ThunderGBM: Fast GBDTs and Random Forests on GPUs

Overview

Documentation Status GitHub license GitHub issues PyPI version Downloads

Documentations | Installation | Parameters | Python (scikit-learn) interface

What's new?

ThunderGBM won 2019 Best Paper Award from IEEE Transactions on Parallel and Distributed Systems by the IEEE Computer Society Publications Board (1 out of 987 submissions, for the work "Zeyi Wen^, Jiashuai Shi*, Bingsheng He, Jian Chen, Kotagiri Ramamohanarao, and Qinbin Li*, Exploiting GPUs for Efficient Gradient Boosting Decision Tree Training , IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 12, 2019, pp. 2706-2717."). see more details: Best Paper Award Winners from IEEE, News from NUS School of Computing

Overview

The mission of ThunderGBM is to help users easily and efficiently apply GBDTs and Random Forests to solve problems. ThunderGBM exploits GPUs to achieve high efficiency. Key features of ThunderGBM are as follows.

  • Often by 10x times over other libraries.
  • Support Python (scikit-learn) interfaces.
  • Supported Operating System(s): Linux and Windows.
  • Support classification, regression and ranking.

Why accelerate GBDT and Random Forests: A survey conducted by Kaggle in 2017 shows that 50%, 46% and 24% of the data mining and machine learning practitioners are users of Decision Trees, Random Forests and GBMs, respectively.

GBDTs and Random Forests are often used for creating state-of-the-art data science solutions. We've listed three winning solutions using GBDTs below. Please check out the XGBoost website for more winning solutions and use cases. Here are some example successes of GDBTs and Random Forests:

Getting Started

Prerequisites

  • cmake 2.8 or above
    • gcc 4.8 or above for Linux | CUDA 9 or above
    • Visual C++ for Windows | CUDA 10

Quick Install

  • For Linux with CUDA 9.0

    • pip install thundergbm
  • For Windows (64bit)

    • Download the Python wheel file (for Python3 or above)

    • Install the Python wheel file

      • pip install thundergbm-0.3.4-py3-none-win_amd64.whl
  • Currently only support python3

  • After you have installed thundergbm, you can import and use the classifier (similarly for regressor) by:

from thundergbm import TGBMClassifier
clf = TGBMClassifier()
clf.fit(x, y)

Build from source

git clone https://github.com/zeyiwen/thundergbm.git
cd thundergbm
#under the directory of thundergbm
git submodule init cub && git submodule update

Build on Linux (build instructions for Windows)

#under the directory of thundergbm
mkdir build && cd build && cmake .. && make -j

Quick Start

./bin/thundergbm-train ../dataset/machine.conf
./bin/thundergbm-predict ../dataset/machine.conf

You will see RMSE = 0.489562 after successful running.

MacOS is not supported, as Apple has suspended support for some NVIDIA GPUs. We will consider supporting MacOS based on our user community feedbacks. Please stay tuned.

How to cite ThunderGBM

If you use ThunderGBM in your paper, please cite our work (TPDS and JMLR).

@ARTICLE{8727750,
  author={Z. {Wen} and J. {Shi} and B. {He} and J. {Chen} and K. {Ramamohanarao} and Q. {Li}},
  journal={IEEE Transactions on Parallel and Distributed Systems}, 
  title={Exploiting GPUs for Efficient Gradient Boosting Decision Tree Training}, 
  year={2019},
  volume={30},
  number={12},
  pages={2706-2717},
  }

@article{wenthundergbm19,
 author = {Wen, Zeyi and Shi, Jiashuai and He, Bingsheng and Li, Qinbin and Chen, Jian},
 title = {{ThunderGBM}: Fast {GBDTs} and Random Forests on {GPUs}},
 journal = {Journal of Machine Learning Research},
 volume={21},
 year = {2020}
}

Related papers

  • Zeyi Wen, Jiashuai Shi, Bingsheng He, Jian Chen, Kotagiri Ramamohanarao and Qinbin Li. Exploiting GPUs for Efficient Gradient Boosting Decision Tree Training. IEEE Transactions on Parallel and Distributed Systems (TPDS), accepted in May 2019. pdf

  • Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen. ThunderGBM: Fast GBDTs and Random Forests on GPUs. Featured at JMLR MLOSS (Machine Learning Open Source Software). Year: 2020, Volume: 21, Issue: 108, Pages: 1−5. pdf

