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53641f1f74
* move more unit tests to bazel * move to avoid conflict * fix lint * fix deps * seprate * fix failing tests * show tests * ignore mismatch * try combining bazel runs * build lint * remove tests from install * fix test utils * better config * split up * exclusive * fix verbosity * fix tests class * cleanup * remove flaky * fix metrics test * Update .travis.yml * no retry flaky * split up actor * split basic test * split up trial runner test * split stress * fix basic test * fix tests * switch to pytest runner for main * make microbench not fail * move load code to py3 * test is no longer package * bazel to end
48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
"""This is the script for `ray clusterbenchmark`."""
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import time
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import numpy as np
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import ray
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from ray.cluster_utils import Cluster
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def main():
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cluster = Cluster(
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initialize_head=True,
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connect=True,
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head_node_args={
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"object_store_memory": 20 * 1024 * 1024 * 1024,
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"num_cpus": 16
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})
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cluster.add_node(
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object_store_memory=20 * 1024 * 1024 * 1024, num_gpus=1, num_cpus=16)
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object_id_list = []
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for i in range(0, 10):
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object_id = ray.put(np.random.rand(1024 * 128, 1024))
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object_id_list.append(object_id)
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@ray.remote(num_gpus=1)
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def f(object_id_list):
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diffs = []
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for object_id in object_id_list:
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before = time.time()
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ray.get(object_id)
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after = time.time()
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diffs.append(after - before)
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time.sleep(1)
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return np.mean(diffs), np.std(diffs)
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time_diff, time_diff_std = ray.get(f.remote(object_id_list))
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print("latency to get an 1G object over network", round(time_diff, 2),
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"+-", round(time_diff_std, 2))
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ray.shutdown()
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cluster.shutdown()
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if __name__ == "__main__":
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main()
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