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https://github.com/vale981/ray
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140a180ebb
Xgboosts train_small timed out because of a CPU borrowing feature related to placement groups. The root bug will be fixed in the coming weeks, but this PR makes the release test consistently pass by requesting 0 CPUs for the remote wrapper script.
67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
"""Small cluster training
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This training run will start 4 workers on 4 nodes (including head node).
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Test owner: Yard1 (primary), krfricke
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Acceptance criteria: Should run through and report final results.
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"""
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import json
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import os
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import time
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import ray
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from ray._private.test_utils import wait_for_num_nodes
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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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if __name__ == "__main__":
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addr = os.environ.get("RAY_ADDRESS")
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job_name = os.environ.get("RAY_JOB_NAME", "train_small")
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if addr.startswith("anyscale://"):
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ray.init(address=addr, job_name=job_name)
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else:
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ray.init(address="auto")
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wait_for_num_nodes(
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int(os.environ.get("RAY_RELEASE_MIN_WORKERS", 0)) + 1, 600)
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output = os.environ["TEST_OUTPUT_JSON"]
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state = os.environ["TEST_STATE_JSON"]
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ray_params = RayParams(
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elastic_training=False,
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max_actor_restarts=2,
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num_actors=4,
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cpus_per_actor=4,
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gpus_per_actor=0)
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start = time.time()
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@ray.remote(num_cpus=0)
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def train():
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os.environ["TEST_OUTPUT_JSON"] = output
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os.environ["TEST_STATE_JSON"] = state
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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=25,
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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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)
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ray.get(train.remote())
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taken = time.time() - start
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result = {
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"time_taken": taken,
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}
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test_output_json = os.environ.get("TEST_OUTPUT_JSON",
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"/tmp/train_small.json")
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with open(test_output_json, "wt") as f:
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json.dump(result, f)
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print("PASSED.")
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