mirror of
https://github.com/vale981/ray
synced 2025-03-12 14:16:39 -04:00
862d10c162
Removes all ML related code from `ray.util` Removes: - `ray.util.xgboost` - `ray.util.lightgbm` - `ray.util.horovod` - `ray.util.ray_lightning` Moves `ray.util.ml_utils` to other locations Closes #23900 Signed-off-by: Amog Kamsetty <amogkamsetty@yahoo.com> Signed-off-by: Kai Fricke <kai@anyscale.com> Co-authored-by: Kai Fricke <kai@anyscale.com>
73 lines
2 KiB
Python
73 lines
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 json
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import os
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import time
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import ray
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from xgboost_ray import RayParams
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from release_test_util import train_ray
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if __name__ == "__main__":
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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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"NCCL_SOCKET_IFNAME": "ens3",
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},
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"working_dir": os.path.dirname(__file__),
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}
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ray.init(address="auto", runtime_env=runtime_env)
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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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start = time.time()
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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=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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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.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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