This is part 1 of refactoring configuration.
* Move tree heuristic configurations.
* Split up declarations and definitions for GBTree.
* Implement UseGPU in gbm.
* - training with external memory - part 2 of 2
- when external memory support is enabled, building of histogram indices are
done incrementally for every sparse page
- the entire set of input data is divided across multiple gpu's and the relative
row positions within each device is tracked when building the compressed histogram buffer
- this was tested using a mortgage dataset containing ~ 670m rows before 4xt4's could be
saturated
* Fix C++11 config parser
* Use raw strings to improve readability of regex
* Fix compilation for GCC 5.x
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* 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
* - 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
* 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.
* - fix issues with training with external memory on cpu
- use the batch size to determine the correct number of rows in a batch
- use the right number of threads in omp parallalization if the batch size
is less than the default omp max threads (applicable for the last batch)
* - handle scenarios where last batch size is < available number of threads
- augment tests such that we can test all scenarios (batch size <, >, = number of threads)
* 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
* 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
* Make CMakeLists.txt compatible with CMake 3.3; require CMake 3.11 for MSVC
* Use CMake 3.12 when sanitizer is enabled
* Disable funroll-loops for MSVC
* Use cmake version in container name
* Add missing arg
* Fix egrep use in ci_build.sh
* Display CMake version
* Do not set OpenMP_CXX_LIBRARIES for MSVC
* Use cmake_minimum_required()
* Use feature interaction constraints to narrow search space for split candidates.
* fix clang-tidy broken at updater_quantile_hist.cc:535:3
* make const
* fix
* try to fix exception thrown in java_test
* fix suspected mistake which cause EvaluateSplit error
* try fix
* Fix bug: feature ID and node ID swapped in argument
* Rename CheckValidation() to CheckFeatureConstraint() for clarity
* Do not create temporary vector validFeatures, to enable parallelism
* Combine thread launches into single launch per tree for gpu_hist
algorithm.
* Address deprecation warning
* Add manual column sampler constructor
* Turn off omp dynamic to get a guaranteed number of threads
* Enable openmp in cuda code
* Refactor CMake scripts.
* Remove CMake CUDA wrapper.
* Bump CMake version for CUDA.
* Use CMake to handle Doxygen.
* Split up CMakeList.
* Export install target.
* Use modern CMake.
* Remove build.sh
* Workaround for gpu_hist test.
* Use cmake 3.12.
* Revert machine.conf.
* Move CLI test to gpu.
* Small cleanup.
* Support using XGBoost as submodule.
* Fix windows
* Fix cpp tests on Windows
* Remove duplicated find_package.
* Fix Histogram allocation.
nidx_map is cleared after `Reset`, but histogram data size isn't changed hence
histogram recycling is used in later iterations. After a reset(building new
tree), newly allocated node will start from 0, while recycling always choose
the node with smallest index, which happens to be our newly allocated node 0.
* make the assignments of HostDeviceVector exception safe.
* storing a dummy GPUDistribution instance in HDV for CPU based code.
* change testxgboost binary location to build directory.
* Optimisations for gpu_hist.
* Use streams to overlap operations.
* ColumnSampler now uses HostDeviceVector to prevent repeatedly copying feature vectors to the device.
