Support building XGBoost with CUDA 11 (#5808)
* Change serialization test. * Add CUDA 11 tests on Linux CI. Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
Andy Adinets
GitHub
parent
ac9136ee49
commit
b3d2e7644a
76
Jenkinsfile
vendored
76
Jenkinsfile
vendored
@ -6,6 +6,9 @@
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// Command to run command inside a docker container
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dockerRun = 'tests/ci_build/ci_build.sh'
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// Which CUDA version to use when building reference distribution wheel
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ref_cuda_ver = '10.0'
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import groovy.transform.Field
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@Field
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@ -65,8 +68,13 @@ pipeline {
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'build-cpu': { BuildCPU() },
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'build-cpu-rabit-mock': { BuildCPUMock() },
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'build-cpu-non-omp': { BuildCPUNonOmp() },
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// Build reference, distribution-ready Python wheel with CUDA 10.0
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// using CentOS 6 image
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'build-gpu-cuda10.0': { BuildCUDA(cuda_version: '10.0') },
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// The build-gpu-* builds below use Ubuntu image
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'build-gpu-cuda10.1': { BuildCUDA(cuda_version: '10.1') },
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'build-gpu-cuda10.2': { BuildCUDA(cuda_version: '10.2') },
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'build-gpu-cuda11.0': { BuildCUDA(cuda_version: '11.0') },
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'build-jvm-packages': { BuildJVMPackages(spark_version: '3.0.0') },
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'build-jvm-doc': { BuildJVMDoc() }
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])
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@ -80,11 +88,12 @@ pipeline {
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script {
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parallel ([
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'test-python-cpu': { TestPythonCPU() },
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'test-python-gpu-cuda10.0': { TestPythonGPU(cuda_version: '10.0') },
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'test-python-gpu-cuda10.1': { TestPythonGPU(cuda_version: '10.1') },
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'test-python-mgpu-cuda10.1': { TestPythonGPU(cuda_version: '10.1', multi_gpu: true) },
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'test-cpp-gpu': { TestCppGPU(cuda_version: '10.1') },
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'test-cpp-mgpu': { TestCppGPU(cuda_version: '10.1', multi_gpu: true) },
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'test-python-gpu-cuda10.0': { TestPythonGPU(host_cuda_version: '10.0') },
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'test-python-gpu-cuda10.2': { TestPythonGPU(host_cuda_version: '10.2') },
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'test-python-gpu-cuda11.0': { TestPythonGPU(artifact_cuda_version: '11.0', host_cuda_version: '11.0') },
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'test-python-mgpu-cuda10.2': { TestPythonGPU(host_cuda_version: '10.2', multi_gpu: true) },
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'test-cpp-gpu-cuda10.2': { TestCppGPU(artifact_cuda_version: '10.2', host_cuda_version: '10.2') },
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'test-cpp-gpu-cuda11.0': { TestCppGPU(artifact_cuda_version: '11.0', host_cuda_version: '11.0') },
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'test-jvm-jdk8': { CrossTestJVMwithJDK(jdk_version: '8', spark_version: '3.0.0') },
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'test-jvm-jdk11': { CrossTestJVMwithJDK(jdk_version: '11') },
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'test-jvm-jdk12': { CrossTestJVMwithJDK(jdk_version: '12') },
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@ -123,6 +132,10 @@ def checkoutSrcs() {
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}
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}
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def GetCUDABuildContainerType(cuda_version) {
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return (cuda_version == ref_cuda_ver) ? 'gpu_build_centos6' : 'gpu_build'
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}
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def ClangTidy() {
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node('linux && cpu_build') {
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unstash name: 'srcs'
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@ -244,7 +257,7 @@ def BuildCUDA(args) {
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node('linux && cpu_build') {
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unstash name: 'srcs'
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echo "Build with CUDA ${args.cuda_version}"
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def container_type = "gpu_build"
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def container_type = GetCUDABuildContainerType(args.cuda_version)
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def docker_binary = "docker"
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def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
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def arch_flag = ""
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@ -254,20 +267,17 @@ def BuildCUDA(args) {
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sh """
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${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/build_via_cmake.sh -DUSE_CUDA=ON -DUSE_NCCL=ON -DOPEN_MP:BOOL=ON -DHIDE_CXX_SYMBOLS=ON ${arch_flag}
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${dockerRun} ${container_type} ${docker_binary} ${docker_args} bash -c "cd python-package && rm -rf dist/* && python setup.py bdist_wheel --universal"
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${dockerRun} ${container_type} ${docker_binary} ${docker_args} python3 tests/ci_build/rename_whl.py python-package/dist/*.whl ${commit_id} manylinux2010_x86_64
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${dockerRun} ${container_type} ${docker_binary} ${docker_args} python tests/ci_build/rename_whl.py python-package/dist/*.whl ${commit_id} manylinux2010_x86_64
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"""
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// Stash wheel for CUDA 10.0 target
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if (args.cuda_version == '10.0') {
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echo 'Stashing Python wheel...'
