* Fix quantile tests running on multi-gpus
* Run some gtests with multiple GPUs
* fix mgpu test naming
* Instruct NCCL to print extra logs
* Allocate extra space in /dev/shm to enable NCCL
* use gtest_skip to skip mgpu tests
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Co-authored-by: Hyunsu Philip Cho <chohyu01@cs.washington.edu>
* Use array interface for CSC matrix.
Use array interface for CSC matrix and align the interface with CSR and dense.
- Fix nthread issue in the R package DMatrix.
- Unify the behavior of handling `missing` with other inputs.
- Unify the behavior of handling `missing` around R, Python, Java, and Scala DMatrix.
- Expose `num_non_missing` to the JVM interface.
- Deprecate old CSR and CSC constructors.
* Prepare for improving Windows networking compatibility.
* Include dmlc filesystem indirectly as dmlc/filesystem.h includes windows.h, which
conflicts with winsock2.h
* Define `NOMINMAX` conditionally.
* Link the winsock library when mysys32 is used.
* Add config file for read the doc.
- Use `bst_bin_t` in batch param constructor.
- Use `StringView` to avoid `std::string` when appropriate.
- Avoid using `MetaInfo` in quantile constructor to limit the scope of parameter.
- Remove unused parameters. There are still many warnings that are not yet
addressed. Currently, the warnings in dmlc-core dominate the error log.
- Remove `distributed` parameter from metric.
- Fixes some warnings about signed comparison.
- Optionally switch to c++17
- Use rmm CMake target.
- Workaround compiler errors.
- Fix GPUMetric inheritance.
- Run death tests even if it's built with RMM support.
Co-authored-by: jakirkham <jakirkham@gmail.com>
* Pass sparse page as adapter, which prepares for quantile dmatrix.
* Remove old external memory code like `rbegin` and extra `Init` function.
* Simplify type dispatch.
Support adaptive tree, a feature supported by both sklearn and lightgbm. The tree leaf is recomputed based on residue of labels and predictions after construction.
For l1 error, the optimal value is the median (50 percentile).
This is marked as experimental support for the following reasons:
- The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors.
- Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
* Use the name `Context`.
* Pass a context object into `SetInfo`.
* Add context to proxy matrix.
* Add context to iterative DMatrix.
This is to remove the use of the default number of threads during `SetInfo` as a follow-up on
removing the global omp variable while preparing for CUDA stream semantic. Currently, XGBoost
uses the legacy CUDA stream, we will gradually remove them in the future in favor of non-blocking streams.
* Generate column matrix from gHistIndex.
* Avoid synchronization with the sparse page once the cache is written.
* Cleanups: Remove member variables/functions, change the update routine to look like approx and gpu_hist.
* Remove pruner.
* Implement `MaxCategory` in quantile.
* Implement partition-based split for GPU evaluation. Currently, it's based on the existing evaluation function.
* Extract an evaluator from GPU Hist to store the needed states.
* Added some CUDA stream/event utilities.
* Update document with references.
* Fixed a bug in approx evaluator where the number of data points is less than the number of categories.
