chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,92 @@
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#!/bin/bash
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set -e
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usage() {
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cat << EOF
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usage: bash $0 OPTIONS
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examples:
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Start a CPU only build: bash $0 -c
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Start a CUDA build: bash $0 -g
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Build incrementally: bash $0
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Remove all intermediate output and restart a CPU only build: bash $0 -c -r
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Build with extra cmake arguments: bash $0 -c -e '-DBUILD_TORCH=ON'
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Build DGL. By default, build incrementally on top of the current state.
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OPTIONS:
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-h Show this message.
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-c Restart CPU only build.
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-e Extra arguments of cmake.
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-g Restart CUDA build.
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-r Remove all intermediate output.
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-t Type of the build: dev, dogfood or release (default: dev).
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EOF
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}
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# Parse flags.
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while getopts "ce:ghrt:" flag; do
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if [[ ${flag} == "c" ]]; then
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cuda="OFF"
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elif [[ ${flag} == "e" ]]; then
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extra_args=${OPTARG}
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elif [[ ${flag} == "g" ]]; then
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cuda="ON"
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elif [[ ${flag} == "r" ]]; then
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remove="YES"
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elif [[ ${flag} == "t" ]]; then
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build_type=${OPTARG}
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elif [[ ${flag} == "h" ]]; then
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usage
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exit 0
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else
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usage
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exit 1
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fi
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done
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if [[ -z ${DGL_HOME} ]]; then
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echo "ERROR: Please make sure environment variable DGL_HOME is set correctly."
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exit 1
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fi
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if [[ ! ${PWD} == ${DGL_HOME} ]]; then
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echo "ERROR: This script only works properly from DGL root directory."
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echo " Current: ${PWD}"
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echo "DGL_HOME: ${DGL_HOME}"
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exit 1
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fi
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if [[ ${remove} == "YES" ]]; then
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rm -rf build
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rm -rf graphbolt/build
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rm -rf dgl_sparse/build
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rm -rf tensoradapter/pytorch/build
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fi
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if [[ -z ${build_type} ]]; then
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build_type="dev"
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fi
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if [[ -z ${cuda} ]]; then
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if [[ -d build ]]; then
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cd build
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else
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echo "ERROR: No existing build status found, unable to build incrementally."
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usage
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exit 1
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fi
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else
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mkdir -p build
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cd build
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cmake -DBUILD_TYPE=${build_type} -DUSE_CUDA=${cuda} ${extra_args} ..
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fi
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if [[ ${PWD} == "${DGL_HOME}/build" ]]; then
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make -j
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else
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echo "ERROR: unexpected working directory."
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echo " Current: ${PWD}"
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echo "Expected: ${DGL_HOME}/build"
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fi
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exit 0
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@@ -0,0 +1,71 @@
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#!/bin/bash
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set -e
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usage() {
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cat << EOF
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usage: bash $0 OPTIONS
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examples:
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Build doc with PyTorch-backend: bash $0 -p
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Build doc with MXNet-backend: bash $0 -m
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Build doc with TensorFlow-backend: bash $0 -t
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Build incrementally with PyTorch-backend: bash $0
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Remove all outputs and restart a PyTorch build: bash $0 -p -r
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Build DGL documentation. By default, build incrementally on top of the current state.
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OPTIONS:
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-h Show this message.
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-p Build doc with PyTorch backend.
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-m Build doc with MXNet backend.
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-t Build doc with TensorFlow backend.
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-r Remove all outputs.
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EOF
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}
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backend="pytorch"
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# Parse flags.
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while getopts "hpmtr" flag; do
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if [[ ${flag} == "p" ]]; then
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backend="pytorch"
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elif [[ ${flag} == "m" ]]; then
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backend="mxnet"
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elif [[ ${flag} == "t" ]]; then
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backend="tensorflow"
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elif [[ ${flag} == "r" ]]; then
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remove="YES"
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elif [[ ${flag} == "h" ]]; then
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usage
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exit 0
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else
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usage
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exit 1
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fi
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done
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if [[ -z ${DGL_HOME} ]]; then
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echo "ERROR: Please make sure environment variable DGL_HOME is set correctly."
