* Has ALL CUDA versions installed. The script pytorch/builder/conda/switch_cuda_version.sh sets /usr/local/cuda to a symlink to e.g. /usr/local/cuda-10.0 to enable different CUDA builds
* Also used for cpu builds
* pytorch/manylinux-cuda90
-* pytorch/manylinux-cuda92
* pytorch/manylinux-cuda100
* Also used for cpu builds
Output looks similar to:
- binary_upload:
- name: binary_linux_manywheel_3_7m_cu92_devtoolset7_nightly_upload
+ name: binary_linux_manywheel_3_7m_cu113_devtoolset7_nightly_upload
context: org-member
- requires: binary_linux_manywheel_3_7m_cu92_devtoolset7_nightly_test
+ requires: binary_linux_manywheel_3_7m_cu113_devtoolset7_nightly_test
filters:
branches:
only:
tags:
only: /v[0-9]+(\\.[0-9]+)*-rc[0-9]+/
package_type: manywheel
- upload_subfolder: cu92
+ upload_subfolder: cu113
"""
return {
"binary_upload": OrderedDict({
export USE_SCCACHE=1
export SCCACHE_BUCKET=ossci-compiler-cache-windows
export NIGHTLIES_PYTORCH_ROOT="$PYTORCH_ROOT"
-
-if [[ "$CUDA_VERSION" == "92" || "$CUDA_VERSION" == "100" ]]; then
- export VC_YEAR=2017
-else
- export VC_YEAR=2019
-fi
+export VC_YEAR=2019
if [[ "${DESIRED_CUDA}" == "cu111" || "${DESIRED_CUDA}" == "cu113" ]]; then
export BUILD_SPLIT_CUDA="ON"
source "/c/w/env"
export CUDA_VERSION="${DESIRED_CUDA/cu/}"
-export VC_YEAR=2017
-
-if [[ "$CUDA_VERSION" == "92" || "$CUDA_VERSION" == "100" ]]; then
- export VC_YEAR=2017
-else
- export VC_YEAR=2019
-fi
+export VC_YEAR=2019
pushd "$BUILDER_ROOT"
build_args+=("INSTALL_TEST=ON")
build_args+=("USE_ZSTD=ON")
-if [[ $BUILD_ENVIRONMENT == *py2-cuda9.0-cudnn7-ubuntu16.04* ]]; then
- # removing http:// duplicate in favor of nvidia-ml.list
- # which is https:// version of the same repo
- sudo rm -f /etc/apt/sources.list.d/nvidia-machine-learning.list
- curl --retry 3 -o ./nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda9.0_1-1_amd64.deb https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda9.0_1-1_amd64.deb
- sudo dpkg -i ./nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda9.0_1-1_amd64.deb
- sudo apt-key add /var/nvinfer-runtime-trt-repo-5.0.2-ga-cuda9.0/7fa2af80.pub
- sudo apt-get -qq update
- sudo apt-get install -y --no-install-recommends libnvinfer5=5.0.2-1+cuda9.0 libnvinfer-dev=5.0.2-1+cuda9.0
- rm ./nvinfer-runtime-trt-repo-ubuntu1604-5.0.2-ga-cuda9.0_1-1_amd64.deb
-
- build_args+=("USE_TENSORRT=ON")
-fi
-
if [[ $BUILD_ENVIRONMENT == *cuda* ]]; then
build_args+=("USE_CUDA=ON")
build_args+=("USE_NNPACK=OFF")
# Alternatively we could point cmake to the right place
# export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
if [[ "$BUILD_ENVIRONMENT" == *xla-linux-bionic* ]] || \
- [[ "$BUILD_ENVIRONMENT" == *linux-xenial-cuda9-cudnn7-py2* ]] || \
[[ "$BUILD_ENVIRONMENT" == *centos* ]] || \
[[ "$BUILD_ENVIRONMENT" == *linux-bionic* ]]; then
if ! which conda; then
# Test PyTorch
if [ -z "${IN_CI}" ]; then
- if [[ "${BUILD_ENVIRONMENT}" == *cuda9.2* ]]; then
- # Eigen gives "explicit specialization of class must precede its first use" error
- # when compiling with Xcode 9.1 toolchain, so we have to use Xcode 8.2 toolchain instead.
- export DEVELOPER_DIR=/Library/Developer/CommandLineTools
- else
- export DEVELOPER_DIR=/Applications/Xcode9.app/Contents/Developer
- fi
+ export DEVELOPER_DIR=/Applications/Xcode9.app/Contents/Developer
fi
# Download torch binaries in the test jobs
)
if x%CUDA_VERSION:.=%==x%CUDA_VERSION% (
- echo CUDA version %CUDA_VERSION% format isn't correct, which doesn't contain '.'
+ echo CUDA version %CUDA_VERSION% format isn't correct, which doesn't contain '.'
exit /b 1
)
set CUDA_SUFFIX=cuda%VERSION_SUFFIX%
if "%CUDA_SUFFIX%" == "" (
- echo unknown CUDA version, please set `CUDA_VERSION` higher than 9.2
+ echo unknown CUDA version, please set `CUDA_VERSION` higher than 10.2
exit /b 1
)