如何在支持 CUDA 的 Ubuntu 20.04 上安装 LightGBM

Question

我按照docs中的说明进行操作，但也尝试了许多其他变体。一切都结束在同一个地方，如下：

LightGBM] [Fatal] Check failed: (split_indices_block_size_data_partition) > (0) at /home/azureuser/localfiles/LightGBM/lightgbm-python/src/treelearner/cuda/cuda_data_partition.cpp, line 280 .

我无法找到正在发生的事情或如何解决此问题的答案。我已经在 conda envs 中尝试过 Python 3.10 和 11 并使用最新的 lightgbm 和 cuda。我怀疑 cuda 实现比其他 GPU 实现更快，但不确定。完整的回溯是：

    "name": "LightGBMError",
    "message": "Check failed: (split_indices_block_size_data_partition) > (0) at /home/azureuser/localfiles/LightGBM/lightgbm-python/src/treelearner/cuda/cuda_data_partition.cpp, line 280 .
",
    "stack": "---------------------------------------------------------------------------
LightGBMError                             Traceback (most recent call last)
Cell In[2], line 19
     12 # Create and train the LightGBM classifier with GPU support
     13 clf = lgb.LGBMClassifier(
     14     objective='binary',
     15     device='cuda',
     16     verbose=1,
     17 )
---> 19 clf.fit(X_train, y_train)
     21 # Predict and evaluate
     22 y_pred = clf.predict(X_test)

File /anaconda/envs/py311/lib/python3.11/site-packages/lightgbm/sklearn.py:1421, in LGBMClassifier.fit(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, feature_name, categorical_feature, callbacks, init_model)
   1418         else:
   1419             valid_sets.append((valid_x, self._le.transform(valid_y)))
-> 1421 super().fit(
   1422     X,
   1423     _y,
   1424     sample_weight=sample_weight,
   1425     init_score=init_score,
   1426     eval_set=valid_sets,
   1427     eval_names=eval_names,
   1428     eval_sample_weight=eval_sample_weight,
   1429     eval_class_weight=eval_class_weight,
   1430     eval_init_score=eval_init_score,
   1431     eval_metric=eval_metric,
   1432     feature_name=feature_name,
   1433     categorical_feature=categorical_feature,
   1434     callbacks=callbacks,
   1435     init_model=init_model,
   1436 )
   1437 return self

File /anaconda/envs/py311/lib/python3.11/site-packages/lightgbm/sklearn.py:1015, in LGBMModel.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, feature_name, categorical_feature, callbacks, init_model)
   1012 evals_result: _EvalResultDict = {}
   1013 callbacks.append(record_evaluation(evals_result))
-> 1015 self._Booster = train(
   1016     params=params,
   1017     train_set=train_set,
   1018     num_boost_round=self.n_estimators,
   1019     valid_sets=valid_sets,
   1020     valid_names=eval_names,
   1021     feval=eval_metrics_callable,  # type: ignore[arg-type]
   1022     init_model=init_model,
   1023     callbacks=callbacks,
   1024 )
   1026 # This populates the property self.n_features_, the number of features in the fitted model,
   1027 # and so should only be set after fitting.
   1028 #
   1029 # The related property self._n_features_in, which populates self.n_features_in_,
   1030 # is set BEFORE fitting.
   1031 self._n_features = self._Booster.num_feature()

File /anaconda/envs/py311/lib/python3.11/site-packages/lightgbm/engine.py:361, in train(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, feature_name, categorical_feature, keep_training_booster, callbacks)
    349 for cb in callbacks_before_iter:
    350     cb(
    351         callback.CallbackEnv(
    352             model=booster,
   (...)
    358         )
    359     )
--> 361 booster.update(fobj=fobj)
    363 evaluation_result_list: List[_LGBM_BoosterEvalMethodResultType] = []
    364 # check evaluation result.

File /anaconda/envs/py311/lib/python3.11/site-packages/lightgbm/basic.py:4143, in Booster.update(self, train_set, fobj)
   4141 if self.__set_objective_to_none:
   4142     raise LightGBMError(\"Cannot update due to null objective function.\")
-> 4143 _safe_call(
   4144     _LIB.LGBM_BoosterUpdateOneIter(
   4145         self._handle,
   4146         ctypes.byref(is_finished),
   4147     )
   4148 )
   4149 self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)]
   4150 return is_finished.value == 1

File /anaconda/envs/py311/lib/python3.11/site-packages/lightgbm/basic.py:295, in _safe_call(ret)
    287 \"\"\"Check the return value from C API call.
    288 
    289 Parameters
   (...)
    292     The return value from C API calls.
    293 \"\"\"
    294 if ret != 0:
--> 295     raise LightGBMError(_LIB.LGBM_GetLastError().decode(\"utf-8\"))

LightGBMError: Check failed: (split_indices_block_size_data_partition) > (0) at /home/azureuser/localfiles/LightGBM/lightgbm-python/src/treelearner/cuda/cuda_data_partition.cpp, line 280 .
"
}

Answer 1

事实证明，截至 2024 年末，LightGBM for CUDA 的计算能力最低级别为 6.0。因此我使用的 NVIDIA M60 Tesla GPU 不兼容。请参阅 github 问题此处。