dmlc--dgl
9a7235faf2
* first commit * some thoughts * move around * more commit * more fixes * now it uses torch allocator * fix symbol export error * fix * fixes * test fix * add script * building separate library per version * fix for vs2019 * more fixes * fix on windows build * update jenkinsfile * auto copy built dlls for windows * lint and installation guide update * fix * specify conda environment * set environment for ci * fix * fix * fix * fix again * revert * fix cmake * fix * switch to using python interpreter path * remove scripts * debug * oops sorry * Update index.rst * Update index.rst * copies automatically, no need for this * do not print message if library not found * tiny fixes * debug on nightly * replace add_compile_definitions to make CMake 3.5 happy * fix linking to wrong lib for multiple pytorch envs * changed building strategy * fix nightly * fix windows * fix windows again * setup bugfix * address comments * change README
44 行
956 B
Bash
44 行
956 B
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 [ $# -ne 1 ]; then
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echo "Device argument required, can be cpu or gpu"
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exit -1
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fi
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CMAKE_VARS="-DBUILD_CPP_TEST=ON -DUSE_OPENMP=ON -DBUILD_TORCH=ON"
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# This is a semicolon-separated list of Python interpreters containing PyTorch.
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# The value here is for CI. Replace it with your own or comment this whole
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# statement for default Python interpreter.
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CMAKE_VARS="$CMAKE_VARS -DTORCH_PYTHON_INTERPS=/opt/conda/envs/pytorch-ci/bin/python"
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if [ "$1" == "gpu" ]; then
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CMAKE_VARS="-DUSE_CUDA=ON $CMAKE_VARS"
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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 $CMAKE_VARS ..
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make -j8
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popd
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pushd python
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for backend in pytorch mxnet tensorflow
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do
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conda activate "${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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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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done
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popd
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