mirror of
https://github.com/dmlc/dgl.git
synced 2026-06-04 19:44:23 +08:00
63 lines
1.8 KiB
Bash
63 lines
1.8 KiB
Bash
#!/bin/bash
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set -e
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. /opt/conda/etc/profile.d/conda.sh
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if [ $# -ne 1 ]; then
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echo "Device argument required, can be cpu, gpu or cugraph"
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exit -1
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fi
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if [[ $1 != "cpu" ]]; then
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# CI is now running on g4dn instance. Specify target arch to avoid below
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# error: Unknown CUDA Architecture Name 9.0a in CUDA_SELECT_NVCC_ARCH_FLAGS
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export TORCH_CUDA_ARCH_LIST=7.5 # For dgl_sparse and tensoradaptor.
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CMAKE_VARS="$CMAKE_VARS -DUSE_CUDA=ON -DCUDA_ARCH_NAME=Turing" # For graphbolt.
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fi
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# This is a semicolon-separated list of Python interpreters containing PyTorch.
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# The value here is for CI. Replace it with your own or comment this whole
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# statement for default Python interpreter.
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if [ "$1" != "cugraph" ]; then
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# We do not build pytorch for cugraph because currently building
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# pytorch against all the supported cugraph versions is not supported
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# See issue: https://github.com/rapidsai/cudf/issues/8510
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CMAKE_VARS="$CMAKE_VARS -DTORCH_PYTHON_INTERPS=/opt/conda/envs/pytorch-ci/bin/python"
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else
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# Disable sparse build as cugraph docker image lacks cuDNN.
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CMAKE_VARS="$CMAKE_VARS -DBUILD_TORCH=OFF -DBUILD_SPARSE=OFF"
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fi
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if [ -d build ]; then
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rm -rf build
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fi
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mkdir build
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rm -rf _download
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pushd build
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cmake $CMAKE_VARS ..
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make -j
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popd
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pushd python
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if [[ $1 == "cugraph" ]]; then
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rm -rf build *.egg-info dist
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pip uninstall -y dgl
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# test install
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python3 setup.py install
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# test inplace build (for cython)
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python3 setup.py build_ext --inplace
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else
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for backend in pytorch mxnet tensorflow
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do
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conda activate "${backend}-ci"
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rm -rf build *.egg-info dist
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pip uninstall -y dgl
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# test install
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DGLBACKEND=${backend} python3 setup.py install
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# test inplace build (for cython)
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DGLBACKEND=${backend} python3 setup.py build_ext --inplace
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done
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fi
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popd
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