* 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>
* [CI] Add RMM as an optional dependency
* Replace caching allocator with pool allocator from RMM
* Revert "Replace caching allocator with pool allocator from RMM"
This reverts commit e15845d4e72e890c2babe31a988b26503a7d9038.
* Use rmm::mr::get_default_resource()
* Try setting default resource (doesn't work yet)
* Allocate pool_mr in the heap
* Prevent leaking pool_mr handle
* Separate EXPECT_DEATH() in separate test suite suffixed DeathTest
* Turn off death tests for RMM
* Address reviewer's feedback
* Prevent leaking of cuda_mr
* Fix Jenkinsfile syntax
* Remove unnecessary function in Jenkinsfile
* [CI] Install NCCL into RMM container
* Run Python tests
* Try building with RMM, CUDA 10.0
* Do not use RMM for CUDA 10.0 target
* Actually test for test_rmm flag
* Fix TestPythonGPU
* Use CNMeM allocator, since pool allocator doesn't yet support multiGPU
* Use 10.0 container to build RMM-enabled XGBoost
* Revert "Use 10.0 container to build RMM-enabled XGBoost"
This reverts commit 789021fa31112e25b683aef39fff375403060141.
* Fix Jenkinsfile
* [CI] Assign larger /dev/shm to NCCL
* Use 10.2 artifact to run multi-GPU Python tests
* Add CUDA 10.0 -> 11.0 cross-version test; remove CUDA 10.0 target
* Rename Conda env rmm_test -> gpu_test
* Use env var to opt into CNMeM pool for C++ tests
* Use identical CUDA version for RMM builds and tests
* Use Pytest fixtures to enable RMM pool in Python tests
* Move RMM to plugin/CMakeLists.txt; use PLUGIN_RMM
* Use per-device MR; use command arg in gtest
* Set CMake prefix path to use Conda env
* Use 0.15 nightly version of RMM
* Remove unnecessary header
* Fix a unit test when cudf is missing
* Add RMM demos
* Remove print()
* Use HostDeviceVector in GPU predictor
* Simplify pytest setup; use LocalCUDACluster fixture
* Address reviewers' commments
Co-authored-by: Hyunsu Cho <chohyu01@cs.wasshington.edu>
* Added plugin with DPC++-based predictor and objective function
* Update CMakeLists.txt
* Update regression_obj_oneapi.cc
* Added README.md for OneAPI plugin
* Added OneAPI predictor support to gbtree
* Update README.md
* Merged kernels in gradient computation. Enabled multiple loss functions with DPC++ backend
* Aligned plugin CMake files with latest master changes. Fixed whitespace typos
* Removed debug output
* [CI] Make oneapi_plugin a CMake target
* Added tests for OneAPI plugin for predictor and obj. functions
* Temporarily switched to default selector for device dispacthing in OneAPI plugin to enable execution in environments without gpus
* Updated readme file.
* Fixed USM usage in predictor
* Removed workaround with explicit templated names for DPC++ kernels
* Fixed warnings in plugin tests
* Fix CMake build of gtest
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Add interval accuracy
* De-virtualize AFT functions
* Lint
* Refactor AFT metric using GPU-CPU reducer
* Fix R build
* Fix build on Windows
* Fix copyright header
* Clang-tidy
* Fix crashing demo
* Fix typos in comment; explain GPU ID
* Remove unnecessary #include
* Add C++ test for interval accuracy
* Fix a bug in accuracy metric: use log pred
* Refactor AFT objective using GPU-CPU Transform
* Lint
* Fix lint
* Use Ninja to speed up build
* Use time, not /usr/bin/time
* Add cpu_build worker class, with concurrency = 1
* Use concurrency = 1 only for CUDA build
* concurrency = 1 for clang-tidy
* Address reviewer's feedback
* Update link to AFT paper
* Implement GK sketching on GPU.
* Strong tests on quantile building.
* Handle sparse dataset by binary searching the column index.
* Hypothesis test on dask.
* Add thread local return entry for DMatrix.
* Save feature name and feature type in binary file.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Group aware GPU weighted sketching.
* Distribute group weights to each data point.
* Relax the test.
* Validate input meta info.
* Fix metainfo copy ctor.
