install lightgbm GPU in a WSL conda env

Question

-------------------- original question ---------------------------------

How to install LightGBM?? I have checked multiple sources but staill failed to install.

I tried pip and conda but both return the error:

[LightGBM] [Warning] Using sparse features with CUDA is currently not supported.
[LightGBM] [Fatal] CUDA Tree Learner was not enabled in this build.
Please recompile with CMake option -DUSE_CUDA=1

What i have tried is following:

git clone --recursive https://github.com/microsoft/LightGBM
cd LightGBM/
mkdir -p build
cd build
cmake -DUSE_GPU=1 ..
make -j$(nproc)
cd ../python-package
pip install .

-------------------- My solution below (cuda) ---------------------------------

Thanks for the replies guys. I tried some ways and finally it works as below: First, make sure cmake is installed (under the wsl):

sudo apt-get update
sudo apt-get install cmake
sudo apt-get install g++

Then,

git clone --recursive https://github.com/microsoft/LightGBM
cd LightGBM
mkdir build
cd build
cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ ..
make -j4

Currently, the install is not linked to any conda env yet. So to do this, under the vscode terminal (or still wsl), conda activate an env and then create a jupyter notebook for testing: Make sure that lib_lightgbm.so is under the LightGBM/python-package, if not, copy into that folder. Then in the jupyter notebook:

import sys
import numpy as np
sys.path.append('/mnt/d/lgm-test2/LightGBM/python-package')
import lightgbm as lgb

The final bit is you can refer Jame's reply that device needs to be set to 'cuda' instead of 'gpu'.

Answer 1

Seeing logs about CUDA in the original posts suggests to me that you're trying to use CUDA-enabled LightGBM. It's important to clarify that, as LightGBM supports two different GPU-accelerated builds:

• -DUSE_GPU=1 ("device": "gpu") = OpenCL-based build targeting a wide range of GPUs
• -DUSE_CUDA=1 ("device": "cuda") = CUDA kernels targeting NVIDIA GPUs

As described in the project's docs (link), as of v4.0.0 building the lightgbm Python package from sources in its git repos requires use of a shell script in that repo.

Run the following to build and install a CUDA-enabled version of the library from the source code on GitHub.

git clone --recursive https://github.com/microsoft/LightGBM
cd LightGBM/
sh build-python.sh install --cuda

If you'd prefer to install from a release on PyPI without having to clone the repo, run the following.

pip install \
  --no-binary lightgbm \
  --config-settings=cmake.define.USE_CUDA=ON \
  'lightgbm>=4.0.0'

With CUDA-enabled LightGBM installed that way, you can then use GPU-accelerated training by passing "device": "cuda" through parameters, like this:

import lightgbm as lgb
from sklearn.datasets import make_regression

X, y = make_regression(n_samples=10_000)
dtrain = lgb.Dataset(X, label=y)
bst = lgb.train(
    params={
        "objective": "regression",
        "device": "cuda",
        "verbose": 1
    },
    train_set=dtrain,
    num_boost_round=5
)