  • Zeyi Wen, Bingsheng He, Kotagiri Ramamohanarao, Shengliang Lu, and Jiashuai Shi. Efficient Gradient Boosted Decision Tree Training on GPUs. The 32nd IEEE Intern ational Parallel and Distributed Processing Symposium (IPDPS), pages 234-243, 2018. pdf

Key members of ThunderGBM

  • Zeyi Wen, NUS (now at The University of Western Australia)
  • Hanfeng Liu, GDUFS (a visting student at NUS)
  • Jiashuai Shi, SCUT (a visiting student at NUS)
  • Qinbin Li, NUS
  • Advisor: Bingsheng He, NUS
  • Collaborators: Jian Chen (SCUT)

Other information

  • This work is supported by a MoE AcRF Tier 2 grant (MOE2017-T2-1-122) and an NUS startup grant in Singapore.

Related libraries

Issues
  • build boost library error(win10)

    build boost library error(win10)

    python2.7.15

    boost-install.generate-cmake-config- D:\boost\boost_1_70_0\lib\cmake\boost_coroutine-1.70.0\boost_coroutine-config.cmake boost-install.generate-cmake-config-version- D:\boost\boost_1_70_0\lib\cmake\boost_coroutine-1.70.0\boost_coroutine-config-version.cmake boost-install.generate-cmake-variant- D:\boost\boost_1_70_0\lib\cmake\boost_coroutine-1.70.0\libboost_coroutine-variant-vc140-mt-gd-x32-1_70-shared.cmake boost-install.generate-cmake-config- D:\boost\boost_1_70_0\lib\cmake\boost_thread-1.70.0\boost_thread-config.cmake boost-install.generate-cmake-config-version- D:\boost\boost_1_70_0\lib\cmake\boost_thread-1.70.0\boost_thread-config-version.cmake boost-install.generate-cmake-variant- D:\boost\boost_1_70_0\lib\cmake\boost_thread-1.70.0\libboost_thread-variant-vc140-mt-gd-x32-1_70-shared.cmake boost-install.generate-cmake-config- D:\boost\boost_1_70_0\lib\cmake\boost_date_time-1.70.0\boost_date_time-config.cmake boost-install.generate-cmake-config-version- D:\boost\boost_1_70_0\lib\cmake\boost_date_time-1.70.0\boost_date_time-config-version.cmake boost-install.generate-cmake-variant- D:\boost\boost_1_70_0\lib\cmake\boost_date_time-1.70.0\libboost_date_time-variant-vc140-mt-gd-x32-1_70-shared.cmake boost-install.generate-cmake-config- D:\boost\boost_1_70_0\lib\cmake\boost_exception-1.70.0\boost_exception-config.cmake boost-install.generate-cmake-config-version- D:\boost\boost_1_70_0\lib\cmake\boost_exception-1.70.0\boost_exception-config-version.cmake ...failed updating 2949 targets... ...skipped 16 targets... ...updated 12449 targets...

    opened by lvpinrui 22
  • There was an error using thundergbm-predict file

    There was an error using thundergbm-predict file

    My environment is: Windows 10 Visual Studio 2017 Community CMake3.14.0-rc3 CUDA10.1.105 Using test_dataset.txt in the library is successful,but Using my own .txt is failed. my thundergbm-predict: image

