310fe60b35
* - pairwise ranking objective implementation on gpu
- there are couple of more algorithms (ndcg and map) for which support will be added
as follow-up pr's
- with no label groups defined, get gradient is 90x faster on gpu (120m instance
mortgage dataset)
- it can perform by an order of magnitude faster with ~ 10 groups (and adequate cores
for the cpu implementation)
* Add JSON config to rank obj.
18 lines
326 B
C++
18 lines
326 B
C++
/*!
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* Copyright 2019 XGBoost contributors
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*/
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// Dummy file to keep the CUDA conditional compile trick.
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#include <dmlc/registry.h>
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namespace xgboost {
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namespace obj {
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DMLC_REGISTRY_FILE_TAG(rank_obj);
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} // namespace obj
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} // namespace xgboost
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#ifndef XGBOOST_USE_CUDA
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#include "rank_obj.cu"
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#endif // XGBOOST_USE_CUDA
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