275da176ba
- Rewrite GPU demos. notebook is converted to script to avoid committing additional png plots. - Add GPU demos into the sphinx gallery. - Add RMM demos into the sphinx gallery. - Test for firing threads with different device ordinals.
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
"""
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Using rmm with Dask
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===================
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"""
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import dask
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from dask.distributed import Client
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from dask_cuda import LocalCUDACluster
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from sklearn.datasets import make_classification
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import xgboost as xgb
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def main(client):
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# Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
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# xgb.set_config(use_rmm=True)
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X, y = make_classification(n_samples=10000, n_informative=5, n_classes=3)
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# In pratice one should prefer loading the data with dask collections instead of
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# using `from_array`.
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X = dask.array.from_array(X)
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y = dask.array.from_array(y)
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dtrain = xgb.dask.DaskDMatrix(client, X, label=y)
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params = {
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"max_depth": 8,
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"eta": 0.01,
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"objective": "multi:softprob",
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"num_class": 3,
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"tree_method": "hist",
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"eval_metric": "merror",
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"device": "cuda",
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}
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output = xgb.dask.train(
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client, params, dtrain, num_boost_round=100, evals=[(dtrain, "train")]
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)
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bst = output["booster"]
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history = output["history"]
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for i, e in enumerate(history["train"]["merror"]):
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print(f"[{i}] train-merror: {e}")
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if __name__ == "__main__":
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# To use RMM pool allocator with a GPU Dask cluster, just add rmm_pool_size option
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# to LocalCUDACluster constructor.
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with LocalCUDACluster(rmm_pool_size="2GB") as cluster:
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with Client(cluster) as client:
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main(client)
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