608ebbe444
* Hack for saving GPU ID. * Declare prediction cache on GBTree. * Add a simple test. * Add `auto` option for GPU Predictor.
94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
import sys
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import pytest
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import numpy as np
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import unittest
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if sys.platform.startswith("win"):
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pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
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try:
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import dask.dataframe as dd
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from xgboost import dask as dxgb
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from dask_cuda import LocalCUDACluster
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from dask.distributed import Client
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import cudf
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except ImportError:
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pass
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sys.path.append("tests/python")
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from test_with_dask import generate_array # noqa
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import testing as tm # noqa
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class TestDistributedGPU(unittest.TestCase):
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@pytest.mark.skipif(**tm.no_dask())
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@pytest.mark.skipif(**tm.no_cudf())
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@pytest.mark.skipif(**tm.no_dask_cudf())
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@pytest.mark.skipif(**tm.no_dask_cuda())
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def test_dask_dataframe(self):
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with LocalCUDACluster() as cluster:
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with Client(cluster) as client:
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X, y = generate_array()
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X = dd.from_dask_array(X)
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y = dd.from_dask_array(y)
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X = X.map_partitions(cudf.from_pandas)
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y = y.map_partitions(cudf.from_pandas)
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dtrain = dxgb.DaskDMatrix(client, X, y)
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out = dxgb.train(client, {'tree_method': 'gpu_hist'},
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dtrain=dtrain,
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evals=[(dtrain, 'X')],
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num_boost_round=2)
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assert isinstance(out['booster'], dxgb.Booster)
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assert len(out['history']['X']['rmse']) == 2
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predictions = dxgb.predict(client, out, dtrain).compute()
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assert isinstance(predictions, np.ndarray)
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@pytest.mark.skipif(**tm.no_dask())
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@pytest.mark.skipif(**tm.no_dask_cuda())
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@pytest.mark.mgpu
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def test_empty_dmatrix(self):
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def _check_outputs(out, predictions):
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assert isinstance(out['booster'], dxgb.Booster)
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assert len(out['history']['validation']['rmse']) == 2
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assert isinstance(predictions, np.ndarray)
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assert predictions.shape[0] == 1
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parameters = {'tree_method': 'gpu_hist', 'verbosity': 3,
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'debug_synchronize': True}
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with LocalCUDACluster() as cluster:
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with Client(cluster) as client:
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kRows, kCols = 1, 97
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X = dd.from_array(np.random.randn(kRows, kCols))
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y = dd.from_array(np.random.rand(kRows))
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dtrain = dxgb.DaskDMatrix(client, X, y)
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out = dxgb.train(client, parameters,
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dtrain=dtrain,
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evals=[(dtrain, 'validation')],
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num_boost_round=2)
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predictions = dxgb.predict(client=client, model=out,
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data=dtrain).compute()
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_check_outputs(out, predictions)
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# train has more rows than evals
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valid = dtrain
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kRows += 1
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X = dd.from_array(np.random.randn(kRows, kCols))
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y = dd.from_array(np.random.rand(kRows))
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dtrain = dxgb.DaskDMatrix(client, X, y)
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out = dxgb.train(client, parameters,
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dtrain=dtrain,
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evals=[(valid, 'validation')],
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num_boost_round=2)
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predictions = dxgb.predict(client=client, model=out,
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data=valid).compute()
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_check_outputs(out, predictions)
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