct
should_accept_batch = (True if backend_config.max_batch_size is not None
else False)
if should_accept_batch and not _backend_accept_batch(func_or_class):
raise batch_annotation_not_found
if _backend_accept_batch(func_or_class):
backend_config.has_accept_batch_annotation = True
arg_list = []
if inspect.isfunction(func_or_class):
# arg list for a fn is function itself
arg_list = [func_or_class]
# ignore lint on lambda expression
creator = lambda kwrgs: TaskRunnerActor._remote(**kwrgs) # noqa: E731
elif inspect.isclass(func_or_class):
# Python inheritance order is right-to-left. We put RayServeMixin
# on the left to make sure its methods are not overriden.
@ray.remote
class CustomActor(RayServeMixin, func_or_class):
pass
arg_list = actor_init_args
# ignore lint on lambda expression
creator = lambda kwargs: CustomActor._remote(**kwargs) # noqa: E731
else:
raise TypeError(
"Backend must be a function or class, it is {}.".format(
type(func_or_class)))
backend_config_dict = dict(backend_config)
# save creator which starts replicas
global_state.backend_table.register_backend(backend_tag, creator)
# save information about configurations needed to start the replicas
global_state.backend_table.register_info(backend_tag, backend_config_dict)
# save the initial arguments needed by replicas
global_state.backend_table.save_init_args(backend_tag, arg_list)
# set the backend config inside the router
# particularly for max-batch-size
ray.get(global_state.init_or_get_router().set_backend_config.remote(
backend_tag, backend_config_dict))
_scale(backend_tag, backend_config_dict["num_replicas"])