91 lines
2.3 KiB
Python
91 lines
2.3 KiB
Python
import os
|
|
import time
|
|
|
|
import tqdm
|
|
from many_nodes_tests.dashboard_test import DashboardTestAtScale
|
|
|
|
import ray
|
|
import ray._common.test_utils
|
|
import ray._private.test_utils as test_utils
|
|
from ray._private.state_api_test_utils import summarize_worker_startup_time
|
|
|
|
is_smoke_test = True
|
|
if "SMOKE_TEST" in os.environ:
|
|
MAX_ACTORS_IN_CLUSTER = 100
|
|
else:
|
|
MAX_ACTORS_IN_CLUSTER = 10000
|
|
is_smoke_test = False
|
|
|
|
|
|
def test_max_actors():
|
|
# TODO (Alex): Dynamically set this based on number of cores
|
|
cpus_per_actor = 0.25
|
|
|
|
@ray.remote(num_cpus=cpus_per_actor)
|
|
class Actor:
|
|
def foo(self):
|
|
pass
|
|
|
|
actors = [
|
|
Actor.remote()
|
|
for _ in tqdm.trange(MAX_ACTORS_IN_CLUSTER, desc="Launching actors")
|
|
]
|
|
|
|
done = ray.get([actor.foo.remote() for actor in actors])
|
|
for result in done:
|
|
assert result is None
|
|
|
|
|
|
def no_resource_leaks():
|
|
return test_utils.no_resource_leaks_excluding_node_resources()
|
|
|
|
|
|
addr = ray.init(address="auto")
|
|
|
|
ray._common.test_utils.wait_for_condition(no_resource_leaks)
|
|
monitor_actor = test_utils.monitor_memory_usage()
|
|
dashboard_test = DashboardTestAtScale(addr)
|
|
|
|
start_time = time.time()
|
|
test_max_actors()
|
|
end_time = time.time()
|
|
|
|
ray.get(monitor_actor.stop_run.remote())
|
|
used_gb, usage = ray.get(monitor_actor.get_peak_memory_info.remote())
|
|
print(f"Peak memory usage: {round(used_gb, 2)}GB")
|
|
print(f"Peak memory usage per processes:\n {usage}")
|
|
del monitor_actor
|
|
|
|
# Get the dashboard result
|
|
ray._common.test_utils.wait_for_condition(no_resource_leaks)
|
|
|
|
rate = MAX_ACTORS_IN_CLUSTER / (end_time - start_time)
|
|
try:
|
|
summarize_worker_startup_time()
|
|
except Exception as e:
|
|
print("Failed to summarize worker startup time.")
|
|
print(e)
|
|
|
|
print(
|
|
f"Success! Started {MAX_ACTORS_IN_CLUSTER} actors in "
|
|
f"{end_time - start_time}s. ({rate} actors/s)"
|
|
)
|
|
|
|
results = {
|
|
"actors_per_second": rate,
|
|
"num_actors": MAX_ACTORS_IN_CLUSTER,
|
|
"time": end_time - start_time,
|
|
"_peak_memory": round(used_gb, 2),
|
|
"_peak_process_memory": usage,
|
|
}
|
|
if not is_smoke_test:
|
|
results["perf_metrics"] = [
|
|
{
|
|
"perf_metric_name": "actors_per_second",
|
|
"perf_metric_value": rate,
|
|
"perf_metric_type": "THROUGHPUT",
|
|
}
|
|
]
|
|
dashboard_test.update_release_test_result(results)
|
|
test_utils.safe_write_to_results_json(results)
|