CPU optimizations #4433
CPU optimizations #4433
Commits on Jun 2, 2019
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max_digits10 guarantees float decimal roundtrip (#4435)
2 additional digits are not needed to guarantee that casting the decimal representation will result in the same float, see #3980 (comment)
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Make AUCPR work with multiple query groups (#4436)
* Make AUCPR work with multiple query groups * Check AUCPR <= 1.0 in distributed setting
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In AUC and AUCPR metrics, detect whether weights are per-instance or …
…per-group (#4216) * In AUC and AUCPR metrics, detect whether weights are per-instance or per-group * Fix C++ style check * Add a test for weighted AUC
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Change obj name to
reg:squarederrorin learner. (#4427)* Change memory dump size in R test.
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[jvm-packages] Tutorial on handling missing values (#4425)
Add tutorial on missing values and how to handle those within XGBoost.
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Enable building with shared NCCL. (#4447)
* Add `BUILD_WITH_SHARED_NCCL` to CMake.
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[jvm-packages] Automatically set maximize_evaluation_metrics if not e…
…xplicitly given in XGBoost4J-Spark (#4446) * Automatically set maximize_evaluation_metrics if not explicitly given. * When custom_eval is set, require maximize_evaluation_metrics. * Update documents on early stop in XGBoost4J-Spark. * Fix code error.
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[BLOCKING][CI] Upgrade to Spark 2.4.3 (#4414)
* [CI] Upgrade to Spark 2.4.2 * Pass Spark version to build script * Allow multiple --build-arg in ci_build.sh * Fix syntax * Fix container name * Update pom.xml * Fix container name * Update Jenkinsfile * Update pom.xml * Update Dockerfile.jvm_cross
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mgpu predictor using explicit offsets (#4438)
* mgpu prediction using explicit sharding
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[CI] Build XGBoost wheels with CUDA 9.0 (#4459)
* [CI] Build XGBoost wheels with CUDA 9.0 * Do not call archiveArtifacts for 8.0 wheel
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[CI] Add Windows GPU to Jenkins CI pipeline (#4463)
* Fix #4462: Use /MT flag consistently for MSVC target * First attempt at Windows CI * Distinguish stages in Linux and Windows pipelines * Try running CMake in Windows pipeline * Add build step
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[CI] Add Python and C++ tests for Windows GPU target (#4469)
* Add CMake option to use bundled gtest from dmlc-core, so that it is easy to build XGBoost with gtest on Windows * Consistently apply OpenMP flag to all targets. Force enable OpenMP when USE_CUDA is turned on. * Insert vcomp140.dll into Windows wheels * Add C++ and Python tests for CPU and GPU targets (CUDA 9.0, 10.0, 10.1) * Prevent spurious msbuild failure * Add GPU tests * Upgrade dmlc-core
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[RFC] Version 0.90 release candidate (#4475)
* Release 0.90 * Add script to automatically generate acknowledgment * Update NEWS.md
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Add support for cross-validation using query ID (#4474)
* adding support for matrix slicing with query ID for cross-validation * hail mary test of unrar installation for windows tests * trying to modify tests to run in Github CI * Remove dependency on wget and unrar * Save error log from R test * Relax assertion in test_training * Use int instead of bool in C function interface * Revise R interface * Add XGDMatrixSliceDMatrixEx and keep old XGDMatrixSliceDMatrix for API compatibility
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[jvm-packages] Add back reg:linear for scala. (#4490)
* Add back reg:linear for scala. * Fix linter.
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Add native support for Dask (#4473)
* Add native support for Dask * Add multi-GPU demo * Add sklearn example
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Fix dask API sphinx docstrings (#4507)
* Fix dask API sphinx docstrings * Update GPU docs page
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De-duplicate GPU parameters. (#4454)
* Only define `gpu_id` and `n_gpus` in `LearnerTrainParam` * Pass LearnerTrainParam through XGBoost vid factory method. * Disable all GPU usage when GPU related parameters are not specified (fixes XGBoost choosing GPU over aggressively). * Test learner train param io. * Fix gpu pickling.
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- training with external memory part 1 of 2 (#4486)
* - training with external memory part 1 of 2 - this pr focuses on computing the quantiles using multiple gpus on a dataset that uses the external cache capabilities - there will a follow-up pr soon after this that will support creation of histogram indices on large dataset as well - both of these changes are required to support training with external memory - the sparse pages in dmatrix are taken in batches and the the cut matrices are incrementally built - also snuck in some (perf) changes related to sketches aggregation amongst multiple features across multiple sparse page batches. instead of aggregating the summary inside each device and merged later, it is aggregated in-place when the device is working on different rows but the same feature -
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Simplify INI-style config reader using C++11 STL (#4478)
* simplify the config.h file * revise config.h * revised config.h * revise format * revise format issues * revise whitespace issues * revise whitespace namespace format issues * revise namespace format issues * format issues * format issues * format issues * format issues * Revert submodule changes * minor change * Update src/common/config.h Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu> * address format issue from trivialfis * Use correct cub submodule
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Smarter choice of histogram construction for distributed gpu_hist (#…
…4519) * Smarter choice of histogram construction for distributed gpu_hist * Limit omp team size in ExecuteShards
