"""
Example of training with Dask on GPU
====================================
"""
import dask_cudf
from dask import array as da
from dask import dataframe as dd
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from xgboost.dask import DaskDMatrix

import xgboost as xgb
from xgboost import dask as dxgb


def using_dask_matrix(client: Client, X, y):
    # DaskDMatrix acts like normal DMatrix, works as a proxy for local
    # DMatrix scatter around workers.
    dtrain = DaskDMatrix(client, X, y)

    # Use train method from xgboost.dask instead of xgboost.  This
    # distributed version of train returns a dictionary containing the
    # resulting booster and evaluation history obtained from
    # evaluation metrics.
    output = xgb.dask.train(client,
                            {'verbosity': 2,
                             # Golden line for GPU training
                             'tree_method': 'gpu_hist'},
                            dtrain,
                            num_boost_round=4, evals=[(dtrain, 'train')])
    bst = output['booster']
    history = output['history']

    # you can pass output directly into `predict` too.
    prediction = xgb.dask.predict(client, bst, dtrain)
    print('Evaluation history:', history)
    return prediction


def using_quantile_device_dmatrix(client: Client, X, y):
    '''`DaskDeviceQuantileDMatrix` is a data type specialized for `gpu_hist`, tree
     method that reduces memory overhead.  When training on GPU pipeline, it's
     preferred over `DaskDMatrix`.

    .. versionadded:: 1.2.0

    '''
    # Input must be on GPU for `DaskDeviceQuantileDMatrix`.
    X = dask_cudf.from_dask_dataframe(dd.from_dask_array(X))
    y = dask_cudf.from_dask_dataframe(dd.from_dask_array(y))

    # `DaskDeviceQuantileDMatrix` is used instead of `DaskDMatrix`, be careful
    # that it can not be used for anything else other than training.
    dtrain = dxgb.DaskQuantileDMatrix(client, X, y)
    output = xgb.dask.train(client,
                            {'verbosity': 2,
                             'tree_method': 'gpu_hist'},
                            dtrain,
                            num_boost_round=4)

    prediction = xgb.dask.predict(client, output, X)
    return prediction


if __name__ == '__main__':
    # `LocalCUDACluster` is used for assigning GPU to XGBoost processes.  Here
    # `n_workers` represents the number of GPUs since we use one GPU per worker
    # process.
    with LocalCUDACluster(n_workers=2, threads_per_worker=4) as cluster:
        with Client(cluster) as client:
            # generate some random data for demonstration
            m = 100000
            n = 100
            X = da.random.random(size=(m, n), chunks=10000)
            y = da.random.random(size=(m, ), chunks=10000)

            print('Using DaskQuantileDMatrix')
            from_ddqdm = using_quantile_device_dmatrix(client, X, y)
            print('Using DMatrix')
            from_dmatrix = using_dask_matrix(client, X, y)