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stash name: 'xgboost_whl_cuda10', includes: 'python-package/dist/*.whl'
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if (env.BRANCH_NAME == 'master' || env.BRANCH_NAME.startsWith('release')) {
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echo 'Uploading Python wheel...'
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path = ("${BRANCH_NAME}" == 'master') ? '' : "${BRANCH_NAME}/"
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s3Upload bucket: 'xgboost-nightly-builds', path: path, acl: 'PublicRead', workingDir: 'python-package/dist', includePathPattern:'**/*.whl'
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}
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echo 'Stashing C++ test executable (testxgboost)...'
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stash name: 'xgboost_cpp_tests', includes: 'build/testxgboost'
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echo 'Stashing Python wheel...'
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stash name: "xgboost_whl_cuda${args.cuda_version}", includes: 'python-package/dist/*.whl'
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if (args.cuda_version == ref_cuda_ver && (env.BRANCH_NAME == 'master' || env.BRANCH_NAME.startsWith('release'))) {
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echo 'Uploading Python wheel...'
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path = ("${BRANCH_NAME}" == 'master') ? '' : "${BRANCH_NAME}/"
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s3Upload bucket: 'xgboost-nightly-builds', path: path, acl: 'PublicRead', workingDir: 'python-package/dist', includePathPattern:'**/*.whl'
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}
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echo 'Stashing C++ test executable (testxgboost)...'
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stash name: "xgboost_cpp_tests_cuda${args.cuda_version}", includes: 'build/testxgboost'
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deleteDir()
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}
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}
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@ -308,7 +318,7 @@ def BuildJVMDoc() {
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def TestPythonCPU() {
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node('linux && cpu') {
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unstash name: 'xgboost_whl_cuda10'
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unstash name: "xgboost_whl_cuda${ref_cuda_ver}"
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unstash name: 'srcs'
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unstash name: 'xgboost_cli'
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echo "Test Python CPU"
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@ -322,15 +332,16 @@ def TestPythonCPU() {
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}
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def TestPythonGPU(args) {
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nodeReq = (args.multi_gpu) ? 'linux && mgpu' : 'linux && gpu'
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def nodeReq = (args.multi_gpu) ? 'linux && mgpu' : 'linux && gpu'
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def artifact_cuda_version = (args.artifact_cuda_version) ?: ref_cuda_ver
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node(nodeReq) {
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unstash name: 'xgboost_whl_cuda10'
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unstash name: 'xgboost_cpp_tests'
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unstash name: "xgboost_whl_cuda${artifact_cuda_version}"
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unstash name: "xgboost_cpp_tests_cuda${artifact_cuda_version}"
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unstash name: 'srcs'
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echo "Test Python GPU: CUDA ${args.cuda_version}"
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echo "Test Python GPU: CUDA ${args.host_cuda_version}"
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def container_type = "gpu"
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def docker_binary = "nvidia-docker"
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def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
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def docker_args = "--build-arg CUDA_VERSION=${args.host_cuda_version}"
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if (args.multi_gpu) {
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echo "Using multiple GPUs"
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sh """
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@ -361,21 +372,16 @@ def TestCppRabit() {
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}
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def TestCppGPU(args) {
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nodeReq = (args.multi_gpu) ? 'linux && mgpu' : 'linux && gpu'
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def nodeReq = 'linux && mgpu'
