mirror of
https://github.com/vale981/ray
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18d535f290
Note that LightGBM release tests were previously not enabled. https://buildkite.com/ray-project/release-tests-branch/builds/113 https://buildkite.com/ray-project/release-tests-branch/builds/114 Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
72 lines
1.8 KiB
Python
72 lines
1.8 KiB
Python
"""Moderate Ray Tune run (4 trials, 32 actors).
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This training run will start 4 Ray Tune trials, each starting 32 actors.
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The cluster comprises 32 nodes.
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Test owner: krfricke
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Acceptance criteria: Should run through and report final results, as well
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as the Ray Tune results table. No trials should error. All trials should
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run in parallel.
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"""
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from collections import Counter
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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 import tune
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from xgboost_ray import RayParams
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from ray.util.xgboost.release_test_util import train_ray
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def train_wrapper(config, ray_params):
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train_ray(
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path="/data/classification.parquet",
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num_workers=None,
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num_boost_rounds=100,
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num_files=128,
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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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xgboost_params=config,
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)
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if __name__ == "__main__":
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search_space = {
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"eta": tune.loguniform(1e-4, 1e-1),
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"subsample": tune.uniform(0.5, 1.0),
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"max_depth": tune.randint(1, 9),
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}
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ray.init(address="auto")
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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=32,
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cpus_per_actor=1,
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gpus_per_actor=0,
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)
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start = time.time()
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analysis = tune.run(
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tune.with_parameters(train_wrapper, ray_params=ray_params),
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config=search_space,
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num_samples=4,
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resources_per_trial=ray_params.get_tune_resources(),
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)
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taken = time.time() - start
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result = {
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"time_taken": taken,
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"trial_states": dict(Counter([trial.status for trial in analysis.trials])),
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}
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test_output_json = os.environ.get("TEST_OUTPUT_JSON", "/tmp/tune_4x32.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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