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https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
549527687f
This PR migrates scalability tests to the new infra. I had to copy the benchmarks folder to the release folder to make it work. I will remove some unnecessary files (e.g., benchmark.yaml or wait_for_cluster file) Alternatively we can support a different path than /release from the tool, but I think this way is cleaner. I am open to suggestion though cc @krfricke
95 lines
2.6 KiB
Python
95 lines
2.6 KiB
Python
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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from ray.util.placement_group import placement_group, remove_placement_group
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import time
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import tqdm
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if "SMOKE_TEST" in os.environ:
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MAX_PLACEMENT_GROUPS = 20
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else:
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MAX_PLACEMENT_GROUPS = 1000
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def test_many_placement_groups():
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# @ray.remote(num_cpus=1, resources={"node": 0.02})
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@ray.remote
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class C1:
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def ping(self):
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return "pong"
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# @ray.remote(num_cpus=1)
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@ray.remote
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class C2:
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def ping(self):
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return "pong"
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# @ray.remote(resources={"node": 0.02})
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@ray.remote
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class C3:
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def ping(self):
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return "pong"
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bundle1 = {"node": 0.02, "CPU": 1}
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bundle2 = {"CPU": 1}
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bundle3 = {"node": 0.02}
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pgs = []
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for _ in tqdm.trange(MAX_PLACEMENT_GROUPS, desc="Creating pgs"):
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pg = placement_group(bundles=[bundle1, bundle2, bundle3])
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pgs.append(pg)
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for pg in tqdm.tqdm(pgs, desc="Waiting for pgs to be ready"):
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ray.get(pg.ready())
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actors = []
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for pg in tqdm.tqdm(pgs, desc="Scheduling tasks"):
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actors.append(C1.options(placement_group=pg).remote())
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actors.append(C2.options(placement_group=pg).remote())
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actors.append(C3.options(placement_group=pg).remote())
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not_ready = [actor.ping.remote() for actor in actors]
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for _ in tqdm.trange(len(actors)):
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ready, not_ready = ray.wait(not_ready)
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assert ray.get(*ready) == "pong"
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for pg in tqdm.tqdm(pgs, desc="Cleaning up pgs"):
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remove_placement_group(pg)
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def no_resource_leaks():
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return ray.available_resources() == ray.cluster_resources()
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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_many_placement_groups()
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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 = MAX_PLACEMENT_GROUPS / (end_time - start_time)
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print(
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f"Success! Started {MAX_PLACEMENT_GROUPS} pgs in "
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f"{end_time - start_time}s. ({rate} pgs/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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"pgs_per_second": rate,
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"num_pgs": MAX_PLACEMENT_GROUPS,
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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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json.dump(results, out_file)
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