def init():
ray.init(num_cpus=4)
async_api.init()
asyncio.get_event_loop().set_debug(False)
yield
async_api.shutdown()
ray.shutdown()
def init():
ray.init(num_cpus=4)
async_api.init()
asyncio.get_event_loop().set_debug(False)
yield
async_api.shutdown()
ray.shutdown()
def init():
os.environ["RAY_FORCE_DIRECT"] = "0"
ray.init(num_cpus=4)
async_api.init()
asyncio.get_event_loop().set_debug(False)
yield
async_api.shutdown()
ray.shutdown()
async def test_asyncio_get(ray_start_regular_shared, event_loop):
loop = event_loop
asyncio.set_event_loop(loop)
loop.set_debug(True)
# This is needed for async plasma
from ray.experimental.async_api import init
init()
# Test Async Plasma
@ray.remote
def task():
return 1
assert await ray.async_compat.get_async(task.remote()) == 1
@ray.remote
def task_throws():
1 / 0
with pytest.raises(ray.exceptions.RayTaskError):
await ray.async_compat.get_async(task_throws.remote())
# Test actor calls.
str_len = 200 * 1024
@ray.remote
class Actor:
def echo(self, i):
return i
def big_object(self):
# 100Kb is the limit for direct call
return "a" * (str_len)
def throw_error(self):
1 / 0
actor = Actor.remote()
actor_call_future = ray.async_compat.get_async(actor.echo.remote(2))
assert await actor_call_future == 2
promoted_to_plasma_future = ray.async_compat.get_async(
actor.big_object.remote())
assert await promoted_to_plasma_future == "a" * str_len
with pytest.raises(ray.exceptions.RayTaskError):
await ray.async_compat.get_async(actor.throw_error.remote())
)
catalog_scrapper = CatalogScrapper(catalog)
producer = asyncio.create_task(catalog.run())
consumer = asyncio.create_task(catalog_scrapper.run())
await asyncio.wait([producer, consumer])
from .pages import MissingPageDownloader, WatchPageScrapper
pages = MissingPageDownloader(
encoding=const.HTML_ENCODING,
parallel_downloads=const.PAGES_PARALLEL_CONNECTIONS,
retry_after=const.RETRY_AFTER,
results_queue_maxsize=const.PAGES_PARSE_QUEUE,
)
pages_scrapper = WatchPageScrapper(pages)
producer = asyncio.create_task(pages.run())
consumer = asyncio.create_task(pages_scrapper.run())
await asyncio.wait([producer, consumer])
if __name__ == '__main__':
import asyncio
from ray.experimental import async_api as ray_async
import ray
ray.init(memory=1 / 2 * 1024 * 1024 * 1024, object_store_memory=1 / 2 * 1024 * 1024 * 1024)
ray_async.init()
asyncio.get_event_loop().run_until_complete(main())
)
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
previous_time = time.perf_counter()
def log_with_timestamp(msg):
global previous_time
now = time.perf_counter()
print(f"[delta_ms={(now-previous_time)*1000}] {msg}")
previous_time = now
ray.init()
async_api.init()
# Creating QueryFrontend
batchsize = 1
query_frontend = QueryFrontend.remote(batchsize)
query_frontend.loop.remote(query_frontend)
register_actor("query_frontend", query_frontend)
n_replicas = 3
query_frontend.add_model.remote("preprocess", ArmadaPreprocessModel)
for i in range(n_replicas):
query_frontend.add_model.remote(f"squeezenet_{i}", ArmadaSqueezenetModel)
query_frontend.add_model.remote("ensemble", ArmadaAverageEnsembleModel)
input_image = np.random.randn(1, 224, 224, 3)