GitLab

* [ci] try pinning openjpeg

* pin even more

* only pin graphite2

* try removing all customizations

* restore all CI

* add a 'conda info'

* add a 'conda list' too

* move conda block in test.sh

* [ci] [python-package] build macOS x86_64 wheels on macOS 13 (Ventura)

* update Xcode version

* Update .ci/setup.sh

* fix XCode version detection

* revert debugging change

* remove dsymutil dirs

* avoid producing dsymutil reproducer directories

* restore all CI

* [ci] [R-package] re-enable 'rchk' checks

* comment out most CI

* it would be helpful to actually enable rchk

* remove all Rf_unprotect() calls to try to trigger failure

* try removing some Rf_protect() calls

* revert CI changes

* just remove rchk

* revert whitespace changes

Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [ci] skip R-devel clang16 job

* use gcc-14 on macOS builds

* try gcc-12

[ci] Require CMake 3.28 and replace `FetchContent_Populate` with `FetchContent_MakeAvailable` (#6550)

* Replace `FetchContent_Populate` with `FetchContent_MakeAvailable`

* Test with VS 2022

* Update IntegratedOpenCL.cmake

* Update IntegratedOpenCL.cmake

* Update IntegratedOpenCL.cmake

* Update IntegratedOpenCL.cmake

* Update IntegratedOpenCL.cmake

* Update .vsts-ci.yml

* bump minimum CMake version

* force symlinking

* fix line length

* install curl

* install sudo

* install certs

* install CMake for R Windows jobs

* debug cmake

* new try

* remove CMake from RTools

* cleanup

* remove duplicted curl installation

* CMake version as variable

[ci] [python-package] run macOS x86_64 tests on macOS 12 (Monterey), stop manually setting macOS wheel tags (#6487)

[ci] Skip Arrow tests on AppVeyor, use Intel macOS runners, upgrade to XCode 14.3 on macOS jobs, disable MacOS MPI jobs (#6425)

* Update .vsts-ci.yml for Azure Pipelines

Upgrade Azure VM Scale Set to use Mariner (Azure Linux) systems.

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* upgrade clang to clang-17

* add -y in apt install

* update name of mariner agent pool

* fix clang 17 compiler name

* apt update before install clang 17

* update libomp version for clang 17

* use clang-17 only for Mariner agents

* fix CXX and CC

* [ci] speed up if-else, swig, and lint conda setup

* add 'source activate'

* python constraint

* start removing cuda v1

* comment out CI

* remove more references

* revert some unnecessaary changes

* revert a few more mistakes

* revert another change that ignored params

* sigh

* remove CUDATreeLearner

* fix tests, docs

* fix quoting in setup.py

* restore all CI

* Apply suggestions from code review
Co-authored-by: shiyu1994 <shiyu_k1994@qq.com>

* Apply suggestions from code review

* completely remove cuda_exp, update docs

---------
Co-authored-by: shiyu1994 <shiyu_k1994@qq.com>

* Add integrated OpenCL build on Linux

* Build integrated OpenCL Linux wheel in CI

* Fix test_dual.py on Linux arm64

* Enable integrated OpenCL Linux wheel arm64 testing in CI

* Update documentation

* Add comment about gpu_use_dp

* add missing fi dropped in merge conflict resolution

* install opencl-headers on bdist task

* use new CI image for x86_64

* update check_dynamic_dependencies script

* use main CI image
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Install PoCL in Docker for old Ubuntu 14.04

* Update .vsts-ci.yml

* Try with Clang

* switch back to gcc

* Switch from dev container to stable one

* Run OpenCL tests against POCL instead of the AMD App SDK

* Update .ci/setup.sh
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Run Linux gpu source on default Python version

* [docs] Update GPU Targets Table
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* new cuda framework

