a2686543a9
* implement broadcast for federated communicator * implement allreduce * add communicator factory * add device adapter * add device communicator to factory * add rabit communicator * add rabit communicator to the factory * add nccl device communicator * add synchronize to device communicator * add back print and getprocessorname * add python wrapper and c api * clean up types * fix non-gpu build * try to fix ci * fix std::size_t * portable string compare ignore case * c style size_t * fix lint errors * cross platform setenv * fix memory leak * fix lint errors * address review feedback * add python test for rabit communicator * fix failing gtest * use json to configure communicators * fix lint error * get rid of factories * fix cpu build * fix include * fix python import * don't export collective.py yet * skip collective communicator pytest on windows * add review feedback * update documentation * remove mpi communicator type * fix tests * shutdown the communicator separately Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
XGBoost Plugin for Federated Learning
This folder contains the plugin for federated learning. Follow these steps to build and test it.
Install gRPC
sudo apt-get install build-essential autoconf libtool pkg-config cmake ninja-build
git clone -b v1.47.0 https://github.com/grpc/grpc
cd grpc
git submodule update --init
cmake -S . -B build -GNinja -DABSL_PROPAGATE_CXX_STD=ON
cmake --build build --target install
Build the Plugin
# Under xgboost source tree.
mkdir build
cd build
# For now NCCL needs to be turned off.
cmake .. -GNinja\
-DPLUGIN_FEDERATED=ON\
-DUSE_CUDA=ON\
-DBUILD_WITH_CUDA_CUB=ON\
-DUSE_NCCL=OFF
ninja
cd ../python-package
pip install -e . # or equivalently python setup.py develop
Test Federated XGBoost
# Under xgboost source tree.
cd tests/distributed
# This tests both CPU training (`hist`) and GPU training (`gpu_hist`).
./runtests-federated.sh