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https://github.com/vale981/ray
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8804758409
* Add XGBoost release tests * Add more xgboost release tests * Use failure state manager * Add release test documentation * Fix wording * Automate fault tolerance tests
56 lines
1.3 KiB
Python
56 lines
1.3 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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import ray
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from ray import tune
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from xgboost_ray import RayParams
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from _train import train_ray
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def train_wrapper(config):
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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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train_ray(
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path="/data/classification.parquet",
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num_workers=32,
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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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analysis = tune.run(
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train_wrapper,
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config=search_space,
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num_samples=4,
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resources_per_trial={
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"cpu": 1,
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"extra_cpu": 31
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})
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