* Only define `gpu_id` and `n_gpus` in `LearnerTrainParam` * Pass LearnerTrainParam through XGBoost vid factory method. * Disable all GPU usage when GPU related parameters are not specified (fixes XGBoost choosing GPU over aggressively). * Test learner train param io. * Fix gpu pickling.
54 lines
1.4 KiB
Python
54 lines
1.4 KiB
Python
'''Test model IO with pickle.'''
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import pickle
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import unittest
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import numpy as np
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import subprocess
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import os
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import xgboost as xgb
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model_path = './model.pkl'
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def build_dataset():
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N = 10
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x = np.linspace(0, N*N, N*N)
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x = x.reshape((N, N))
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y = np.linspace(0, N, N)
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return x, y
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class TestPickling(unittest.TestCase):
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def test_pickling(self):
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x, y = build_dataset()
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train_x = xgb.DMatrix(x, label=y)
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param = {'tree_method': 'gpu_hist',
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'gpu_id': 0,
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'n_gpus': -1,
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'verbosity': 1}
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bst = xgb.train(param, train_x)
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with open(model_path, 'wb') as fd:
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pickle.dump(bst, fd)
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args = ["pytest",
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"--verbose",
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"-s",
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"--fulltrace",
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"./tests/python-gpu/load_pickle.py"]
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command = ''
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for arg in args:
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command += arg
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command += ' '
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cuda_environment = {'CUDA_VISIBLE_DEVICES': ''}
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env = os.environ
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# Passing new_environment directly to `env' argument results
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# in failure on Windows:
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# Fatal Python error: _Py_HashRandomization_Init: failed to
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# get random numbers to initialize Python
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env.update(cuda_environment)
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# Load model in a CPU only environment.
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status = subprocess.call(command, env=env, shell=True)
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assert status == 0
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os.remove(model_path)
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