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dgl/tests/scripts/build_dgl.sh

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#!/bin/bash
set -e
. /opt/conda/etc/profile.d/conda.sh
if [ $# -ne 1 ]; then
echo "Device argument required, can be cpu, gpu or cugraph"
exit -1
fi
if [[ $1 != "cpu" ]]; then
# CI is now running on g4dn instance. Specify target arch to avoid below
# error: Unknown CUDA Architecture Name 9.0a in CUDA_SELECT_NVCC_ARCH_FLAGS
export TORCH_CUDA_ARCH_LIST=7.5 # For dgl_sparse and tensoradaptor.
CMAKE_VARS="$CMAKE_VARS -DUSE_CUDA=ON -DCUDA_ARCH_NAME=Turing" # For graphbolt.
fi
# This is a semicolon-separated list of Python interpreters containing PyTorch.
# The value here is for CI. Replace it with your own or comment this whole
# statement for default Python interpreter.
if [ "$1" != "cugraph" ]; then
# We do not build pytorch for cugraph because currently building
# pytorch against all the supported cugraph versions is not supported
# See issue: https://github.com/rapidsai/cudf/issues/8510
CMAKE_VARS="$CMAKE_VARS -DTORCH_PYTHON_INTERPS=/opt/conda/envs/pytorch-ci/bin/python"
else
# Disable sparse build as cugraph docker image lacks cuDNN.
CMAKE_VARS="$CMAKE_VARS -DBUILD_TORCH=OFF -DBUILD_SPARSE=OFF"
fi
if [ -d build ]; then
rm -rf build
fi
mkdir build
rm -rf _download
pushd build
cmake $CMAKE_VARS ..
make -j
popd
pushd python
if [[ $1 == "cugraph" ]]; then
rm -rf build *.egg-info dist
pip uninstall -y dgl
# test install
python3 setup.py install
# test inplace build (for cython)
python3 setup.py build_ext --inplace
else
for backend in pytorch mxnet tensorflow
do
conda activate "${backend}-ci"
rm -rf build *.egg-info dist
pip uninstall -y dgl
# test install
DGLBACKEND=${backend} python3 setup.py install
# test inplace build (for cython)
DGLBACKEND=${backend} python3 setup.py build_ext --inplace
done
fi
popd