3136185bc5
* Add saving/loading JSON configuration. * Implement Python pickle interface with new IO routines. * Basic tests for training continuation.
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
'''Loading a pickled model generated by test_pickling.py, only used by
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`test_gpu_with_dask.py`'''
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import unittest
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import os
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import xgboost as xgb
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import json
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from test_gpu_pickling import build_dataset, model_path, load_pickle
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class TestLoadPickle(unittest.TestCase):
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def test_load_pkl(self):
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'''Test whether prediction is correct.'''
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assert os.environ['CUDA_VISIBLE_DEVICES'] == '-1'
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bst = load_pickle(model_path)
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x, y = build_dataset()
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test_x = xgb.DMatrix(x)
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res = bst.predict(test_x)
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assert len(res) == 10
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def test_predictor_type_is_auto(self):
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'''Under invalid CUDA_VISIBLE_DEVICES, predictor should be set to
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auto'''
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assert os.environ['CUDA_VISIBLE_DEVICES'] == '-1'
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bst = load_pickle(model_path)
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config = bst.save_config()
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config = json.loads(config)
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assert config['learner']['gradient_booster']['gbtree_train_param'][
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'predictor'] == 'auto'
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def test_predictor_type_is_gpu(self):
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'''When CUDA_VISIBLE_DEVICES is not specified, keep using
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`gpu_predictor`'''
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assert 'CUDA_VISIBLE_DEVICES' not in os.environ.keys()
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bst = load_pickle(model_path)
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config = bst.save_config()
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config = json.loads(config)
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assert config['learner']['gradient_booster']['gbtree_train_param'][
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'predictor'] == 'gpu_predictor'
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