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def artifact_cuda_version = (args.artifact_cuda_version) ?: ref_cuda_ver
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node(nodeReq) {
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unstash name: 'xgboost_cpp_tests'
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unstash name: "xgboost_cpp_tests_cuda${artifact_cuda_version}"
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unstash name: 'srcs'
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echo "Test C++, CUDA ${args.cuda_version}"
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echo "Test C++, CUDA ${args.host_cuda_version}"
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def container_type = "gpu"
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def docker_binary = "nvidia-docker"
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def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
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if (args.multi_gpu) {
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echo "Using multiple GPUs"
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sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost --gtest_filter=*.MGPU_*"
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} else {
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echo "Using a single GPU"
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sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost --gtest_filter=-*.MGPU_*"
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}
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def docker_args = "--build-arg CUDA_VERSION=${args.host_cuda_version}"
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sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost"
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deleteDir()
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}
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}
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@ -9,7 +9,9 @@ if (USE_CUDA)
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file(GLOB_RECURSE CUDA_SOURCES *.cu *.cuh)
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target_sources(objxgboost PRIVATE ${CUDA_SOURCES})
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target_compile_definitions(objxgboost PRIVATE -DXGBOOST_USE_CUDA=1)
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target_include_directories(objxgboost PRIVATE ${xgboost_SOURCE_DIR}/cub/)
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if (CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.0)
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target_include_directories(objxgboost PRIVATE ${xgboost_SOURCE_DIR}/cub/)
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endif (CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.0)
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target_compile_options(objxgboost PRIVATE
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$<$<COMPILE_LANGUAGE:CUDA>:--expt-extended-lambda>
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$<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
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@ -1,53 +1,30 @@
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ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-devel-centos6
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FROM nvidia/cuda:$CUDA_VERSION-devel-ubuntu16.04
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ARG CUDA_VERSION
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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ENV DEVTOOLSET_URL_ROOT http://vault.centos.org/6.9/sclo/x86_64/rh/devtoolset-4/
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SHELL ["/bin/bash", "-c"] # Use Bash as shell
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# Install all basic requirements
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RUN \
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yum -y update && \
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yum install -y tar unzip wget xz git centos-release-scl yum-utils && \
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yum-config-manager --enable centos-sclo-rh-testing && \
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yum -y update && \
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yum install -y $DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-5.3.1-6.1.el6.x86_64.rpm \
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$DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-c++-5.3.1-6.1.el6.x86_64.rpm \
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$DEVTOOLSET_URL_ROOT/devtoolset-4-binutils-2.25.1-8.el6.x86_64.rpm \
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$DEVTOOLSET_URL_ROOT/devtoolset-4-runtime-4.1-3.sc1.el6.x86_64.rpm \
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$DEVTOOLSET_URL_ROOT/devtoolset-4-libstdc++-devel-5.3.1-6.1.el6.x86_64.rpm && \
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# Python
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wget -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
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bash Miniconda3.sh -b -p /opt/python && \
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apt-get update && \
