* Optimisations for gpu_hist.
* Use streams to overlap operations.
* ColumnSampler now uses HostDeviceVector to prevent repeatedly copying feature vectors to the device.
* Brought the silent parameter for the SKLearn-like API back, marked it deprecated.
- added deprecation notice and warning
- removed silent from the tests for the SKLearn-like API
* Improved multi-node multi-GPU random forests.
- removed rabit::Broadcast() from each invocation of column sampling
- instead, syncing the PRNG seed when a ColumnSampler() object is constructed
- this makes non-trivial column sampling significantly faster in the distributed case
- refactored distributed GPU tests
- added distributed random forests tests
* Upgrade gtest for clang-tidy.
* Use CMake to install GTest instead of mv.
* Don't enforce clang-tidy to return 0 due to errors in thrust.
* Add a small test for tidy itself.
* Reformat.
* Added SKLearn-like random forest Python API.
- added XGBRFClassifier and XGBRFRegressor classes to SKL-like xgboost API
- also added n_gpus and gpu_id parameters to SKL classes
- added documentation describing how to use xgboost for random forests,
as well as existing caveats
* Initial commit to support multi-node multi-gpu xgboost using dask
* Fixed NCCL initialization by not ignoring the opg parameter.
- it now crashes on NCCL initialization, but at least we're attempting it properly
* At the root node, perform a rabit::Allreduce to get initial sum_gradient across workers
* Synchronizing in a couple of more places.
- now the workers don't go down, but just hang
- no more "wild" values of gradients
- probably needs syncing in more places
* Added another missing max-allreduce operation inside BuildHistLeftRight
* Removed unnecessary collective operations.
* Simplified rabit::Allreduce() sync of gradient sums.
* Removed unnecessary rabit syncs around ncclAllReduce.
- this improves performance _significantly_ (7x faster for overall training,
20x faster for xgboost proper)
* pulling in latest xgboost
* removing changes to updater_quantile_hist.cc
* changing use_nccl_opg initialization, removing unnecessary if statements
* added definition for opaque ncclUniqueId struct to properly encapsulate GetUniqueId
* placing struct defintion in guard to avoid duplicate code errors
* addressing linting errors
* removing
* removing additional arguments to AllReduer initialization
* removing distributed flag
* making comm init symmetric
* removing distributed flag
* changing ncclCommInit to support multiple modalities
* fix indenting
* updating ncclCommInitRank block with necessary group calls
* fix indenting
* adding print statement, and updating accessor in vector
* improving print statement to end-line
* generalizing nccl_rank construction using rabit
* assume device_ordinals is the same for every node
* test, assume device_ordinals is identical for all nodes
* test, assume device_ordinals is unique for all nodes
* changing names of offset variable to be more descriptive, editing indenting
* wrapping ncclUniqueId GetUniqueId() and aesthetic changes
* adding synchronization, and tests for distributed
* adding to tests
* fixing broken #endif
* fixing initialization of gpu histograms, correcting errors in tests
* adding to contributors list
* adding distributed tests to jenkins
* fixing bad path in distributed test
* debugging
* adding kubernetes for distributed tests
* adding proper import for OrderedDict
* adding urllib3==1.22 to address ordered_dict import error
* added sleep to allow workers to save their models for comparison
* adding name to GPU contributors under docs
* Fix test_gpu_coordinate.
* Use `gpu_coord_descent` in test.
* Reduce number of running rounds.
* Remove nthread.
* Use githubusercontent for r-appveyor.
* Use githubusercontent in travis r tests.
* Prevent empty quantiles
* Revise and improve unit tests for quantile hist
* Remove unnecessary comment
* Add #2943 as a test case
* Skip test if no sklearn
* Revise misleading comments
* Add checks for group size.
* Simple docs.
* Search group index during hist cut matrix initialization.
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Fix broken R test: Install Homebrew GCC
Missing GCC Fortran causes installation failure of a dependency package
(igraph)
* Register gfortran system-wide
* Use correct keg
* Set env vars to change compiler choice
* Do not break other Mac builds
* Nuclear option: symlink gfortran
* Use /usr/local/bin instead of /usr/bin
* Symlink library path too
* Update run_test.sh
* Remove GHistRow, GHistEntry, GHistIndexRow.
* Remove kSimpleStats.
* Remove CheckInfo, SetLeafVec in GradStats and in SKStats.
* Clean up the GradStats.
* Cleanup calcgain.
* Move LossChangeMissing out of common.
* Remove [] operator from GHistIndexBlock.
* Basic script for using compilation database.
* Add `GENERATE_COMPILATION_DATABASE' to CMake.
* Rearrange CMakeLists.txt.
* Add basic python clang-tidy script.
* Remove modernize-use-auto.
* Add clang-tidy to Jenkins
* Refine logic for correct path detection
In Jenkins, the project root is of form /home/ubuntu/workspace/xgboost_PR-XXXX
* Run clang-tidy in CUDA 9.2 container
* Use clang_tidy container
* Enable xgb_model parameter in XGClassifier scikit-learn API
https://github.com/dmlc/xgboost/issues/3049
* add test_XGBClassifier_resume():
test for xgb_model parameter in XGBClassifier API.
* Update test_with_sklearn.py
* Fix lint
* Fix failing Travis CI on Mac
Use Homebrew Addon + latest Mac image
* Use long command for pytest
* Downgrade OSX image to xcode9.3, to use Java 8
* Install pytest in Python 2 environment
* Remove clang-tidy from Travis
- ./testxgboost (without filters) failed if run on a multi-GPU machine because
the memory was allocated on the current device, but device 0
was always passed into LaunchN
* Use Span in gpu coordinate.
* Use Span in device code.
* Fix shard size calculation.
- Use lower_bound instead of upper_bound.
* Check empty devices.
* Unify logging facilities.
* Enhance `ConsoleLogger` to handle different verbosity.
* Override macros from `dmlc`.
* Don't use specialized gamma when building with GPU.
* Remove verbosity cache in monitor.
* Test monitor.
* Deprecate `silent`.
* Fix doc and messages.
* Fix python test.
* Fix silent tests.
* Port elementwise metrics to GPU.
* All elementwise metrics are converted to static polymorphic.
* Create a reducer for metrics reduction.
* Remove const of Metric::Eval to accommodate CubMemory.
- Improved GPU performance logging
- Only use one execute shards function
- Revert performance regression on multi-GPU
- Use threads to launch NCCL AllReduce
* use gain for sklearn feature_importances_
`gain` is a better feature importance criteria than the currently used `weight`
* added importance_type to class
* fixed test
* white space
* fix variable name
* fix deprecation warning
* fix exp array
* white spaces
