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47243ace7c
In xgboost 1.6, support for older GPU architectures was removed (dmlc/xgboost#7767). This PR updates the instance types used in our xgboost-ray gpu release tests to use Volta GPUs instead of Kepler GPUs so that xgboost-ray can run successfully with xgboost v1.6. Closes #24048
75 lines
2 KiB
Python
75 lines
2 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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# Manually set NCCL_SOCKET_IFNAME to "ens3" so NCCL training works on
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# anyscale_default_cloud.
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# See https://github.com/pytorch/pytorch/issues/68893 for more details.
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# Passing in runtime_env to ray.init() will also set it for all the
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# workers.
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runtime_env = {
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"env_vars": {
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"RXGB_PLACEMENT_GROUP_TIMEOUT_S": "1200",
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"NCCL_SOCKET_IFNAME": "ens3",
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}
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}
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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", runtime_env=runtime_env)
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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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