1.2.1 patch release (#6206)
* Hide C++ symbols from dmlc-core (#6188) * Up version to 1.2.1 * Fix lint * [CI] Fix Docker build for CUDA 11 (#6202) * Update Dockerfile.gpu
This commit is contained in:
Philip Hyunsu Cho
GitHub
parent
0cd0dad0b5
commit
bcb15a980f
@ -1,9 +1,10 @@
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cmake_minimum_required(VERSION 3.13)
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project(xgboost LANGUAGES CXX C VERSION 1.2.0)
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project(xgboost LANGUAGES CXX C VERSION 1.2.1)
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include(cmake/Utils.cmake)
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list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
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cmake_policy(SET CMP0022 NEW)
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cmake_policy(SET CMP0079 NEW)
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set(CMAKE_POLICY_DEFAULT_CMP0063 NEW)
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cmake_policy(SET CMP0063 NEW)
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if ((${CMAKE_VERSION} VERSION_GREATER 3.13) OR (${CMAKE_VERSION} VERSION_EQUAL 3.13))
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@ -173,9 +174,6 @@ foreach(lib rabit rabit_base rabit_empty rabit_mock rabit_mock_static)
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# from dmlc is correctly applied to rabit.
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if (TARGET ${lib})
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target_link_libraries(${lib} dmlc ${CMAKE_THREAD_LIBS_INIT})
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if (HIDE_CXX_SYMBOLS) # Hide all C++ symbols from Rabit
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set_target_properties(${lib} PROPERTIES CXX_VISIBILITY_PRESET hidden)
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endif (HIDE_CXX_SYMBOLS)
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if (ENABLE_ALL_WARNINGS)
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target_compile_options(${lib} PRIVATE -Wall -Wextra)
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endif (ENABLE_ALL_WARNINGS)
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@ -204,8 +202,9 @@ endif (USE_NVTX)
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#-- Hide all C++ symbols
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if (HIDE_CXX_SYMBOLS)
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set_target_properties(objxgboost PROPERTIES CXX_VISIBILITY_PRESET hidden)
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set_target_properties(xgboost PROPERTIES CXX_VISIBILITY_PRESET hidden)
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foreach(target objxgboost xgboost dmlc rabit rabit_mock_static)
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set_target_properties(${target} PROPERTIES CXX_VISIBILITY_PRESET hidden)
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endforeach()
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endif (HIDE_CXX_SYMBOLS)
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target_include_directories(xgboost
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14
Jenkinsfile
vendored
14
Jenkinsfile
vendored
@ -144,7 +144,7 @@ def ClangTidy() {
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echo "Running clang-tidy job..."
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def container_type = "clang_tidy"
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def docker_binary = "docker"
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def dockerArgs = "--build-arg CUDA_VERSION=10.1"
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def dockerArgs = "--build-arg CUDA_VERSION_ARG=10.1"
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sh """
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${dockerRun} ${container_type} ${docker_binary} ${dockerArgs} python3 tests/ci_build/tidy.py
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"""
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@ -261,7 +261,7 @@ def BuildCUDA(args) {
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echo "Build with CUDA ${args.cuda_version}"
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def container_type = GetCUDABuildContainerType(args.cuda_version)
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def docker_binary = "docker"
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def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
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def docker_args = "--build-arg CUDA_VERSION_ARG=${args.cuda_version}"
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def arch_flag = ""
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if (env.BRANCH_NAME != 'master' && !(env.BRANCH_NAME.startsWith('release'))) {
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arch_flag = "-DGPU_COMPUTE_VER=75"
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@ -290,7 +290,7 @@ def BuildJVMPackagesWithCUDA(args) {
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echo "Build XGBoost4J-Spark with Spark ${args.spark_version}, CUDA ${args.cuda_version}"
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def container_type = "jvm_gpu_build"
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def docker_binary = "nvidia-docker"
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def docker_args = "--build-arg CUDA_VERSION=${args.cuda_version}"
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def docker_args = "--build-arg CUDA_VERSION_ARG=${args.cuda_version}"
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def arch_flag = ""
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if (env.BRANCH_NAME != 'master' && !(env.BRANCH_NAME.startsWith('release'))) {
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arch_flag = "-DGPU_COMPUTE_VER=75"
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@ -365,7 +365,7 @@ def TestPythonGPU(args) {
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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.host_cuda_version}"
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def docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
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if (args.multi_gpu) {
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echo "Using multiple GPUs"
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// Allocate extra space in /dev/shm to enable NCCL
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@ -406,7 +406,7 @@ def TestCppGPU(args) {
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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.host_cuda_version}"
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def docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
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sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} build/testxgboost"
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deleteDir()
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}
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@ -424,7 +424,7 @@ def CrossTestJVMwithJDKGPU(args) {
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}
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def container_type = "gpu_jvm"
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def docker_binary = "nvidia-docker"
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def docker_args = "--build-arg CUDA_VERSION=${args.host_cuda_version}"
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def docker_args = "--build-arg CUDA_VERSION_ARG=${args.host_cuda_version}"
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sh "${dockerRun} ${container_type} ${docker_binary} ${docker_args} tests/ci_build/test_jvm_gpu_cross.sh"
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deleteDir()
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}
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@ -476,7 +476,7 @@ def DeployJVMPackages(args) {
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${dockerRun} jvm docker tests/ci_build/deploy_jvm_packages.sh ${args.spark_version} 0
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"""
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sh """
