a68580e2a7
* [jvm-packages] fix executor crashing issue when transforming on xgboost4j-spark-gpu the API XGBoosterSetParam is not thread-safe. Dring the phase of transforming, XGBoost runs several transforming tasks at a time, and each of them will set the "gpu_id" and "predictor" parameters, so if several tasks (multi-threads) all XGBoosterSetParam simultaneously, it may cause the memory to be corrupted and cause SIGSEGV. This PR first get the booster from broadcast and set to the correct gpu_id and predictor, and then all transforming taskes will use the same booster to do the transforming.
eXtreme Gradient Boosting
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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.
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© Contributors, 2021. Licensed under an Apache-2 license.
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XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the Community Page.
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.
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