Files
2026-07-13 13:17:40 +08:00

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)