* Move thread local entry into Learner.
This is an attempt to workaround CUDA context issue in static variable, where
the CUDA context can be released before device vector.
* Add PredictionEntry to thread local entry.
This eliminates one copy of prediction vector.
* Don't define CUDA C API in a namespace.
* Use pre-rounding based method to obtain reproducible floating point
summation.
* GPU Hist for regression and classification are bit-by-bit reproducible.
* Add doc.
* Switch to thrust reduce for `node_sum_gradient`.
- move segment sorter to common
- this is the first of a handful of pr's that splits the larger pr #5326
- it moves this facility to common (from ranking objective class), so that it can be
used for metric computation
- it also wraps all the bald device pointers into span.
* Remove f-string, since it's not supported by Python 3.5 (#5330)
* Remove f-string, since it's not supported by Python 3.5
* Add Python 3.5 to CI, to ensure compatibility
* Remove duplicated matplotlib
* Show deprecation notice for Python 3.5
* Fix lint
* Fix lint
* Fix a unit test that mistook MINOR ver for PATCH ver
* Enforce only major version in JSON model schema
* Bump version to 1.1.0-SNAPSHOT
Move this function into gbtree, and uses only updater for doing so. As now the predictor knows exactly how many trees to predict, there's no need for it to update the prediction cache.
* Move prediction cache into Learner.
* Clean-ups
- Remove duplicated cache in Learner and GBM.
- Remove ad-hoc fix of invalid cache.
- Remove `PredictFromCache` in predictors.
- Remove prediction cache for linear altogether, as it's only moving the
prediction into training process but doesn't provide any actual overall speed
gain.
- The cache is now unique to Learner, which means the ownership is no longer
shared by any other components.
* Changes
- Add version to prediction cache.
- Use weak ptr to check expired DMatrix.
- Pass shared pointer instead of raw pointer.
The setup.py is rewritten. This new script uses only Python code and provide customized
implementation of setuptools commands. This way users can run most of setuptools commands
just like any other Python libraries.
* Remove setup_pip.py
* Remove soft links.
* Define customized commands.
* Remove shell script.
* Remove makefile script.
* Update the doc for building from source.
* Make pip install xgboost*.tar.gz work by fixing build-python.sh
* Simplify install doc
* Add test
* Install Miniconda for Linux target too
* Build XGBoost only once in sdist
* Try importing xgboost after installation
* Don't set PYTHONPATH env var for sdist test
* Turn xgboost::DataType into C++11 enum class
* New binary serialization format for DMatrix::MetaInfo
* Fix clang-tidy
* Fix c++ test
* Implement new format proposal
* Move helper functions to anonymous namespace; remove unneeded field
* Fix lint
* Add shape.
* Keep only roundtrip test.
* Fix test.
* various fixes
* Update data.cc
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* Simplify Scikit-Learn parameter management.
* Copy base class for removing duplicated parameter signatures.
* Set all parameters to None.
* Handle None in set_param.
* Extract the doc.
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