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${dockerRun} jvm_gpu_build docker --build-arg CUDA_VERSION=10.0 tests/ci_build/deploy_jvm_packages.sh ${args.spark_version} 1
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${dockerRun} jvm_gpu_build docker --build-arg CUDA_VERSION_ARG=10.0 tests/ci_build/deploy_jvm_packages.sh ${args.spark_version} 1
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"""
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}
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deleteDir()
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@ -1 +1 @@
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1.2.0
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1.2.1
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@ -40,7 +40,7 @@ class EarlyStopException(Exception):
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"""
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def __init__(self, best_iteration):
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super(EarlyStopException, self).__init__()
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super().__init__()
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self.best_iteration = best_iteration
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@ -1017,7 +1017,7 @@ class XGBRFClassifier(XGBClassifier):
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**kwargs)
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def get_xgb_params(self):
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params = super(XGBRFClassifier, self).get_xgb_params()
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params = super().get_xgb_params()
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params['num_parallel_tree'] = self.n_estimators
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return params
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@ -1049,7 +1049,7 @@ class XGBRFRegressor(XGBRegressor):
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reg_lambda=reg_lambda, **kwargs)
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def get_xgb_params(self):
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params = super(XGBRFRegressor, self).get_xgb_params()
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params = super().get_xgb_params()
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params['num_parallel_tree'] = self.n_estimators
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return params
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@ -1,5 +1,6 @@
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ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-devel-ubuntu18.04
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ARG CUDA_VERSION_ARG
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FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-ubuntu18.04
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ARG CUDA_VERSION_ARG
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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@ -1,5 +1,6 @@
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ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-runtime-ubuntu16.04
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ARG CUDA_VERSION_ARG
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FROM nvidia/cuda:$CUDA_VERSION_ARG-runtime-ubuntu16.04
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ARG CUDA_VERSION_ARG
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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@ -18,7 +19,7 @@ ENV PATH=/opt/python/bin:$PATH
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# Create new Conda environment with cuDF, Dask, and cuPy
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RUN \
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conda create -n gpu_test -c rapidsai-nightly -c rapidsai -c nvidia -c conda-forge -c defaults \
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python=3.7 cudf=0.15* cudatoolkit=$CUDA_VERSION dask dask-cuda dask-cudf cupy \
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python=3.7 cudf=0.15* cudatoolkit=$CUDA_VERSION_ARG dask dask-cuda dask-cudf cupy \
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numpy pytest scipy scikit-learn pandas matplotlib wheel python-kubernetes urllib3 graphviz hypothesis
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ENV GOSU_VERSION 1.10
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@ -1,6 +1,6 @@
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ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-devel-ubuntu16.04
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ARG CUDA_VERSION
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ARG CUDA_VERSION_ARG
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FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-ubuntu16.04
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ARG CUDA_VERSION_ARG
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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@ -19,7 +19,7 @@ RUN \
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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 CUDA_SHORT=`echo $CUDA_VERSION_ARG | egrep -o '[0-9]+\.[0-9]'` && \
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export NCCL_VERSION=2.7.5-1 && \
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apt-get update && \
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apt-get install -y --allow-downgrades --allow-change-held-packages libnccl2=${NCCL_VERSION}+cuda${CUDA_SHORT} libnccl-dev=${NCCL_VERSION}+cuda${CUDA_SHORT}
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@ -1,6 +1,6 @@
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ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-devel-centos6
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ARG CUDA_VERSION
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ARG CUDA_VERSION_ARG
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FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-centos6
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ARG CUDA_VERSION_ARG
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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@ -33,7 +33,7 @@ RUN \
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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 CUDA_SHORT=`echo $CUDA_VERSION_ARG | egrep -o '[0-9]+\.[0-9]'` && \
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export NCCL_VERSION=2.4.8-1 && \
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wget https://developer.download.nvidia.com/compute/machine-learning/repos/rhel7/x86_64/nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
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rpm -i nvidia-machine-learning-repo-rhel7-1.0.0-1.x86_64.rpm && \
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@ -1,5 +1,6 @@
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ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-runtime-ubuntu16.04
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ARG CUDA_VERSION_ARG
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FROM nvidia/cuda:$CUDA_VERSION_ARG-runtime-ubuntu16.04
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ARG CUDA_VERSION_ARG
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ARG JDK_VERSION=8
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ARG SPARK_VERSION=3.0.0
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@ -1,6 +1,6 @@
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ARG CUDA_VERSION
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FROM nvidia/cuda:$CUDA_VERSION-devel-centos6
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ARG CUDA_VERSION
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ARG CUDA_VERSION_ARG
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FROM nvidia/cuda:$CUDA_VERSION_ARG-devel-centos6
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ARG CUDA_VERSION_ARG
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# Environment
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ENV DEBIAN_FRONTEND noninteractive
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@ -30,7 +30,7 @@ RUN \
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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 CUDA_SHORT=`echo $CUDA_VERSION_ARG | 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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