def invoke_single(self, request_item):
args, kwargs, is_web_context = parse_request_item(request_item)
serve_context.web = is_web_context
start_timestamp = time.time()
try:
result = self.__call__(*args, **kwargs)
except Exception as e:
result = wrap_to_ray_error(e)
self._serve_metric_error_counter += 1
self._serve_metric_latency_list.append(time.time() - start_timestamp)
return result
def invoke_single(self, request_item):
args, kwargs, is_web_context, result_object_id = parse_request_item(
request_item)
serve_context.web = is_web_context
start_timestamp = time.time()
try:
result = self.__call__(*args, **kwargs)
ray.worker.global_worker.put_object(result, result_object_id)
except Exception as e:
wrapped_exception = wrap_to_ray_error(e)
self._serve_metric_error_counter += 1
ray.worker.global_worker.put_object(wrapped_exception,
result_object_id)
self._serve_metric_latency_list.append(time.time() - start_timestamp)
def invoke_batch(self, request_item_list):
# TODO(alind) : create no-http services. The enqueues
# from such services will always be TaskContext.Python.
# Assumption : all the requests in a bacth
# have same serve context.
# For batching kwargs are modified as follows -
# kwargs [Python Context] : key,val
# kwargs_list : key, [val1,val2, ... , valn]
# or
# args[Web Context] : val
# args_list : [val1,val2, ...... , valn]
# where n (current batch size) <= max_batch_size of a backend
arg_list = []
kwargs_list = defaultdict(list)
context_flags = set()
batch_size = len(request_item_list)
for item in request_item_list:
args, kwargs, is_web_context = parse_request_item(item)
context_flags.add(is_web_context)
if is_web_context:
# Python context only have kwargs
flask_request = args[0]
arg_list.append(flask_request)
else:
# Web context only have one positional argument
for k, v in kwargs.items():
kwargs_list[k].append(v)
# Set the flask request as a list to conform
# with batching semantics: when in batching
# mode, each argument it turned into list.
arg_list.append(FakeFlaskRequest())
try:
# check mixing of query context
# unified context needed
if len(context_flags) != 1:
raise RayServeException(
"Batched queries contain mixed context. Please only send "
"the same type of requests in batching mode.")
serve_context.web = context_flags.pop()
serve_context.batch_size = batch_size
start_timestamp = time.time()
result_list = self.__call__(*args, **kwargs_list)
self._serve_metric_latency_list.append(time.time() -
start_timestamp)
if (not isinstance(result_list,
list)) or (len(result_list) != batch_size):
raise RayServeException("__call__ function "
"doesn't preserve batch-size. "
"Please return a list of result "
"with length equals to the batch "
"size.")
return result_list
except Exception as e:
wrapped_exception = wrap_to_ray_error(e)
self._serve_metric_error_counter += batch_size
return [wrapped_exception for _ in range(batch_size)]
def invoke_batch(self, request_item_list):
# TODO(alind) : create no-http services. The enqueues
# from such services will always be TaskContext.Python.
# Assumption : all the requests in a bacth
# have same serve context.
# For batching kwargs are modified as follows -
# kwargs [Python Context] : key,val
# kwargs_list : key, [val1,val2, ... , valn]
# or
# args[Web Context] : val
# args_list : [val1,val2, ...... , valn]
# where n (current batch size) <= max_batch_size of a backend
kwargs_list = defaultdict(list)
result_object_ids, context_flag_list, arg_list = [], [], []
curr_batch_size = len(request_item_list)
for item in request_item_list:
args, kwargs, is_web_context, result_object_id = (
parse_request_item(item))
context_flag_list.append(is_web_context)
# Python context only have kwargs
# Web context only have one positional argument
if is_web_context:
arg_list.append(args[0])
else:
for k, v in kwargs.items():
kwargs_list[k].append(v)
result_object_ids.append(result_object_id)
try:
# check mixing of query context
# unified context needed
if len(set(context_flag_list)) != 1:
raise RayServeException(
"Batched queries contain mixed context.")
serve_context.web = all(context_flag_list)
if serve_context.web:
args = (arg_list, )
else:
# Set the flask request as a list to conform
# with batching semantics: when in batching
# mode, each argument it turned into list.
fake_flask_request_lst = [
FakeFlaskRequest() for _ in range(curr_batch_size)
]
args = (fake_flask_request_lst, )
# set the current batch size (n) for serve_context
serve_context.batch_size = len(result_object_ids)
start_timestamp = time.time()
result_list = self.__call__(*args, **kwargs_list)
if (not isinstance(result_list, list)) or (len(result_list) !=
len(result_object_ids)):
raise RayServeException("__call__ function "
"doesn't preserve batch-size. "
"Please return a list of result "
"with length equals to the batch "
"size.")
for result, result_object_id in zip(result_list,
result_object_ids):
ray.worker.global_worker.put_object(result, result_object_id)
self._serve_metric_latency_list.append(time.time() -
start_timestamp)
except Exception as e:
wrapped_exception = wrap_to_ray_error(e)
self._serve_metric_error_counter += len(result_object_ids)
for result_object_id in result_object_ids:
ray.worker.global_worker.put_object(wrapped_exception,
result_object_id)