* add histogram construction kernel

* before removing multi-gpu

* new cuda framework

* tree learner cuda kernels

* single tree framework ready

* single tree training framework

* remove comments

* boosting with cuda

* optimize for best split find

* data split

* move boosting into cuda

* parallel synchronize best split point

* merge split data kernels

* before code refactor

* use tasks instead of features as units for split finding

* refactor cuda best split finder

* fix configuration error with small leaves in data split

* skip histogram construction of too small leaf

* skip split finding of invalid leaves

stop when no leaf to split

* support row wise with CUDA

* copy data for split by column

* copy data from host to CPU by column for data partition

* add synchronize best splits for one leaf from multiple blocks

* partition dense row data

* fix sync best split from task blocks

* add support for sparse row wise for CUDA

* remove useless code

* add l2 regression objective

* sparse multi value bin enabled for CUDA

* fix cuda ranking objective

* support for number of items <= 2048 per query

* speedup histogram construction by interleaving global memory access

* split optimization

* add cuda tree predictor

* remove comma

* refactor objective and score updater

* before use struct

* use structure for split information

* use structure for leaf splits

* return CUDASplitInfo directly after finding best split

* split with CUDATree directly

* use cuda row data in cuda histogram constructor

* clean src/treelearner/cuda

* gather shared cuda device functions

* put shared CUDA functions into header file

* change smaller leaf from <= back to < for consistent result with CPU

* add tree predictor

* remove useless cuda_tree_predictor

* predict on CUDA with pipeline

* add global sort algorithms

* add global argsort for queries with many items in ranking tasks

* remove limitation of maximum number of items per query in ranking

* add cuda metrics

* fix CUDA AUC

* remove debug code

* add regression metrics

* remove useless file

* don't use mask in shuffle reduce

* add more regression objectives

* fix cuda mape loss

add cuda xentropy loss

* use template for different versions of BitonicArgSortDevice

* add multiclass metrics

* add ndcg metric

* fix cross entropy objectives and metrics

* fix cross entropy and ndcg metrics

* add support for customized objective in CUDA

* complete multiclass ova for CUDA

* separate cuda tree learner

* use shuffle based prefix sum

* clean up cuda_algorithms.hpp

* add copy subset on CUDA

* add bagging for CUDA

* clean up code

* copy gradients from host to device

* support bagging without using subset

* add support of bagging with subset for CUDAColumnData

* add support of bagging with subset for dense CUDARowData

* refactor copy sparse subrow

* use copy subset for column subset

* add reset train data and reset config for CUDA tree learner

add deconstructors for cuda tree learner

* add USE_CUDA ifdef to cuda tree learner files

* check that dataset doesn't contain CUDA tree learner

* remove printf debug information

* use full new cuda tree learner only when using single GPU

* disable all CUDA code when using CPU version

* recover main.cpp

* add cpp files for multi value bins

* update LightGBM.vcxproj

* update LightGBM.vcxproj

fix lint errors

* fix lint errors

* fix lint errors

* update Makevars

fix lint errors

* fix the case with 0 feature and 0 bin

fix split finding for invalid leaves

create cuda column data when loaded from bin file

* fix lint errors

hide GetRowWiseData when cuda is not used

* recover default device type to cpu

* fix na_as_missing case

fix cuda feature meta information

* fix UpdateDataIndexToLeafIndexKernel

* create CUDA trees when needed in CUDADataPartition::UpdateTrainScore

* add refit by tree for cuda tree learner

* fix test_refit in test_engine.py

* create set of large bin partitions in CUDARowData

* add histogram construction for columns with a large number of bins

* add find best split for categorical features on CUDA

* add bitvectors for categorical split

* cuda data partition split for categorical features

* fix split tree with categorical feature

* fix categorical feature splits

* refactor cuda_data_partition.cu with multi-level templates

* refactor CUDABestSplitFinder by grouping task information into struct

* pre-allocate space for vector split_find_tasks_ in CUDABestSplitFinder

* fix misuse of reference

* remove useless changes

* add support for path smoothing

* virtual destructor for LightGBM::Tree

* fix overlapped cat threshold in best split infos

* reset histogram pointers in data partition and spllit finder in ResetConfig

* comment useless parameter

* fix reverse case when na is missing and default bin is zero

* fix mfb_is_na and mfb_is_zero and is_single_feature_column

* remove debug log

* fix cat_l2 when one-hot

fix gradient copy when data subset is used

* switch shared histogram size according to CUDA version

* gpu_use_dp=true when cuda test

* revert modification in config.h

* fix setting of gpu_use_dp=true in .ci/test.sh

* fix linter errors

* fix linter error

remove useless change

* recover main.cpp

* separate cuda_exp and cuda

* fix ci bash scripts

add description for cuda_exp

* add USE_CUDA_EXP flag

* switch off USE_CUDA_EXP

* revert changes in python-packages

* more careful separation for USE_CUDA_EXP

* fix CUDARowData::DivideCUDAFeatureGroups

fix set fields for cuda metadata

* revert config.h

* fix test settings for cuda experimental version

* skip some tests due to unsupported features or differences in implementation details for CUDA Experimental version

* fix lint issue by adding a blank line

* fix lint errors by resorting imports

* fix lint errors by resorting imports

* fix lint errors by resorting imports

* merge cuda.yml and cuda_exp.yml

* update python version in cuda.yml

* remove cuda_exp.yml

* remove unrelated changes

* fix compilation warnings

fix cuda exp ci task name

* recover task

* use multi-level template in histogram construction

check split only in debug mode

* ignore NVCC related lines in parameter_generator.py

* update job name for CUDA tests

* apply review suggestions

* Update .github/workflows/cuda.yml
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update .github/workflows/cuda.yml
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update header

* remove useless TODOs

* remove [TODO(shiyu1994): constrain the split with min_data_in_group] and record in #5062

* #include <LightGBM/utils/log.h> for USE_CUDA_EXP only

* fix include order

* fix include order

* remove extra space

* address review comments

* add warning when cuda_exp is used together with deterministic

* add comment about gpu_use_dp in .ci/test.sh

* revert changing order of included headers
Co-authored-by: Yu Shi <shiyu1994@qq.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [ci] use conda-forge in CI jobs (fixes #4948)

