"""
Using rmm with Dask
===================
"""

import dask
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from sklearn.datasets import make_classification

import xgboost as xgb


def main(client):
    # Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
    # xgb.set_config(use_rmm=True)

    X, y = make_classification(n_samples=10000, n_informative=5, n_classes=3)
    # In pratice one should prefer loading the data with dask collections instead of
    # using `from_array`.
    X = dask.array.from_array(X)
    y = dask.array.from_array(y)
    dtrain = xgb.dask.DaskDMatrix(client, X, label=y)

    params = {
        "max_depth": 8,
        "eta": 0.01,
        "objective": "multi:softprob",
        "num_class": 3,
        "tree_method": "hist",
        "eval_metric": "merror",
        "device": "cuda",
    }
    output = xgb.dask.train(
        client, params, dtrain, num_boost_round=100, evals=[(dtrain, "train")]
    )
    bst = output["booster"]
    history = output["history"]
    for i, e in enumerate(history["train"]["merror"]):
        print(f"[{i}] train-merror: {e}")


if __name__ == "__main__":
    # To use RMM pool allocator with a GPU Dask cluster, just add rmm_pool_size option
    # to LocalCUDACluster constructor.
    with LocalCUDACluster(rmm_pool_size="2GB") as cluster:
        with Client(cluster) as client:
            main(client)

"""
Using rmm with Dask
===================
"""
import dask
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from sklearn.datasets import make_classification

import xgboost as xgb


def main(client):
    # Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
    # xgb.set_config(use_rmm=True)

    X, y = make_classification(n_samples=10000, n_informative=5, n_classes=3)
    # In pratice one should prefer loading the data with dask collections instead of
    # using `from_array`.
    X = dask.array.from_array(X)
    y = dask.array.from_array(y)
    dtrain = xgb.dask.DaskDMatrix(client, X, label=y)

    params = {
        "max_depth": 8,
        "eta": 0.01,
        "objective": "multi:softprob",
        "num_class": 3,
        "tree_method": "hist",
        "eval_metric": "merror",
        "device": "cuda",
    }
    output = xgb.dask.train(
        client, params, dtrain, num_boost_round=100, evals=[(dtrain, "train")]
    )
    bst = output["booster"]
    history = output["history"]
    for i, e in enumerate(history["train"]["merror"]):
        print(f"[{i}] train-merror: {e}")


if __name__ == "__main__":
    # To use RMM pool allocator with a GPU Dask cluster, just add rmm_pool_size option
    # to LocalCUDACluster constructor.
    with LocalCUDACluster(rmm_pool_size="2GB") as cluster:
        with Client(cluster) as client:
            main(client)
