LightGBM Frequently Asked Questions
A critical issue could be a crash, prediction error, nonsense output, or something else requiring immediate attention.
Please post such an issue in the Microsoft/LightGBM repository.
You may also ping a member of the core team according to the relevant area of expertise by mentioning them with the arabase (@) symbol:
- @guolinke Guolin Ke (C++ code / R-package / Python-package)
- @chivee Qiwei Ye (C++ code / Python-package)
- @Laurae2 Damien Soukhavong (R-package)
- @jameslamb James Lamb (R-package)
- @wxchan Wenxuan Chen (Python-package)
- @henry0312 Tsukasa Omoto (Python-package)
- @StrikerRUS Nikita Titov (Python-package)
- @huanzhang12 Huan Zhang (GPU support)
Please include as much of the following information as possible when submitting a critical issue:
- Is it reproducible on CLI (command line interface), R, and/or Python?
- Is it specific to a wrapper? (R or Python?)
- Is it specific to the compiler? (gcc or Clang version? MinGW or Visual Studio version?)
- Is it specific to your Operating System? (Windows? Linux? macOS?)
- Are you able to reproduce this issue with a simple case?
- Does the issue persist after removing all optimization flags and compiling LightGBM in debug mode?
When submitting issues, please keep in mind that this is largely a volunteer effort, and we may not be available 24/7 to provide support.
General LightGBM Questions¶
- 1. Where do I find more details about LightGBM parameters?
- 2. On datasets with millions of features, training does not start (or starts after a very long time).
- 3. When running LightGBM on a large dataset, my computer runs out of RAM.
- 4. I am using Windows. Should I use Visual Studio or MinGW for compiling LightGBM?
- 5. When using LightGBM GPU, I cannot reproduce results over several runs.
- 6. Bagging is not reproducible when changing the number of threads.
- 7. I tried to use Random Forest mode, and LightGBM crashes!
- 8. CPU usage is low (like 10%) in Windows when using LightGBM on very large datasets with many-core systems.
- 9. When I’m trying to specify a categorical column with the
categorical_featureparameter, I get the following sequence of warnings, but there are no negative values in the column.
- 10. LightGBM crashes randomly with the error like:
Initializing libiomp5.dylib, but found libomp.dylib already initialized.
- 11. LightGBM hangs when multithreading (OpenMP) and using forking in Linux at the same time.
- 12. Why is early stopping not enabled by default in LightGBM?
1. Where do I find more details about LightGBM parameters?¶
2. On datasets with millions of features, training does not start (or starts after a very long time).¶
Use a smaller value for
bin_construct_sample_cnt and a larger value for
3. When running LightGBM on a large dataset, my computer runs out of RAM.¶
Multiple Solutions: set the
histogram_pool_size parameter to the MB you want to use for LightGBM (histogram_pool_size + dataset size = approximately RAM used),
num_leaves or lower
max_bin (see Microsoft/LightGBM#562).
4. I am using Windows. Should I use Visual Studio or MinGW for compiling LightGBM?¶
Visual Studio performs best for LightGBM.
5. When using LightGBM GPU, I cannot reproduce results over several runs.¶
This is normal and expected behaviour, but you may try to use
gpu_use_dp = true for reproducibility
You may also use the CPU version.
6. Bagging is not reproducible when changing the number of threads.¶
LightGBM bagging is multithreaded, so its output depends on the number of threads used. There is no workaround currently.
7. I tried to use Random Forest mode, and LightGBM crashes!¶
This is expected behaviour for arbitrary parameters. To enable Random Forest,
you must use
feature_fraction different from 1, along with a
This thread includes an example.
8. CPU usage is low (like 10%) in Windows when using LightGBM on very large datasets with many-core systems.¶
9. When I’m trying to specify a categorical column with the
categorical_feature parameter, I get the following sequence of warnings, but there are no negative values in the column.¶
[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN [LightGBM] [Warning] There are no meaningful features, as all feature values are constant.
The column you’re trying to pass via
categorical_feature likely contains very large values.
Categorical features in LightGBM are limited by int32 range,
so you cannot pass values that are greater than
Int32.MaxValue (2147483647) as categorical features (see Microsoft/LightGBM#1359).
You should convert them to integers ranging from zero to the number of categories first.
10. LightGBM crashes randomly with the error like:
Initializing libiomp5.dylib, but found libomp.dylib already initialized.¶
OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized. OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.
Possible Cause: This error means that you have multiple OpenMP libraries installed on your machine and they conflict with each other. (File extensions in the error message may differ depending on the operating system).
If you are using Python distributed by Conda, then it is highly likely that the error is caused by the
numpy package from Conda which includes the
mkl package which in turn conflicts with the system-wide library.