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exit 1
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fi
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if [[ ! ${PWD} == ${DGL_HOME} ]]; then
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echo "ERROR: This script only works properly from DGL root directory."
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echo " Current: ${PWD}"
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echo "DGL_HOME: ${DGL_HOME}"
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exit 1
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fi
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cd ${DGL_HOME}/docs
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if [[ ${remove} == "YES" ]]; then
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bash clean.sh
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fi
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export DGLBACKEND=$backend
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export DGL_LIBRARY_PATH=${DGL_HOME}/build
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export PYTHONPATH=${DGL_HOME}/python:$PYTHONPATH
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make $backend
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exit 0
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@@ -0,0 +1,184 @@
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#!/bin/bash
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readonly CUDA_VERSIONS="11.8,12.1,12.4"
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readonly TORCH_VERSION="2.1.0"
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readonly PYTHON_VERSION="3.10"
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usage() {
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cat << EOF
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usage: bash $0 OPTIONS
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examples:
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bash $0 -c
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bash $0 -g 12.1
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bash $0 -g 12.1 -p 3.10
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bash $0 -g 12.1 -p 3.10 -t 2.1.0
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bash $0 -c -n dgl-dev-cpu
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Create a developement environment for DGL developers.
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OPTIONS:
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-h Show this message.
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-c Create dev environment in CPU mode.
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-d Only display environment YAML file instead of creating it.
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-f Force creation of environment (removing a previously existing
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environment of the same name).
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-g Create dev environment in GPU mode with specified CUDA version,
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supported: ${CUDA_VERSIONS}.
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-n Specify the name of the environment.
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-o Save environment YAML file to specified path.
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-p Create dev environment based on specified python version.
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-s Run silently which indicates always 'yes' for any confirmation.
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-t Create dev environment based on specified PyTorch version such
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as '2.0.0'.
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EOF
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}
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validate() {
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values=$(echo "$1" | tr "," "\n")
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for value in ${values}
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do
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if [[ "${value}" == $2 ]]; then
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return 0
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fi
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done
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return 1
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}
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confirm() {
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echo "Continue? [yes/no]:"
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read confirm
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if [[ ! ${confirm} == "yes" ]]; then
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exit 0
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fi
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}
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# Parse flags.
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while getopts "cdfg:hn:o:p:st:" flag; do
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case "${flag}" in
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c)
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cpu=1
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;;
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d)
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dry_run=1
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;;
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f)
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force_create=1
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;;
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g)
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cuda_version=${OPTARG}
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;;
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h)
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usage
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exit 0
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;;
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n)
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name=${OPTARG}
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;;
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o)
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output_path=${OPTARG}
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;;
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p)
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python_version=${OPTARG}
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;;
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s)
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always_yes=1
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;;
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t)
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torch_version=${OPTARG}
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;;
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:)
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echo "Error: -${OPTARG} requires an argument."
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exit 1
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;;
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*)
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usage
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exit 1
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;;
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esac
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done
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if [[ -n ${cuda_version} && ${cpu} -eq 1 ]]; then
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echo "Only one mode can be specified."
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exit 1
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fi
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if [[ -z ${cuda_version} && -z ${cpu} ]]; then
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usage
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exit 1
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fi
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if [[ -z "${torch_version}" ]]; then
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torch_version=${TORCH_VERSION}
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fi
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# Set up CPU mode.
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if [[ ${cpu} -eq 1 ]]; then
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torchversion=${torch_version}"+cpu"
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if [[ -z "${name}" ]]; then
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name="dgl-dev-cpu"
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fi
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fi
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# Set up GPU mode.
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if [[ -n ${cuda_version} ]]; then
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if ! validate ${CUDA_VERSIONS} ${cuda_version}; then
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echo "Error: Invalid CUDA version."
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usage
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exit 1
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fi
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echo "Confirm the installed CUDA version matches the specified one."
