mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
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"""Fault tolerance test (small cluster, non-elastic training)
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In this run, two training actors will die after some time. It is expected that
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in both cases lightgbm_ray stops training, restarts the dead actors, and
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continues training with all four actors.
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Test owner: Yard1 (primary), krfricke
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Acceptance criteria: Should run through and report final results. Intermediate
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output should show that training halts wenn an actor dies and continues only
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when all four actors are available again. The test will fail if fault
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tolerance did not work correctly.
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Notes: This test seems to be somewhat flaky. This might be due to
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race conditions in handling dead actors. This is likely a problem of
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the lightgbm_ray implementation and not of this test.
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"""
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import ray
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from lightgbm_ray import RayParams
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from ray.util.lightgbm.release_test_util import train_ray, \
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FailureState, FailureInjection, TrackingCallback
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if __name__ == "__main__":
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ray.init(address="auto")
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failure_state = FailureState.remote()
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ray_params = RayParams(
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max_actor_restarts=2, num_actors=4, cpus_per_actor=4, gpus_per_actor=0)
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_, additional_results, _ = train_ray(
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path="/data/classification.parquet",
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num_workers=4,
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num_boost_rounds=100,
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num_files=200,
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regression=False,
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use_gpu=False,
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ray_params=ray_params,
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lightgbm_params=None,
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callbacks=[
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TrackingCallback(),
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FailureInjection(
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id="first_fail", state=failure_state, ranks=[1], iteration=14),
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FailureInjection(
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id="second_fail", state=failure_state, ranks=[0], iteration=34)
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])
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print("PASSED.")
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