    When building thundergbm-predict : image

    opened by lvpinrui 13
  • Make error

    Make error

    Hi, I am getting the following error when calling make -j

    [  3%] Building CXX object src/thundergbm/CMakeFiles/thundergbm.dir/objective/ranking_obj.cpp.o
    /tmp/thundergbm/src/thundergbm/objective/ranking_obj.cpp: In member function ‘virtual void LambdaRank::get_gradient(const SyncArray<float>&, const SyncArray<float>&, SyncArray<GHPair>&)’:
    /tmp/thundergbm/src/thundergbm/objective/ranking_obj.cpp:50:14: error: ‘mt19937’ is not a member of ‘std’
             std::mt19937 gen(std::rand());
                  ^~~~~~~
    /tmp/thundergbm/src/thundergbm/objective/ranking_obj.cpp:50:14: note: suggested alternative:
    In file included from /usr/include/c++/7/tr1/random:47:0,
                     from /usr/include/c++/7/parallel/random_number.h:36,
                     from /usr/include/c++/7/parallel/partition.h:38,
                     from /usr/include/c++/7/parallel/quicksort.h:36,
                     from /usr/include/c++/7/parallel/sort.h:48,
                     from /usr/include/c++/7/parallel/algo.h:45,
                     from /usr/include/c++/7/parallel/algorithm:37,
                     from /tmp/thundergbm/src/thundergbm/objective/ranking_obj.cpp:6:
    /usr/include/c++/7/tr1/random.h:701:7: note:   ‘std::tr1::mt19937’
         > mt19937;
           ^~~~~~~
    /tmp/thundergbm/src/thundergbm/objective/ranking_obj.cpp:61:33: error: ‘gen’ was not declared in this scope
                         int m = dis(gen);
                                     ^~~
    /tmp/thundergbm/src/thundergbm/objective/ranking_obj.cpp:61:33: note: suggested alternative: ‘len’
                         int m = dis(gen);
                                     ^~~
                                     len
    src/thundergbm/CMakeFiles/thundergbm.dir/build.make:11442: recipe for target 'src/thundergbm/CMakeFiles/thundergbm.dir/objective/ranking_obj.cpp.o' failed
    make[2]: *** [src/thundergbm/CMakeFiles/thundergbm.dir/objective/ranking_obj.cpp.o] Error 1
    CMakeFiles/Makefile2:131: recipe for target 'src/thundergbm/CMakeFiles/thundergbm.dir/all' failed
    make[1]: *** [src/thundergbm/CMakeFiles/thundergbm.dir/all] Error 2
    Makefile:83: recipe for target 'all' failed
    make: *** [all] Error 2
    
    
    opened by psinger 13
  • build thundergbm

    build thundergbm

    src/thundergbm/CMakeFiles/thundergbm.dir/build.make:147: recipe for target 'src/thundergbm/CMakeFiles/thundergbm.dir/thundergbm_generated_tree.cu.o' failed ...

    I have download the latest version of ThunderGBM, but it still can't make it. can you tell me how to fix it? Thank you very much!

    opened by mmobai 10
  • "File Modification Detected" and errors in Visual Studio

    downloaded the branch with cuda 11 support, for win10 and followed the instructions. When building the thundergbm.sln file in Visual Studio, I get a message that says "File Modification Detected". When choosing "Reload All" or any of the other options, the building ends up with a bunch of errors and bugs. Please see the pictures below for more details.

    These are the steps I took before getting to the building process: git clone -b support_cuda11 --single-branch https://github.com/zeyiwen/thundergbm.git cd thundergbm git submodule init cub && git submodule update cd thundergbm mkdir build cd build cmake .. -DCMAKE_WINDOWS_EXPORT_ALL_SYMBOLS=TRUE -DBUILD_SHARED_LIBS=TRUE -G "Visual Studio 16 2019" the last step is to open the file thundergbm.sln and click on "build solution" in the "Build" menu in Visual Studio.

    Here is the message window.

    File Modification Detected

    Down below is the errors generated by the building process, in the files xutility, xmemory, atomic and LINK.

    errors thundergbm

    Any idea what might be causing this?

    opened by AlanSpencer2 7
  • Bug: Memory Leak in sci-kit interface

    Bug: Memory Leak in sci-kit interface

    Hello,

    I'm running TGBM on a Windows 10 machine with Cuda 10. In my use case I create a lot of different GBDTs, which go out of scope after some time. The problem is, that the gpu memory stays allocated.

    My expectation were, that when pythons garbage collector deletes the instance, the memory gets freed. However, even when I explicitly delete the instance, the memory stays allocated.

    I think to resolve this Issue in "scikit_tgbm.cpp" the model_free function must be extended to free the allocated memory. Currently, I don't know how to do this. But maybe if I find time I will take a deeper look in memory management with Cuda.

