mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
ba500133af
Fix the failure to unbreak nightly and unblock 1.13 release. The root cause is the upgrade of GRPC to 1.45.2 made it slightly slow; this is an acceptable regression which is needed to make this upgrade. Co-authored-by: Ubuntu <ubuntu@ip-172-31-32-136.us-west-2.compute.internal>
107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
import click
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import json
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import os
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import ray
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import ray._private.test_utils as test_utils
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import time
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import tqdm
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sleep_time = 300
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def test_max_running_tasks(num_tasks):
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cpus_per_task = 0.25
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@ray.remote(num_cpus=cpus_per_task)
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def task():
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time.sleep(sleep_time)
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def time_up(start_time):
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return time.time() - start_time >= sleep_time
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refs = [task.remote() for _ in tqdm.trange(num_tasks, desc="Launching tasks")]
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max_cpus = ray.cluster_resources()["CPU"]
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min_cpus_available = max_cpus
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start_time = time.time()
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for _ in tqdm.trange(int(sleep_time / 0.1), desc="Waiting"):
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try:
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cur_cpus = ray.available_resources().get("CPU", 0)
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min_cpus_available = min(min_cpus_available, cur_cpus)
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except Exception:
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# There are race conditions `.get` can fail if a new heartbeat
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# comes at the same time.
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pass
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if time_up(start_time):
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print(f"Time up for sleeping {sleep_time} seconds")
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break
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time.sleep(0.1)
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# There are some relevant magic numbers in this check. 10k tasks each
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# require 1/4 cpus. Therefore, ideally 2.5k cpus will be used.
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err_str = f"Only {max_cpus - min_cpus_available}/{max_cpus} cpus used."
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threshold = num_tasks * cpus_per_task * 0.70
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assert max_cpus - min_cpus_available > threshold, err_str
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for _ in tqdm.trange(num_tasks, desc="Ensuring all tasks have finished"):
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done, refs = ray.wait(refs)
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assert ray.get(done[0]) is None
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def no_resource_leaks():
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return test_utils.no_resource_leaks_excluding_node_resources()
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@click.command()
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@click.option("--num-tasks", required=True, type=int, help="Number of tasks to launch.")
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@click.option(
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"--smoke-test",
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is_flag=True,
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type=bool,
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default=False,
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help="If set, it's a smoke test",
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)
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def test(num_tasks, smoke_test):
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ray.init(address="auto")
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test_utils.wait_for_condition(no_resource_leaks)
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monitor_actor = test_utils.monitor_memory_usage()
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start_time = time.time()
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test_max_running_tasks(num_tasks)
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end_time = time.time()
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ray.get(monitor_actor.stop_run.remote())
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used_gb, usage = ray.get(monitor_actor.get_peak_memory_info.remote())
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print(f"Peak memory usage: {round(used_gb, 2)}GB")
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print(f"Peak memory usage per processes:\n {usage}")
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del monitor_actor
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test_utils.wait_for_condition(no_resource_leaks)
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rate = num_tasks / (end_time - start_time - sleep_time)
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print(
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f"Success! Started {num_tasks} tasks in {end_time - start_time}s. "
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f"({rate} tasks/s)"
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)
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if "TEST_OUTPUT_JSON" in os.environ:
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out_file = open(os.environ["TEST_OUTPUT_JSON"], "w")
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results = {
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"tasks_per_second": rate,
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"num_tasks": num_tasks,
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"time": end_time - start_time,
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"success": "1",
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"_peak_memory": round(used_gb, 2),
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"_peak_process_memory": usage,
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}
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if not smoke_test:
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results["perf_metrics"] = [
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{
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"perf_metric_name": "tasks_per_second",
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"perf_metric_value": rate,
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"perf_metric_type": "THROUGHPUT",
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}
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]
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json.dump(results, out_file)
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if __name__ == "__main__":
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test()
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