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apt-get install -y tar unzip wget bzip2 libgomp1 git build-essential doxygen graphviz llvm libasan2 libidn11 liblz4-dev ninja-build && \
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# CMake
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wget -nv -nc https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.sh --no-check-certificate && \
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bash cmake-3.13.0-Linux-x86_64.sh --skip-license --prefix=/usr && \
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# Ninja
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mkdir -p /usr/local && \
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cd /usr/local/ && \
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wget -nv -nc https://github.com/ninja-build/ninja/archive/v1.10.0.tar.gz --no-check-certificate && \
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tar xf v1.10.0.tar.gz && mv ninja-1.10.0 ninja && rm -v v1.10.0.tar.gz && \
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cd ninja && \
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python ./configure.py --bootstrap
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# Python
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wget -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
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bash Miniconda3.sh -b -p /opt/python
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# NCCL2 (License: https://docs.nvidia.com/deeplearning/sdk/nccl-sla/index.html)
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RUN \
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export CUDA_SHORT=`echo $CUDA_VERSION | egrep -o '[0-9]+\.[0-9]'` && \
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export NCCL_VERSION=2.4.8-1 && \
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wget https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
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rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
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yum -y update && \
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yum install -y libnccl-${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-devel-${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-static-${NCCL_VERSION}+cuda${CUDA_SHORT} && \
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rm -f nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm;
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export NCCL_VERSION=2.7.5-1 && \
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apt-get update && \
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apt-get install -y --allow-downgrades --allow-change-held-packages libnccl2=${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-dev=${NCCL_VERSION}+cuda${CUDA_SHORT}
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ENV PATH=/opt/python/bin:/usr/local/ninja:$PATH
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ENV CC=/opt/rh/devtoolset-4/root/usr/bin/gcc
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ENV CXX=/opt/rh/devtoolset-4/root/usr/bin/c++
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ENV CPP=/opt/rh/devtoolset-4/root/usr/bin/cpp
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# Install Python packages
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RUN \
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pip install numpy pytest scipy scikit-learn wheel kubernetes urllib3==1.22
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ENV PATH=/opt/python/bin:$PATH
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ENV GOSU_VERSION 1.10
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62
tests/ci_build/Dockerfile.gpu_build_centos6
Normal file
62
tests/ci_build/Dockerfile.gpu_build_centos6
Normal file
@ -0,0 +1,62 @@
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ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-devel-centos6
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ARG CUDA_VERSION
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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ENV DEVTOOLSET_URL_ROOT http://vault.centos.org/6.9/sclo/x86_64/rh/devtoolset-4/
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# Install all basic requirements
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RUN \
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yum -y update && \
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yum install -y tar unzip wget xz git centos-release-scl yum-utils && \
|
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yum-config-manager --enable centos-sclo-rh-testing && \
|
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yum -y update && \
|
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yum install -y $DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-5.3.1-6.1.el6.x86_64.rpm \
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$DEVTOOLSET_URL_ROOT/devtoolset-4-gcc-c++-5.3.1-6.1.el6.x86_64.rpm \
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$DEVTOOLSET_URL_ROOT/devtoolset-4-binutils-2.25.1-8.el6.x86_64.rpm \
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$DEVTOOLSET_URL_ROOT/devtoolset-4-runtime-4.1-3.sc1.el6.x86_64.rpm \
