def test_batching(serve_instance):
class BatchingExample:
def __init__(self):
self.count = 0
@serve.accept_batch
def __call__(self, flask_request, temp=None):
self.count += 1
batch_size = serve.context.batch_size
return [self.count] * batch_size
serve.create_endpoint("counter1", "/increment")
# Keep checking the routing table until /increment is populated
while "/increment" not in requests.get("http://127.0.0.1:8000/").json():
time.sleep(0.2)
# set the max batch size
b_config = BackendConfig(max_batch_size=5)
serve.create_backend(BatchingExample,
"counter:v11",
backend_config=b_config)
serve.link("counter1", "counter:v11")
future_list = []
handle = serve.get_handle("counter1")
for _ in range(20):
f = handle.remote(temp=1)
future_list.append(f)
counter_result = ray.get(future_list)
# since count is only updated per batch of queries
# If there atleast one __call__ fn call with batch size greater than 1
# counter result will always be less than 20
assert max(counter_result) < 20
def test_new_driver(serve_instance):
script = """
import ray
ray.init(address="auto")
from ray.experimental import serve
serve.init()
@serve.route("/driver")
def driver(flask_request):
return "OK!"
"""
with tempfile.NamedTemporaryFile(mode="w", delete=False) as f:
path = f.name
f.write(script)
proc = subprocess.Popen(["python", path])
return_code = proc.wait(timeout=10)
assert return_code == 0
handle = serve.get_handle("driver")
assert ray.get(handle.remote()) == "OK!"
os.remove(path)
def test_no_route(serve_instance):
serve.create_endpoint("noroute-endpoint", blocking=True)
global_state = serve.api._get_global_state()
result = global_state.route_table.list_service(include_headless=True)
assert result[NO_ROUTE_KEY] == ["noroute-endpoint"]
without_headless_result = global_state.route_table.list_service()
assert NO_ROUTE_KEY not in without_headless_result
def func(_, i=1):
return 1
serve.create_backend(func, "backend:1")
serve.link("noroute-endpoint", "backend:1")
service_handle = serve.get_handle("noroute-endpoint")
result = ray.get(service_handle.remote(i=1))
assert result == 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
serve.create_endpoint("exception-test", "/noListReturned")
# set the max batch size
b_config = BackendConfig(max_batch_size=5)
serve.create_backend(NoListReturned,
"exception:v1",
backend_config=b_config)
serve.link("exception-test", "exception:v1")
handle = serve.get_handle("exception-test")
with pytest.raises(ray.exceptions.RayTaskError):
assert ray.get(handle.remote(temp=1))
import time
import requests
import ray
from ray.experimental import serve
from ray.experimental.serve.utils import pformat_color_json
def echo(_):
raise Exception("Something went wrong...")
serve.init(blocking=True)
serve.create_endpoint("my_endpoint", "/echo", blocking=True)
serve.create_backend(echo, "echo:v1")
serve.link("my_endpoint", "echo:v1")
for _ in range(2):
resp = requests.get("http://127.0.0.1:8000/echo").json()
print(pformat_color_json(resp))
print("...Sleeping for 2 seconds...")
time.sleep(2)
handle = serve.get_handle("my_endpoint")
print("Invoke from python will raise exception with traceback:")
ray.get(handle.remote())
serve.create_endpoint("ECG")
# create data point service for hospital
serve.create_endpoint("hospital",
route="/hospital",
kwargs_creator=kwargs_creator)
# create backend for ECG
b_config = BackendConfig(num_replicas=1)
serve.create_backend(PytorchPredictorECG,
"PredictECG",
model,
cuda,
backend_config=b_config)
# link service and backend
serve.link("ECG", "PredictECG")
handle = serve.get_handle("ECG")
# prepare args for StorePatientData backend.
service_handles_dict = {"ECG": handle}
# do prediction after every 3750 queries.
num_queries_dict = {"ECG": 3750}
# Always keep num_replicas as 1 as this is a stateful Backend
# This backend will store all the patient's data and transfer
# the prediction to respective Backend (ECG handle in this case)
b_config_hospital = BackendConfig(num_replicas=1)
serve.create_backend(StorePatientData,
"StoreData",
service_handles_dict,
num_queries_dict,
backend_config=b_config_hospital)
serve.link("hospital", "StoreData")
else:
result = []
for b in base_number:
ans = b + self.increment
result.append(ans)
return result
serve.init(blocking=True)
serve.create_endpoint("magic_counter", "/counter", blocking=True)
b_config = BackendConfig(max_batch_size=5)
serve.create_backend(MagicCounter, "counter:v1", 42,
backend_config=b_config) # increment=42
serve.link("magic_counter", "counter:v1")
print("Sending ten queries via HTTP")
for i in range(10):
url = "http://127.0.0.1:8000/counter?base_number={}".format(i)
print("> Pinging {}".format(url))
resp = requests.get(url).json()
print(pformat_color_json(resp))
time.sleep(0.2)
print("Sending ten queries via Python")
handle = serve.get_handle("magic_counter")
for i in range(10):
print("> Pinging handle.remote(base_number={})".format(i))
result = ray.get(handle.remote(base_number=i))
print("< Result {}".format(result))
/ \
/ \
/ \
/ \
"my_endpoint2" "my_endpoint3"
\ /
\ /
\ /
\ /
\ /
\ /
\/
"my_endpoint4"
"""
# get the handle of the endpoints
handle1 = serve.get_handle("echo_v1")
handle2 = serve.get_handle("echo_v2")
handle3 = serve.get_handle("echo_v3")
handle4 = serve.get_handle("echo_v4")
start = time.time()
print("Start firing to the pipeline: {} s".format(time.time()))
handle1_oid = handle1.remote(response="hello")
handle4_oid = handle4.remote(relay1=handle2.remote(relay=handle1_oid),
relay2=handle3.remote(relay=handle1_oid))
print("Firing ended now waiting for the result,"
"time taken: {} s".format(time.time() - start))
result = ray.get(handle4_oid)
print("Result: {}, time taken: {} s".format(result, time.time() - start))
def echo_v1(flask_request, response="hello from python!"):
if serve.context.web:
response = flask_request.url
return response
serve.create_backend(echo_v1, "echo:v1")
# We can link an endpoint to a backend, the means all the traffic
# goes to my_endpoint will now goes to echo:v1 backend.
serve.link("my_endpoint", "echo:v1")
print(requests.get("http://127.0.0.1:8000/echo").json())
# The service will be reachable from http
print(ray.get(serve.get_handle("my_endpoint").remote(response="hello")))
# as well as within the ray system.
# We can also add a new backend and split the traffic.
def echo_v2(flask_request):
# magic, only from web.
return "something new"
serve.create_backend(echo_v2, "echo:v2")
# The two backend will now split the traffic 50%-50%.
serve.split("my_endpoint", {"echo:v1": 0.5, "echo:v2": 0.5})
def benchmark(func, name):
for _ in range(NUM_WARMUPS):
func()
for _ in range(NUM_REPEATS):
with profile(name):
func()
def work(_):
time.sleep(0.05)
@ray.remote
def work_ray():
time.sleep(0.05)
serve.init()
serve.create_endpoint('sleep', '/')
serve.create_backend(work, 'sleep:v1')
serve.link('sleep', 'sleep:v1')
handle = serve.get_handle('sleep')
benchmark(lambda: ray.get(handle.remote()), "serve_sleep")
benchmark(lambda: ray.get(work_ray.remote()), "ray_sleep")
summarize_profile()
