LightGBM GPU Tutorial

The purpose of this document is to give you a quick step-by-step tutorial on GPU training.

For Windows, please see GPU Windows Tutorial.

We will use the GPU instance on Microsoft Azure cloud computing platform for demonstration, but you can use any machine with modern AMD or NVIDIA GPUs.

GPU Setup

You need to launch a NV type instance on Azure (available in East US, North Central US, South Central US, West Europe and Southeast Asia zones) and select Ubuntu 16.04 LTS as the operating system.

For testing, the smallest NV6 type virtual machine is sufficient, which includes 1/2 M60 GPU, with 8 GB memory, 180 GB/s memory bandwidth and 4,825 GFLOPS peak computation power. Don’t use the NC type instance as the GPUs (K80) are based on an older architecture (Kepler).

First we need to install minimal NVIDIA drivers and OpenCL development environment:

sudo apt-get update
sudo apt-get install --no-install-recommends nvidia-375
sudo apt-get install --no-install-recommends nvidia-opencl-icd-375 nvidia-opencl-dev opencl-headers

After installing the drivers you need to restart the server.

sudo init 6

After about 30 seconds, the server should be up again.

If you are using a AMD GPU, you should download and install the AMDGPU-Pro driver and also install package ocl-icd-libopencl1 and ocl-icd-opencl-dev.

Build LightGBM

Now install necessary building tools and dependencies:

sudo apt-get install --no-install-recommends git cmake build-essential libboost-dev libboost-system-dev libboost-filesystem-dev

The NV6 GPU instance has a 320 GB ultra-fast SSD mounted at /mnt. Let’s use it as our workspace (skip this if you are using your own machine):

sudo mkdir -p /mnt/workspace
sudo chown $(whoami):$(whoami) /mnt/workspace
cd /mnt/workspace

Now we are ready to checkout LightGBM and compile it with GPU support:

git clone --recursive https://github.com/Microsoft/LightGBM
cd LightGBM
mkdir build ; cd build
cmake -DUSE_GPU=1 ..
# if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following:
# cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ ..
make -j$(nproc)
cd ..

You will see two binaries are generated, lightgbm and lib_lightgbm.so.

If you are building on macOS, you probably need to remove macro BOOST_COMPUTE_USE_OFFLINE_CACHE in src/treelearner/gpu_tree_learner.h to avoid a known crash bug in Boost.Compute.

Install Python Interface (optional)

If you want to use the Python interface of LightGBM, you can install it now (along with some necessary Python-package dependencies):

sudo apt-get -y install python-pip
sudo -H pip install setuptools numpy scipy scikit-learn -U
cd python-package/
sudo python setup.py install --precompile
cd ..

You need to set an additional parameter "device" : "gpu" (along with your other options like learning_rate, num_leaves, etc) to use GPU in Python.

You can read our Python-package Examples for more information on how to use the Python interface.

Dataset Preparation

Using the following commands to prepare the Higgs dataset:

git clone https://github.com/guolinke/boosting_tree_benchmarks.git
cd boosting_tree_benchmarks/data
wget "https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz"
gunzip HIGGS.csv.gz
python higgs2libsvm.py
cd ../..
ln -s boosting_tree_benchmarks/data/higgs.train
ln -s boosting_tree_benchmarks/data/higgs.test

Now we create a configuration file for LightGBM by running the following commands (please copy the entire block and run it as a whole):

cat > lightgbm_gpu.conf <<EOF
max_bin = 63
num_leaves = 255
num_iterations = 50
learning_rate = 0.1
tree_learner = serial
task = train
is_training_metric = false
min_data_in_leaf = 1
min_sum_hessian_in_leaf = 100
ndcg_eval_at = 1,3,5,10
sparse_threshold = 1.0
device = gpu
gpu_platform_id = 0
gpu_device_id = 0
EOF
echo "num_threads=$(nproc)" >> lightgbm_gpu.conf

GPU is enabled in the configuration file we just created by setting device=gpu. In this configuration we use the first GPU installed on the system (gpu_platform_id=0 and gpu_device_id=0). If gpu_platform_id or gpu_device_id is not set, the default platform and GPU will be selected. You might have multiple platforms (AMD/Intel/NVIDIA) or GPUs. You can use the clinfo utility to identify the GPUs on each platform. On Ubuntu, you can install clinfo by executing sudo apt-get install clinfo. If you have a discrete GPU by AMD/NVIDIA and an integrated GPU by Intel, make sure to select the correct gpu_platform_id to use the discrete GPU.

Run Your First Learning Task on GPU

Now we are ready to start GPU training!

First we want to verify the GPU works correctly. Run the following command to train on GPU, and take a note of the AUC after 50 iterations:

./lightgbm config=lightgbm_gpu.conf data=higgs.train valid=higgs.test objective=binary metric=auc

Now train the same dataset on CPU using the following command. You should observe a similar AUC:

./lightgbm config=lightgbm_gpu.conf data=higgs.train valid=higgs.test objective=binary metric=auc device=cpu

Now we can make a speed test on GPU without calculating AUC after each iteration.

./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=binary metric=auc

Speed test on CPU:

./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=binary metric=auc device=cpu

You should observe over three times speedup on this GPU.

The GPU acceleration can be used on other tasks/metrics (regression, multi-class classification, ranking, etc) as well. For example, we can train the Higgs dataset on GPU as a regression task:

./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=regression_l2 metric=l2

Also, you can compare the training speed with CPU:

./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=regression_l2 metric=l2 device=cpu

Reference

Please kindly cite the following article in your publications if you find the GPU acceleration useful:

Huan Zhang, Si Si and Cho-Jui Hsieh. “GPU Acceleration for Large-scale Tree Boosting.” arXiv:1706.08359, 2017.