mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
97b4490401
- Removing scale_to logic from object store. We don't need to scale during tests, which will disambiguate infra failures vs app failures. - Run microbenchmark in core nightly, meaning it will run even more often - Run weekly scalability tests daily instead. (They are not too expensive). - Run some core daily tests separately to avoid infra failures.
65 lines
1.4 KiB
Python
65 lines
1.4 KiB
Python
import numpy as np
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import ray
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import ray.autoscaler.sdk
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import json
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import os
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from time import perf_counter
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from tqdm import tqdm
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NUM_NODES = 50
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OBJECT_SIZE = 2**30
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def num_alive_nodes():
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n = 0
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for node in ray.nodes():
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if node["Alive"]:
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n += 1
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return n
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def test_object_broadcast():
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assert num_alive_nodes() == NUM_NODES
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@ray.remote(num_cpus=1, resources={"node": 1})
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class Actor:
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def foo(self):
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pass
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def sum(self, arr):
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return np.sum(arr)
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actors = [Actor.remote() for _ in range(NUM_NODES)]
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arr = np.ones(OBJECT_SIZE, dtype=np.uint8)
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ref = ray.put(arr)
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for actor in tqdm(actors, desc="Ensure all actors have started."):
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ray.get(actor.foo.remote())
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result_refs = []
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for actor in tqdm(actors, desc="Broadcasting objects"):
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result_refs.append(actor.sum.remote(ref))
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results = ray.get(result_refs)
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for result in results:
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assert result == OBJECT_SIZE
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ray.init(address="auto")
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start = perf_counter()
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test_object_broadcast()
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end = perf_counter()
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print(f"Broadcast time: {end - start} ({OBJECT_SIZE} B x {NUM_NODES} nodes)")
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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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"broadcast_time": end - start,
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"object_size": OBJECT_SIZE,
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"num_nodes": NUM_NODES,
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"success": "1"
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}
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json.dump(results, out_file)
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