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https://github.com/vale981/ray
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a0f73cf3f7
* Update XGBoost release test configs * Use GPU containers * Fix elastic check * Use spot instances for GPU * Add debugging output * Fix success check, failure checking, outputs, sync behavior * Update release checklist, rename mounts
48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
"""Training on a GPU cluster.
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This will train a small dataset on a distributed GPU cluster.
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Test owner: krfricke
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Acceptance criteria: Should run through and report final results.
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Notes: The test will report output such as this:
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```
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[05:14:49] WARNING: ../src/gbm/gbtree.cc:350: Loading from a raw memory buffer
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on CPU only machine. Changing tree_method to hist.
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[05:14:49] WARNING: ../src/learner.cc:222: No visible GPU is found, setting
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`gpu_id` to -1
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```
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This is _not_ an error. This is due to the checkpoints being loaded on the
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XGBoost driver, and since the driver lives on the head node (which has no
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GPU), XGBoost warns that it can't use the GPU. Training still happened using
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the GPUs.
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"""
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import ray
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from xgboost_ray import RayParams
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from _train import train_ray
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if __name__ == "__main__":
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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=4,
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cpus_per_actor=4,
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gpus_per_actor=1)
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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=True,
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ray_params=ray_params,
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xgboost_params=None,
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)
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print("PASSED.")
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