Upgrade to 1.3.0 #91
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Upgrade to 1.3.0 #91
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…ting oneAPI programming model (dmlc#5825) * 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>
* Remove parameter on JVM Packages.
…dask.DaskXGBClassifier (dmlc#5986)
* Fix nightly build doc. [skip ci] * Fix title too short. [skip ci]
* [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 e15845d. * 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 789021f. * 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>
* [CI] Cancel builds on subsequent pushes * Use a more secure method * test commit
…#5813) * 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 c2d2bdc. Co-authored-by: Qi Zhang <q.zhang@ibm.com> Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* add SHAP summary plot using ggplot2 * Update xgb.plot.shap * Update example in xgb.plot.shap documentation * update logic, add tests * whitespace fixes * whitespace fixes for test_helpers * namespace for sd function * explicitly declare variables that are automatically evaluated by data.table * Fix R lint Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
…lc#5996) The functions featureValueOfSparseVector or featureValueOfDenseVector could return a Float.NaN if the input vectore was containing any missing values. This would make fail the partition key computation and most of the vectors would end up in the same partition. We fix this by avoid returning a NaN and simply use the row HashCode in this case. We added a test to ensure that the repartition is indeed now uniform on input dataset containing values by checking that the partitions size variance is below a certain threshold. Signed-off-by: Anthony D'Amato <anthony.damato@hotmail.fr>
* Bypass RABIT serialization reducer and use custom allgather based merging.
Co-authored-by: SHVETS, KIRILL <kirill.shvets@intel.com>
* [CI] Move lint to GitHub Actions * [CI] Move Doxygen to GitHub Actions * [CI] Move Sphinx build test to GitHub Actions * [CI] Reduce workload for Windows R tests * [CI] Move clang-tidy to Build stage
…raw' (dmlc#6517) (dmlc#6524) * Enable loading model from <1.0.0 trained with objective='binary:logitraw' * Add binary:logitraw in model compatibility testing suite * Feedback from @trivialfis: Override ProbToMargin() for LogisticRaw Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com> Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.
Pscheidl
approved these changes
Jan 20, 2021
This was referenced May 26, 2023
Merged
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This PR is not valid since we go directly to xgboost 1.6: #93 |
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my changes related to the merge:
8a7186c
ae1708b