* 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
* bump scala to 2.12 which requires java 8 and also newer flink and akka
* put scala version in artifactId
* fix appveyor
* fix for scaladoc issue that looks like https://github.com/scala/bug/issues/10509
* fix ci_build
* update versions in generate_pom.py
* fix generate_pom.py
* apache does not have a download for spark 2.4.3 distro using scala 2.12 yet, so for now i use a tgz i put on s3
* Upload spark-2.4.3-bin-scala2.12-hadoop2.7.tgz to our own S3
* Update Dockerfile.jvm_cross
* Update Dockerfile.jvm_cross
* Fix#4630, #4421: Preserve correct ordering between metrics, and always use last metric for early stopping
* Clarify semantics of early stopping in presence of multiple valid sets and metrics
* Add a test
* Fix lint
* _maybe_pandas_xxx should return their arguments unchanged if no pandas installed
* Tests should not assume pandas is installed
* Mark tests which require pandas as such
* Fix external memory for get column batches.
This fixes two bugs:
* Use PushCSC for get column batches.
* Don't remove the created temporary directory before finishing test.
* Check all pages.
* Add to documentation how to build native unit tests
* Add instructions to run Python tests and to use Docker container [skip ci]
* Fix link to pytest chapter
* Add link to Google Test [skip ci]
* Set PYTHONPATH [skip ci]
* Revise test_python.sh for running tests locally
* Update test_python.sh
* Place Docker recommendation notice in a prominent place [skip ci]
* Initial performance optimizations for xgboost
* remove includes
* revert float->double
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* fix for CI
* Check existence of _mm_prefetch and __builtin_prefetch
* Fix lint
* optimizations for CPU
* appling comments in review
* add some comments, code refactoring
* fixing issues in CI
* adding runtime checks
* remove 1 extra check
* remove extra checks in BuildHist
* remove checks
* add debug info
* added debug info
* revert changes
* added comments
* Apply suggestions from code review
Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* apply review comments
* Remove unused function CreateNewNodes()
* Add descriptive comment on node_idx variable in QuantileHistMaker::Builder::BuildHistsBatch()
* Implement tree model dump with a code generator.
* Split up generators.
* Implement graphviz generator.
* Use pattern matching.
* [Breaking] Return a Source in `to_graphviz` instead of Digraph in Python package.
Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* - do not create device vectors for the entire sparse page while computing histograms...
- while creating the compressed histogram indices, the row vector is created for the entire
sparse page batch. this is needless as we only process chunks at a time based on a slice
of the total gpu memory
- this pr will allocate only as much as required to store the ppropriate row indices and the entries
* - do not dereference row_ptrs once the device_vector has been created to elide host copies of those counts
- instead, grab the entry counts directly from the sparsepage
* - set the appropriate device before freeing device memory...
- pr #4532 added a global memory tracker/logger to keep track of number of (de)allocations
and peak memory usage on a per device basis.
- this pr adds the appropriate check to make sure that the (de)allocation counts and memory usages
makes sense for the device. since verbosity is typically increased on debug/non-retail builds.
* - pre-create cub allocators and reuse them
- create them once and not resize them dynamically. we need to ensure that these allocators
are created and destroyed exactly once so that the appropriate device id's are set
This is part 1 of refactoring configuration.
* Move tree heuristic configurations.
* Split up declarations and definitions for GBTree.
* Implement UseGPU in gbm.
* - training with external memory - part 2 of 2
- when external memory support is enabled, building of histogram indices are
done incrementally for every sparse page
- the entire set of input data is divided across multiple gpu's and the relative
row positions within each device is tracked when building the compressed histogram buffer
- this was tested using a mortgage dataset containing ~ 670m rows before 4xt4's could be
saturated
* Fix C++11 config parser
* Use raw strings to improve readability of regex
* Fix compilation for GCC 5.x
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* - training with external memory part 1 of 2
- this pr focuses on computing the quantiles using multiple gpus on a
dataset that uses the external cache capabilities
- there will a follow-up pr soon after this that will support creation
of histogram indices on large dataset as well
- both of these changes are required to support training with external memory
- the sparse pages in dmatrix are taken in batches and the the cut matrices
are incrementally built
- also snuck in some (perf) changes related to sketches aggregation amongst multiple
features across multiple sparse page batches. instead of aggregating the summary
inside each device and merged later, it is aggregated in-place when the device
is working on different rows but the same feature
* Only define `gpu_id` and `n_gpus` in `LearnerTrainParam`
* Pass LearnerTrainParam through XGBoost vid factory method.
* Disable all GPU usage when GPU related parameters are not specified (fixes XGBoost choosing GPU over aggressively).
* Test learner train param io.
* Fix gpu pickling.
* - fix issues with training with external memory on cpu
- use the batch size to determine the correct number of rows in a batch
- use the right number of threads in omp parallalization if the batch size
is less than the default omp max threads (applicable for the last batch)
* - handle scenarios where last batch size is < available number of threads
- augment tests such that we can test all scenarios (batch size <, >, = number of threads)
* adding support for matrix slicing with query ID for cross-validation
* hail mary test of unrar installation for windows tests
* trying to modify tests to run in Github CI
* Remove dependency on wget and unrar
* Save error log from R test
* Relax assertion in test_training
* Use int instead of bool in C function interface
* Revise R interface
* Add XGDMatrixSliceDMatrixEx and keep old XGDMatrixSliceDMatrix for API compatibility
* Add CMake option to use bundled gtest from dmlc-core, so that it is easy to build XGBoost with gtest on Windows
* Consistently apply OpenMP flag to all targets. Force enable OpenMP when USE_CUDA is turned on.
* Insert vcomp140.dll into Windows wheels
* Add C++ and Python tests for CPU and GPU targets (CUDA 9.0, 10.0, 10.1)
* Prevent spurious msbuild failure
* Add GPU tests
* Upgrade dmlc-core
