wehub-resource-sync
76 lines
1.8 KiB
Python
76 lines
1.8 KiB
Python
import json
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import os
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from time import perf_counter
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import numpy as np
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from tqdm import tqdm
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import ray
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import ray.autoscaler.sdk
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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 data_len(self, arr):
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return len(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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start = perf_counter()
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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.data_len.remote(ref))
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results = ray.get(result_refs)
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end = perf_counter()
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for result in results:
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assert result == OBJECT_SIZE
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return end - start
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ray.init(address="auto")
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duration = test_object_broadcast()
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print(f"Broadcast time: {duration} ({OBJECT_SIZE} B x {NUM_NODES} nodes)")
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if "TEST_OUTPUT_JSON" in os.environ:
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with open(os.environ["TEST_OUTPUT_JSON"], "w") as out_file:
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results = {
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"broadcast_time": duration,
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"object_size": OBJECT_SIZE,
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"num_nodes": NUM_NODES,
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}
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perf_metric_name = f"time_to_broadcast_{OBJECT_SIZE}_bytes_to_{NUM_NODES}_nodes"
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results["perf_metrics"] = [
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{
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"perf_metric_name": perf_metric_name,
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"perf_metric_value": duration,
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"perf_metric_type": "LATENCY",
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}
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]
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json.dump(results, out_file)
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