[pyspark] support gpu transform #9542
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trivialfis
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Sep 5, 2023
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| try: | ||
| import cupy | ||
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| return True | ||
| except ImportError: | ||
| return False |
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The test for cuDF availability originally had the comment:
# Checking by `importing` instead of check `importlib.util.find_spec("cudf") is not None`
WeichenXu123 marked this conversation as resolved.
# because user might install cudf successfully but importing cudf raises issues (e.g. saying
# running on mismatched cuda version)
which to me was weird, that's a mismanaged environment and I'm not sure it's necessary for xgboost to work around it (or even a good idea to workaround anything since users with cuDF installed might expect GPU to be used).
python-package/xgboost/spark/core.py
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| def set_device(self, value: str) -> "_SparkXGBParams": | ||
| """Set device (cpu, cuda, gpu)""" | ||
| assert value in ("cpu", "cuda", "gpu") | ||
| self.set(self.device, value) | ||
| return self | ||
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We have a check here:
xgboost/python-package/xgboost/core.py
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Seems _check_distributed_params just checks the type of the value, it seems we also need to restrict it to be one of (CPU ,GPU, cuda)
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trivialfis
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--------- Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
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PR to support gpu transform. @WeichenXu123 @trivialfis Please help to review this PR. Thx