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$DEVTOOLSET_URL_ROOT/devtoolset-4-libstdc++-devel-5.3.1-6.1.el6.x86_64.rpm && \
|
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# Python
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wget -O Miniconda3.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
|
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bash Miniconda3.sh -b -p /opt/python && \
|
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# CMake
|
||||
wget -nv -nc https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.sh --no-check-certificate && \
|
||||
bash cmake-3.13.0-Linux-x86_64.sh --skip-license --prefix=/usr && \
|
||||
# Ninja
|
||||
mkdir -p /usr/local && \
|
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cd /usr/local/ && \
|
||||
wget -nv -nc https://github.com/ninja-build/ninja/archive/v1.10.0.tar.gz --no-check-certificate && \
|
||||
tar xf v1.10.0.tar.gz && mv ninja-1.10.0 ninja && rm -v v1.10.0.tar.gz && \
|
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cd ninja && \
|
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python ./configure.py --bootstrap
|
||||
|
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# NCCL2 (License: https://docs.nvidia.com/deeplearning/sdk/nccl-sla/index.html)
|
||||
RUN \
|
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export CUDA_SHORT=`echo $CUDA_VERSION | egrep -o '[0-9]+\.[0-9]'` && \
|
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export NCCL_VERSION=2.4.8-1 && \
|
||||
wget https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
|
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rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
|
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yum -y update && \
|
||||
yum install -y libnccl-${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-devel-${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-static-${NCCL_VERSION}+cuda${CUDA_SHORT} && \
|
||||
rm -f nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm;
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||||
|
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ENV PATH=/opt/python/bin:/usr/local/ninja:$PATH
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||||
ENV CC=/opt/rh/devtoolset-4/root/usr/bin/gcc
|
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ENV CXX=/opt/rh/devtoolset-4/root/usr/bin/c++
|
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ENV CPP=/opt/rh/devtoolset-4/root/usr/bin/cpp
|
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|
||||
ENV GOSU_VERSION 1.10
|
||||
|
||||
# Install lightweight sudo (not bound to TTY)
|
||||
RUN set -ex; \
|
||||
wget -O /usr/local/bin/gosu "https://github.com/tianon/gosu/releases/download/$GOSU_VERSION/gosu-amd64" && \
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chmod +x /usr/local/bin/gosu && \
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||||
gosu nobody true
|
||||
|
||||
# Default entry-point to use if running locally
|
||||
# It will preserve attributes of created files
|
||||
COPY entrypoint.sh /scripts/
|
||||
|
||||
WORKDIR /workspace
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||||
ENTRYPOINT ["/scripts/entrypoint.sh"]
|
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@ -187,6 +187,10 @@ then
|
||||
# that is associated with the particular branch or pull request
|
||||
echo "docker tag ${DOCKER_IMG_NAME} ${DOCKER_CACHE_REPO}/${DOCKER_IMG_NAME}:${BRANCH_NAME}"
|
||||
docker tag "${DOCKER_IMG_NAME}" "${DOCKER_CACHE_REPO}/${DOCKER_IMG_NAME}:${BRANCH_NAME}"
|
||||
|
||||
echo "python3 -m awscli ecr create-repository --repository-name ${DOCKER_IMG_NAME} --region ${DOCKER_CACHE_ECR_REGION} || true"
|
||||
python3 -m awscli ecr create-repository --repository-name ${DOCKER_IMG_NAME} --region ${DOCKER_CACHE_ECR_REGION} || true
|
||||
|
||||
echo "docker push ${DOCKER_CACHE_REPO}/${DOCKER_IMG_NAME}:${BRANCH_NAME}"
|
||||
docker push "${DOCKER_CACHE_REPO}/${DOCKER_IMG_NAME}:${BRANCH_NAME}"
|
||||
if [[ $? != "0" ]]; then
|
||||
|
||||
@ -19,8 +19,10 @@ target_link_libraries(testxgboost PRIVATE objxgboost)
|
||||
if (USE_CUDA)
|
||||
# OpenMP is mandatory for CUDA
|
||||
find_package(OpenMP REQUIRED)
|
||||
target_include_directories(testxgboost PRIVATE
|
||||
${xgboost_SOURCE_DIR}/cub/)
|
||||
if (CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.0)
|
||||
target_include_directories(testxgboost PRIVATE
|
||||
${xgboost_SOURCE_DIR}/cub/)
|
||||
endif (CMAKE_CUDA_COMPILER_VERSION VERSION_LESS 11.0)
|
||||
target_compile_options(testxgboost PRIVATE
|
||||
$<$<COMPILE_LANGUAGE:CUDA>:--expt-extended-lambda>
|
||||
$<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
|
||||
|
||||
@ -148,8 +148,8 @@ void TestLearnerSerialization(Args args, FeatureMap const& fmap, std::shared_ptr
|
||||
// Binary is not tested, as it is NOT reproducible.
|
||||
class SerializationTest : public ::testing::Test {
|
||||
protected:
|
||||
size_t constexpr static kRows = 10;
|
||||
size_t constexpr static kCols = 10;
|
||||
size_t constexpr static kRows = 15;
|
||||
size_t constexpr static kCols = 15;
|
||||
std::shared_ptr<DMatrix> p_dmat_;
|
||||
FeatureMap fmap_;
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user