[PRACTICE] Add parallel training to BaggingTrainer#1
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mattsgarlata wants to merge 3 commits intomainfrom
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[PRACTICE] Add parallel training to BaggingTrainer#1mattsgarlata wants to merge 3 commits intomainfrom
mattsgarlata wants to merge 3 commits intomainfrom
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* Bumping XGBoost to 2.1.3 * Moving to XGBoost 3.0.4 for compatibility. * Pom file updates to latest versions of Maven plugins. * Force XGBoost to save in ubj format, which has been the default since XGBoost 2.1. * Moving from Java 24 to Java 25 in the CI and adding a Linux aarch64 runner. * Adding libomp to macOS github actions. * Adding libomp install to the 21 and 25 macOS CI runs.
Implement parallel training for ensemble models using individual task submission for optimal load balancing across threads. Each model is submitted as a separate task to the executor, allowing threads to grab work as they become available. - Add configurable thread count (numThreads parameter) - Defaults to single-threaded (backward compatible) - Set numThreads=-1 to use all available processors - Use MIN_PRIORITY threads to avoid starving user-facing operations - Maintain reproducibility through pre-generated random seeds - Fall back to sequential training for single-threaded execution Performance testing shows 7-10x speedup with 16 threads on typical workloads, demonstrating substantial benefit from parallelization.
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Added testBaggingParallel() method to exercise the parallel training codepath. The test creates a BaggingTrainer with 2 threads and is integrated into testDenseData(), testSparseData(), and testSparseBinaryData() test methods.
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PRACTICE PR
Verifying we have exactly 1 commit before submitting to oracle/tribuo.
Should show commit: 8f76e13
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