Gpu版lightgbm的免编译安装文件

LightGBM, Light Gradient Boosting Machine ========================================= [](https://travis-ci.org/Microsoft/LightGBM) [](https://ci.appveyor.com/project/guolinke/lightgbm/branch/master) [](https://lightgbm.readthedocs.io/) [](https://github.com/Microsoft/LightGBM/issues) [](https://github.com/Microsoft/LightGBM/blob/master/LICENSE) [](https://pypi.python.org/pypi/lightgbm) [](https://pypi.python.org/pypi/lightgbm) [](https://gitter.im/Microsoft/LightGBM?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [](https://lightgbm-slack-autojoin.herokuapp.com/) LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: - Faster training speed and higher efficiency - Lower memory usage - Better accuracy - Parallel and GPU learning supported - Capable of handling large-scale data For more details, please refer to [Features](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst). Benefit from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/Microsoft/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions. [Comparison experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, the [parallel experiments](https://github.com/Microsoft/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings. News ---- 08/15/2017 : Optimal split for categorical features. 07/13/2017 : [Gitter](https://gitter.im/Microsoft/LightGBM) is available. 06/20/2017 : Python-package is on [PyPI](https://pypi.python.org/pypi/lightgbm) now. 06/09/2017 : [LightGBM Slack team](https://lightgbm.slack.com) is available. 05/03/2017 : LightGBM v2 stable release. 04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our [GPU Tutorial](./docs/GPU-Tutorial.rst) and [Performance Comparison](./docs/GPU-Performance.rst). 02/20/2017 : Update to LightGBM v2. 02/12/2017 : LightGBM v1 stable release. 01/08/2017 : Release [**R-package**](https://github.com/Microsoft/LightGBM/tree/master/R-package) beta version, welcome to have a try and provide feedback. 12/05/2016 : **Categorical Features as input directly** (without one-hot coding). 12/02/2016 : Release [**Python-package**](https://github.com/Microsoft/LightGBM/tree/master/python-package) beta version, welcome to have a try and provide feedback. More detailed update logs : [Key Events](https://github.com/Microsoft/LightGBM/blob/master/docs/Key-Events.md). External (unofficial) Repositories ---------------------------------- Julia Package: https://github.com/Allardvm/LightGBM.jl JPMML: https://github.com/jpmml/jpmml-lightgbm Get Started and Documentation ----------------------------- Install by following the [guide](https://github.com/Microsoft/LightGBM/blob/master/docs/Installation-Guide.rst) for the command line program, [Python-package](https://github.com/Microsoft/LightGBM/tree/master/python-package) or [R-package](https://github.com/Microsoft/LightGBM/tree/master/R-package). Then please see the [Quick Start](https://github.com/Microsoft/LightGBM/blob/master/docs/Quick-Start.rst) guide. Our primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository. Next you may want to read: * [**Examples**](https://github.com/Microsoft/LightGBM/tree/master/examples) showing command line usage of common tasks * [**Features**](https://github.com/Microsoft/LightGBM/blob/master/docs/Features.rst) and algorithms supported by LightGBM * [**Parameters**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make * [**Parallel Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/Microsoft/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation * [**Laurae++ interactive documentation**](https://sites.google.com/view/lauraepp/parameters) is a detailed guide for hyperparameters Documentation for contributors: * [**How we update readthedocs.io**](https://github.com/Microsoft/LightGBM/blob/master/docs/README.rst) * Check out the [**Development Guide**](https://github.com/Microsoft/LightGBM/blob/master/docs/Development-Guide.rst). Support ------- * Ask a question [on Stack Overflow with the `lightgbm` tag](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions. * Discuss on the [LightGBM Gitter](https://gitter.im/Microsoft/LightGBM). * Discuss on the [LightGBM Slack team](https://lightgbm.slack.com). * Use [this invite link](https://lightgbm-slack-autojoin.herokuapp.com/) to join the team. * Open **bug reports** and **feature requests** (not questions) on [GitHub issues](https://github.com/Microsoft/LightGBM/issues). How to Contribute ----------------- LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone. - Check out [call for contributions](https://github.com/Microsoft/LightGBM/issues?q=is%3Aissue+is%3Aopen+label%3Acall-for-contribution) to see what can be improved, or open an issue if you want something. - Contribute to the [tests](https://github.com/Microsoft/LightGBM/tree/master/tests) to make it more reliable. - Contribute to the [documents](https://github.com/Microsoft/LightGBM/tree/master/docs) to make it clearer for everyone. - Contribute to the [examples](https://github.com/Microsoft/LightGBM/tree/master/examples) to share your experience with other users. - Add your stories and experience to [Awesome LightGBM](https://github.com/Microsoft/LightGBM/blob/master/examples/README.md). - Open issue if you met problems during development. Microsoft Open Source Code of Conduct ------------------------------------- This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. Reference Paper --------------- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. "[LightGBM: A Highly Efficient Gradient Boosting Decision Tree](https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree)". In Advances in Neural Information Processing Systems (NIPS), pp. 3149-3157. 2017. Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tieyan Liu. "[A Communication-Efficient Parallel Algorithm for Decision Tree](http://papers.nips.cc/paper/6380-a-communication-efficient-parallel-algorithm-for-decision-tree)". Advances in Neural Information Processing Systems 29 (NIPS 2016). Huan Zhang, Si Si