This is the one last PR for removing omp global variable.
* Add context object to the `DMatrix`. This bridges `DMatrix` with https://github.com/dmlc/xgboost/issues/7308 .
* Require context to be available at the construction time of booster.
* Add `n_threads` support for R csc DMatrix constructor.
* Remove `omp_get_max_threads` in R glue code.
* Remove threading utilities that rely on omp global variable.
This PR is meant the end the confusion around best_ntree_limit and unify model slicing. We have multi-class and random forests, asking users to understand how to set ntree_limit is difficult and error prone.
* Implement the save_best option in early stopping.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* fixed some endian issues
* Use dmlc::ByteSwap() to simplify code
* Fix lint check
* [CI] Add test for s390x
* Download latest CMake on s390x
* Fix a bug in my code
* Save magic number in dmatrix with byteswap on big-endian machine
* Save version in binary with byteswap on big-endian machine
* Load scalar with byteswap in MetaInfo
* Add a debugging message
* Handle arrays correctly when byteswapping
* EOF can also be 255
* Handle magic number in MetaInfo carefully
* Skip Tree.Load test for big-endian, since the test manually builds little-endian binary model
* Handle missing packages in Python tests
* Don't use boto3 in model compatibility tests
* Add s390 Docker file for local testing
* Add model compatibility tests
* Add R compatibility test
* Revert "Add R compatibility test"
This reverts commit c2d2bdcb7dbae133cbb927fcd20f7e83ee2b18a8.
Co-authored-by: Qi Zhang <q.zhang@ibm.com>
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Pass pointer to model parameters.
This PR de-duplicates most of the model parameters except the one in
`tree_model.h`. One difficulty is `base_score` is a model property but can be
changed at runtime by objective function. Hence when performing model IO, we
need to save the one provided by users, instead of the one transformed by
objective. Here we created an immutable version of `LearnerModelParam` that
represents the value of model parameter after configuration.
* Apply Configurable to objective functions.
* Apply Model to Learner and Regtree, gbm.
* Add Load/SaveConfig to objs.
* Refactor obj tests to use smart pointer.
* Dummy methods for Save/Load Model.
* Refactor configuration [Part II].
* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.
* Learner changes:
** Make `LearnerImpl` the only source of configuration.
All configurations are stored and carried out by `LearnerImpl::Configure()`.
** Remove booster in C API.
Originally kept for "compatibility reason", but did not state why. So here
we just remove it.
** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`. Configuration will always be lazy.
** Run `Configure` before every iteration.
* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.
`GBTree` is now used to dispatch the predictor.
** Remove some GPU Predictor tests.
* IO
No IO changes. The binary model format stability is tested by comparing
hashing value of save models between two commits
* repared serialization after update process; fixes#2545
* non-stratified folds in python could omit some data instances
* Makefile: fixes for older makes on windows; clean R-package too
* make cub to be a shallow submodule
* improve $(MAKE) recovery