* comment out more jobs

* try reverting graphviz patch, running more cuda jobs

* get graphviz from PyPI and try removing some patches for r-lintr

* start running appveyor again

* use conda-forge if using conda

* fix commands

* conda install graphviz

* try newer openmp

* pin below openmp 11.x

* focus on gpu task

* trying to narrow down error

* maybe gcc11 is the issue

* start adding other tests back

* pin openmp too

* maybe need to pin to gcc less than 10.x

* pin libgfortran and libstdcxx as well

* pin to gcc 9.3.0

* move constraints up to initial environment

* add all CI jobs back

* try installing python-graphviz separately

* try new lightgbm/vsts-agent image

* fix typo

* test if pinning gcc for linux gpu_source build is still necessary

* ok yes, pinning gcc is necessary

* test if Linux gpu_source works with Python 3.9.6

* no special exception for Linux gpu_source job

* pin to Python 3.9.6 in Linux gpu_source

* try explicitly asking for libstdcxx-ng for every linux build

* swap compilers

* switch compilers back

* revert accidental whitespace change

* comment out CI

* try Linux gpu_source with different Python versions

* Revert "try Linux gpu_source with different Python versions"

This reverts commit f6f63cbb9b4a9cf138f3580ae4223a8acdd0e94a.

* Revert "comment out CI"

This reverts commit ece191f01e3650c2f325e80ff86bfc8c485fb7bc.

* remove libxml2 install, change CONDA path

* avoid installing conda in rchk job

* empty commit 1

* empty commit 2

* empty commit 3

* empty commit 4

* add more verbose logging around installation of python-graphviz

* empty commit 1

* get mamba info

* get more conda info

* add another mamba info call

* allow for other macOS environments in GHA configuration

* Revert "allow for other macOS environments in GHA configuration"

This reverts commit a3c7a19926be94e3719f5ae9100fbe30e87b35da.

* get more logs from mamba

* get Build.ArtifactsStagingDirectory

* get more logs and try to force re-installing everything

* clean cache after every step

* remove --update-all and make logs less verbose

* remove more print statements and uncomment jobs

* test if conda-clean issue fixes segfaults for gpu_source

* pin python version for gpu_source

* empty commit 1

* use miniforge instead

* empty commit 1

* Apply suggestions from code review

* bring workarounds back

* remove duplicated graphviz system-wide installation (reverts #4095, #4097, #4238)

* empty commit 1

* empty commit 2

* empty commit 3

* empty commit 4

* empty commit 5

* empty commit 6

* empty commit 7

* empty commit 8

* empty commit 9

* empty commit 10

* empty commit 10

* empty commit 10

* empty commit 10

* empty commit 11

* one more try

* try to downgrade Python version for Linux GPU job

* swap compilers

* Revert "swap compilers"

This reverts commit f04dc27b17920a69cbcba1254a8e109ce9791154.
Co-authored-by: Nikita Titov <nekit94-12@hotmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Revert "[ci] ignore certificates for kitware apt channel in CUDA jobs (fixes #4646) (#4648)"

This reverts commit 10e0edc4.

* update cuda at CI

* Update .vsts-ci.yml

* Update setup.sh

* Update .vsts-ci.yml

* Update test.sh

* Update README.rst

* [ci] ignore certificates for kitware apt channel in CUDA jobs (fixes #4646)

* try building from a fork

* Update .gitmodules

* Install graphviz from default conda channel

* Update test.sh

* Update setup.sh

* Update test.sh

* Update setup.sh

* Update setup.sh

* Update setup.sh

* Update setup.sh

* Update setup.sh

* Update setup.sh

This reverts commit ab474dc4.

* restrict CMake version for CUDA jobs

* Update setup.sh

* Update setup.sh

* Update setup.sh

* Update test.sh

* Update setup.sh

* Update .vsts-ci.yml

* Update test.sh

* Update setup.sh

* Update .vsts-ci.yml

* Update setup.sh

* Update setup.sh

* Update setup.sh

* Update test.sh

* Update test_dask.py

* Update test_engine.py

* Update .vsts-ci.yml

* Update test_r_package.sh

* Update dockerfile.gpu

* Update dockerfile-python

* Update setup.sh

* Update setup.sh

* test different CUDA versions

* Update cuda.yml

* Update cuda.yml

* Update setup.sh

* Update setup.sh

* Update cuda.yml

* Update cuda.yml

* Update setup.sh

* Update setup.sh

* Update setup.sh

* Update setup.sh

* Update cuda.yml

* Update cuda.yml

* Update cuda.yml

* Update CMakeLists.txt

* Update CMakeLists.txt

* Update cuda.yml

* Update cuda.yml

* Update cuda.yml

* Update CMakeLists.txt

* specify CUDA version in workflow job name