我想使用 Google colaboratory 在我的数据集上使用 GPU 训练 LightGBM（我还选择了运行时 Python3 和 GPU）。为此，我使用了以下代码块：
!apt-get -qq install --no-install-recommends nvidia-375
!apt-get -qq install --no-install-recommends nvidia-opencl-icd-375 nvidia-opencl-dev opencl-headers
#!apt-get update
!apt-get install --no-install-recommends git cmake build-essential libboost-dev libboost-system-dev libboost-filesystem-dev ocl-icd-libopencl1 ocl-icd-opencl-dev
!pip install -qq lightgbm --install-option=--gpu
同样在笔记本中，我选择了设备 gpu：
clf = LGBMClassifier(
n_estimators=10000,
learning_rate=0.03,
num_leaves=30,
colsample_bytree=.8,
subsample=.9,
max_depth=7,
reg_alpha=.1,
reg_lambda=.1,
min_split_gain=.01,
min_child_weight=2,
silent=-1,
verbose=-1,
device = 'gpu'
#gpu_platform_id: '0'
#gpu_device_id: '0'
)
得到了这个：
LightGBMError Traceback (most recent call last)
<ipython-input-10-936c00d106e3> in <module>()
50 clf.fit(trn_x, trn_y,
51 eval_set= [(trn_x, trn_y), (val_x, val_y)],
---> 52 eval_metric='auc', verbose=100, early_stopping_rounds=100 #30
53 )
54
/usr/local/lib/python3.6/dist-packages/lightgbm/sklearn.py in fit(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks)
673 verbose=verbose, feature_name=feature_name,
674 categorical_feature=categorical_feature,
--> 675 callbacks=callbacks)
676 return self
677
/usr/local/lib/python3.6/dist-packages/lightgbm/sklearn.py in fit(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, early_stopping_rounds, verbose, feature_name, categorical_feature, callbacks)
467 verbose_eval=verbose, feature_name=feature_name,
468 categorical_feature=categorical_feature,
--> 469 callbacks=callbacks)
470
471 if evals_result:
/usr/local/lib/python3.6/dist-packages/lightgbm/engine.py in train(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)
178 # construct booster
179 try:
--> 180 booster = Booster(params=params, train_set=train_set)
181 if is_valid_contain_train:
182 booster.set_train_data_name(train_data_name)
/usr/local/lib/python3.6/dist-packages/lightgbm/basic.py in __init__(self, params, train_set, model_file, silent)
1303 train_set.construct().handle,
1304 c_str(params_str),
-> 1305 ctypes.byref(self.handle)))
1306 # save reference to data
1307 self.train_set = train_set
/usr/local/lib/python3.6/dist-packages/lightgbm/basic.py in _safe_call(ret)
46 """
47 if ret != 0:
---> 48 raise LightGBMError(_LIB.LGBM_GetLastError())
49
50
LightGBMError: b'No OpenCL device found'
我也试过这个解决方案在 Google Collab 上为 LightGBM 安装 GPU 支持，但没有任何改变