    I found a very very ugly workaround by changing some code in the "thundergbm.py". I extended the del function of TGBMModel to reload the whole dll. This isn't a good solution but okayish as a workaround if really required by somebody.

    opened by Tripton 7
  • Building from source on ubuntu18.04 with CUDA11.0

    Building from source on ubuntu18.04 with CUDA11.0

    Hi, I am trying to build thundergbm on ubuntu18.04 with CUDA 11.0 using the instructions here. While building the binary, I get a string of warnings about C++14 (from CUB and THRUST), but the build proceeds until it hits this error:

    thundergbm/src/thundergbm/sparse_columns.cu(52): error: identifier "cusparseScsr2csc" is undefined 1 error detected in the compilation of "thundergbm/src/thundergbm/sparse_columns.cu". CMake Error at thundergbm_generated_sparse_columns.cu.o.Release.cmake:279 (message): Error generating file thundergbm/build/src/thundergbm/CMakeFiles/thundergbm.dir//./thundergbm_generated_sparse_columns.cu.o src/thundergbm/CMakeFiles/thundergbm.dir/build.make:9146: recipe for target 'src/thundergbm/CMakeFiles/thundergbm.dir/thundergbm_generated_sparse_columns.cu.o' failed make[2]: *** [src/thundergbm/CMakeFiles/thundergbm.dir/thundergbm_generated_sparse_columns.cu.o] Error 1 CMakeFiles/Makefile2:126: recipe for target 'src/thundergbm/CMakeFiles/thundergbm.dir/all' failed make[1]: *** [src/thundergbm/CMakeFiles/thundergbm.dir/all] Error 2 Makefile:83: recipe for target 'all' failed make: *** [all] Error 2

    The line that is seemingly causing the problem (sparse_columns.cu(52)) is

    usparseScsr2csc(handle, dataset.n_instances(), n_column, nnz, val.device_data(), row_ptr.device_data(), col_idx.device_data(), csc_val.device_data(), csc_row_idx.device_data(), csc_col_ptr.device_data(), CUSPARSE_ACTION_NUMERIC, CUSPARSE_INDEX_BASE_ZERO);

    Any suggestions on how to get around this?

    Thanks.

    call for contribution 
    opened by sensharma 6
  • CUDA Error 29: driver shutting down

    CUDA Error 29: driver shutting down

    I get the following error at the end of my training for TGBMRegressor with default parameters, this doesn't allow the python process to release the GPU memory and it stays there.

    2019-06-27 16:15:43,952 INFO [default] #instances = 1280, #features = 729 2019-06-27 16:15:44,031 INFO [default] copy csr matrix to GPU 2019-06-27 16:15:44,220 INFO [default] converting csr matrix to csc matrix 2019-06-27 16:15:44,483 INFO [default] Getting cut points... 2019-06-27 16:15:44,485 INFO [default] ################>>>: 1 2019-06-27 16:15:44,491 INFO [default] ----------------->>> Last LOOP: 0.0072718 2019-06-27 16:15:44,494 INFO [default] TOTAL CP:152949 2019-06-27 16:15:45,617 INFO [default] RMSE = 3.72949 2019-06-27 16:15:45,645 INFO [default] RMSE = 3.3029 2019-06-27 16:15:45,672 INFO [default] RMSE = 2.93213 2019-06-27 16:15:45,673 INFO [default] training time = 1.17665 2019-06-27 16:15:45,711 INFO [default] #instances = 549, #features = 729 CUDA error 29 [D:\thundergbm\src\thundergbm\syncmem.cpp, 576]: driver shutting down CUDA error 29 [D:\thundergbm\cub\cub/util_allocator.cuh, 657]: driver shutting down

    enhancement 
    opened by talhachattha 6
  • Make error

    Make error

    I am getting this error after executing make -j:

    /thundergbm/src/thundergbm/hist_cut.cu(142): error: a reference of type "SyncArray<float> &" (not const-qualified) cannot be initialized with a value of type "SyncArray<float_type>"
    

    I am using the code of the support_cuda11 branch (as my CUDA version is 11).

    opened by mahi045 5
  • FATAL [default] Check failed: [error == cudaSuccess]  out of memory

    FATAL [default] Check failed: [error == cudaSuccess] out of memory

    My system is:

    (_env) D:_env\project\series>nvidia-smi +-----------------------------------------------------------------------------+ | NVIDIA-SMI 442.19 Driver Version: 442.19 CUDA Version: 10.2 | |-------------------------------+----------------------+----------------------+ | GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+=================| | 0 GeForce RTX 2070 WDDM | 00000000:01:00.0 Off | N/A | | N/A 46C P8 7W / N/A | 219MiB / 8192MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |==============================================| | 0 7852 C+G ...xperience\NVIDIA GeForce Experience.exe N/A | +-----------------------------------------------------------------------------+

