mirror of
https://github.com/vale981/ray
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65 lines
1.6 KiB
Python
65 lines
1.6 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: 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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if __name__ == "__main__":
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os.environ["RXGB_PLACEMENT_GROUP_TIMEOUT_S"] = "1200"
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addr = os.environ.get("RAY_ADDRESS")
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job_name = os.environ.get("RAY_JOB_NAME", "train_gpu_connect")
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runtime_env = {"env_vars": {"RXGB_PLACEMENT_GROUP_TIMEOUT_S": "1200"}}
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if addr.startswith("anyscale://"):
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ray.init(address=addr, job_name=job_name, runtime_env=runtime_env)
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else:
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ray.init(address="auto")
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from xgboost_ray import RayParams
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from ray.util.xgboost.release_test_util import train_ray, get_parquet_files
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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=1,
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)
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@ray.remote
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def ray_get_parquet_files():
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return get_parquet_files(
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path="/data/classification.parquet",
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num_files=25,
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)
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start = time.time()
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train_ray(
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path=ray.get(ray_get_parquet_files.remote()),
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num_workers=4,
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num_boost_rounds=100,
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regression=False,
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use_gpu=True,
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ray_params=ray_params,
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xgboost_params=None,
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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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}
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test_output_json = os.environ.get("TEST_OUTPUT_JSON", "/tmp/train_gpu_connect.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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