In this case you can update the
numpy package in Conda or replace the Conda’s OpenMP library instance with system-wide one by creating a symlink to it in Conda environment folder
Solution: Assuming you are using macOS with Homebrew, the command which overwrites OpenMP library files in the current active Conda environment with symlinks to the system-wide library ones installed by Homebrew:
ln -sf `ls -d "$(brew --cellar libomp)"/*/lib`/* $CONDA_PREFIX/lib
The described above fix worked fine before the release of OpenMP 8.0.0 version.
Starting from 8.0.0 version, Homebrew formula for OpenMP includes
-DLIBOMP_INSTALL_ALIASES=OFF option which leads to that the fix doesn’t work anymore.
However, you can create symlinks to library aliases manually:
for LIBOMP_ALIAS in libgomp.dylib libiomp5.dylib libomp.dylib; do sudo ln -sf "$(brew --cellar libomp)"/*/lib/libomp.dylib $CONDA_PREFIX/lib/$LIBOMP_ALIAS; done
Another workaround would be removing MKL optimizations from Conda’s packages completely:
conda install nomkl
If this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them.
11. LightGBM hangs when multithreading (OpenMP) and using forking in Linux at the same time.¶
nthreads=1 to disable multithreading of LightGBM. There is a bug with OpenMP which hangs forked sessions
with multithreading activated. A more expensive solution is to use new processes instead of using fork, however,
keep in mind it is creating new processes where you have to copy memory and load libraries (example: if you want to
fork 16 times your current process, then you will require to make 16 copies of your dataset in memory)
An alternative, if multithreading is really necessary inside the forked sessions, would be to compile LightGBM with Intel toolchain. Intel compilers are unaffected by this bug.
For C/C++ users, any OpenMP feature cannot be used before the fork happens. If an OpenMP feature is used before the fork happens (example: using OpenMP for forking), OpenMP will hang inside the forked sessions. Use new processes instead and copy memory as required by creating new processes instead of forking (or, use Intel compilers).
12. Why is early stopping not enabled by default in LightGBM?¶
Early stopping involves choosing a validation set, a special type of holdout which is used to evaluate the current state of the model after each iteration to see if training can stop.
LightGBM, we have decided to require that users specify this set directly. Many options exist for splitting training data into training, test, and validation sets.
The appropriate splitting strategy depends on the task and domain of the data, information that a modeler has but which
LightGBM as a general-purpose tool does not.
1. Any training command using LightGBM does not work after an error occurred during the training of a previous LightGBM model.¶
lgb.unloader(wipe = TRUE) in the R console, and recreate the LightGBM datasets (this will wipe all LightGBM-related variables).
Due to the pointers, choosing to not wipe variables will not fix the error.
This is a known issue: Microsoft/LightGBM#698.
Error: setup script specifies an absolute path when installing from GitHub using
python setup.py install.¶
error: Error: setup script specifies an absolute path: /Users/Microsoft/LightGBM/python-package/lightgbm/../../lib_lightgbm.so setup() arguments must *always* be /-separated paths relative to the setup.py directory, *never* absolute paths.
This error should be solved in latest version.
If you still meet this error, try to remove
lightgbm.egg-info folder in your Python-package and reinstall,
or check this thread on stackoverflow.
2. Error messages:
Cannot ... before construct dataset.¶
I see error messages like…
Cannot get/set label/weight/init_score/group/num_data/num_feature before construct dataset
but I’ve already constructed a dataset by some code like:
train = lightgbm.Dataset(X_train, y_train)
or error messages like
Cannot set predictor/reference/categorical feature after freed raw data, set free_raw_data=False when construct Dataset to avoid this.
Solution: Because LightGBM constructs bin mappers to build trees, and train and valid Datasets within one Booster share the same bin mappers,
categorical features and feature names etc., the Dataset objects are constructed when constructing a Booster.
If you set
free_raw_data=True (default), the raw data (with Python data struct) will be freed.
So, if you want to:
- get label (or weight/init_score/group/data) before constructing a dataset, it’s same as get
- set label (or weight/init_score/group) before constructing a dataset, it’s same as
- get num_data (or num_feature) before constructing a dataset, you can get data with
self.data. Then, if your data is
numpy.ndarray, use some code like
self.data.shape. But do not do this after subsetting the Dataset, because you’ll get always
- set predictor (or reference/categorical feature) after constructing a dataset,
you should set
free_raw_data=Falseor init a Dataset object with the same raw data.
3. I encounter segmentation faults (segfaults) randomly after installing LightGBM from PyPI using
pip install lightgbm.¶
We are doing our best to provide universal wheels which have high running speed and are compatible with any hardware, OS, compiler, etc. at the same time. However, sometimes it’s just impossible to guarantee the possibility of usage of LightGBM in any specific environment (see Microsoft/LightGBM#1743).
Therefore, the first thing you should try in case of segfaults is compiling from the source using
pip install --no-binary :all: lightgbm.
For the OS-specific prerequisites see this guide.
Also, feel free to post a new issue in our GitHub repository. We always look at each case individually and try to find a root cause.