    I am on Windows 10

    When trying to run the TGBMClassifier I get the following error:

    2020-02-22 20:13:15,901 INFO [default] #instances = 20289, #features = 40 2020-02-22 20:13:16,503 INFO [default] convert csr to csc using gpu... 2020-02-22 20:13:16,878 INFO [default] Converting csr to csc using time: 0.363863 s 2020-02-22 20:13:16,878 INFO [default] Fast getting cut points... 2020-02-22 20:13:16,878 FATAL [default] Check failed: [error == cudaSuccess] out of memory 2020-02-22 20:13:16,894 WARNING [default] Aborting application. Reason: Fatal log at [D:_env\project\series\thundergbm\src\thundergbm\syncmem.cpp:107]

    Any suggestion how to fix this?

    opened by Kagaratsch 5
  • Request: Run quietly (surpress all prints) with sci-kit interface

    Request: Run quietly (surpress all prints) with sci-kit interface

    Hi, I am implementing ThunderGBM in an AutoML framework with the goal to optimise for speed. It would be great if we had the option to run .fit() and .predict() methods with no printing to stdout.

    I tried searching in the Python files but it became apparent that it is not there. I did see the a reply in another issue that this is currently not possible. If you tell me where/how to do this, I could give it a try.

    Thanks

    enhancement 
    opened by beevabeeva 5
  • How to visualize the ThunderGBM?

    How to visualize the ThunderGBM?

    Is there a way to see what is under the hood of the random forest? Is there a way to get the parameters of a trained model? Or maybe you can get the individual decisions trees as a nested-if-statement?

    Thanks alot!

    opened by DarkoAlexander 3
  • issue with saving and reloading model?

    issue with saving and reloading model?

    I am trying to save a trained model using: clf.save_model(path)... But when I reload the model, it doesn´t produce the same results as the saved model, despite using the same exact test data.

    I reloaded with the command clf.load_model(path).

    What is the cause of this problem? How can I save and reload models successfully?

    opened by AlanSpencer2 1
  • AttributeError: 'TGBMClassifier' object has no attribute 'save'

    AttributeError: 'TGBMClassifier' object has no attribute 'save'

    Are there functions in thundergbm that can save a trained model to file and reload it from the file later?

    There is a function "clf.predict_proba()" in sklearn that calculates probabilities of the different labels. Is there a corresponding function in thundergbm?

    There is a function in sklearn that calculates the importance of the different features, called "clf.feature_importances_". Is there is a similar function in thundergbm?

    opened by DonSeger 1
  • Debug Assertion Failed

    Debug Assertion Failed

    I have installed thundergbm with cuda 11 support. But when I try to run the thundergbm classifier inside a nested loop in order to do a hyperparameter search, I get the following error:

    Debug Asertion Failed

    Python Version: 3.7.8 Microsoft Visual Studio 19: v.16.11.8 Cuda: 11.5 cmake: 3.22.1

    Below is the code that I run in a Python Jupyter Notebook:

    for n in range(100,700,100):           
        for d in range(3,16,1):             
            for c in [0.4,0.5,0.6,0.7,0.8]:   
                                                               
                clf = TGBMClassifier(depth=d, n_trees = 1, n_parallel_trees=n, bagging=1, column_sampling_rate=c, objective = 
                                                 "binary:logistic")
                clf.fit(X_train, y_train)
    

    What is the reason?

    opened by DonSeger 0
  • the Random Forest classifies everything to be 1

    the Random Forest classifies everything to be 1

    I am new to thundergbm, and just trying to get a simple Random Forest classifier going. But the classifier classifies every single sample to be 1. Not one single case out of 188244 samples is classified as 0. No other classifier behaves like this. I also tried different number of trees, depth etc. But it still classies everything to 1. Is there something wrong with the following code?

    from thundergbm import TGBMClassifier clf = TGBMClassifier(depth=6, n_trees = 1, n_parallel_trees=100, bagging=1) clf.fit(X_train, y_train) y_pred = clf.predict(X_test)

    #y_pred classifies everything in the test set (X_test) to one.

    opened by AlanSpencer2 2
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