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[[ -n "${always_yes}" ]] || confirm
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torchversion=${torch_version}"+cu"${cuda_version//[-._]/}
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if [[ -z "${name}" ]]; then
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name="dgl-dev-gpu-"${cuda_version//[-._]/}
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fi
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fi
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# Set python version.
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if [[ -z "${python_version}" ]]; then
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python_version=${PYTHON_VERSION}
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fi
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echo "Confirm you are excuting the script from your DGL root directory."
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echo "Current working directory: ${PWD}"
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[[ -n "${always_yes}" ]] || confirm
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# Prepare the conda environment yaml file.
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rand=$(echo "${RANDOM}" | md5sum | head -c 20)
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mkdir -p /tmp/${rand}
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yaml_path="/tmp/${rand}/dgl_dev.yml"
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cp script/dgl_dev.yml.template ${yaml_path}
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sed -i "s|__NAME__|${name}|g" ${yaml_path}
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sed -i "s|__PYTHON_VERSION__|${python_version}|g" ${yaml_path}
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sed -i "s|__TORCH_VERSION__|${torchversion}|g" ${yaml_path}
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sed -i "s|__DGL_HOME__|${PWD}|g" ${yaml_path}
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# Ask for final confirmation.
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echo "--------------------------------------------------"
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cat ${yaml_path}
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echo "--------------------------------------------------"
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echo "Create a conda enviroment with the config?"
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[[ -n "${always_yes}" ]] || confirm
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# Save YAML file to specified path
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if [[ -n "${output_path}" ]]; then
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cp ${yaml_path} ${output_path}
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echo "Environment YAML file has been saved to ${output_path}."
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fi
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# Create conda environment.
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if [[ -z "${dry_run}" ]]; then
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conda_args=""
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if [[ -n "${force_create}" ]]; then
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conda_args="${conda_args} --force "
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fi
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conda env create -f ${yaml_path} ${conda_args}
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else
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echo "Running in dry mode, so creation of conda environment is skipped."
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fi
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# Clean up created tmp conda environment yaml file.
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rm -rf /tmp/${rand}
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exit 0
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@@ -0,0 +1,57 @@
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name: __NAME__
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channels:
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- conda-forge
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- defaults
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dependencies:
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- libstdcxx-ng>=9.5.0
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- python=__PYTHON_VERSION__
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- pip
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- graphviz
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- pandoc
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- pygraphviz
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- pip:
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- --find-links https://download.pytorch.org/whl/torch/
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- cmake>=3.18
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- cython
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- filelock
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- matplotlib
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- networkx
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- nltk
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- nose
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- numpy
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- ogb
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- pandas
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- psutil
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- pyarrow
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- pydantic>=2.0
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- pytest
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- pyyaml
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- rdflib
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- requests[security]
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- scikit-learn
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- scipy
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- torch==__TORCH_VERSION__
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- torcheval
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- torchmetrics
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- torch_geometric
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- tqdm
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- boto3 # AWS SDK for python
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- sphinx
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- sphinx-gallery
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- sphinx_rtd_theme
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- sphinx_copybutton
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- sphinxemoji
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- nbsphinx
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- nbsphinx-link
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- pillow
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- seaborn
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- jupyter_http_over_ws
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- ufmt
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- clang-format
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- pylint
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- lintrunner
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- jupyterlab
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- ipywidgets
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- expecttest
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variables:
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DGL_HOME: __DGL_HOME__
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@@ -0,0 +1,69 @@
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#!/bin/bash
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set -e
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usage() {
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cat << EOF
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usage: bash $0 OPTIONS TARGETS
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examples:
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Run python tests on CPU: bash $0 -c tests/compute/test_subgraph.py
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Run python tests on GPU: bash $0 -g tests/compute/test_subgraph.py
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Run DGL python tests.
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OPTIONS:
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-h Show this message.
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-c Run python tests on CPU.
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-g Run python tests on GPU.
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EOF
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}
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# Parse flags.
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while getopts "cgh" flag; do
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if [[ ${flag} == "c" ]]; then
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device="cpu"
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elif [[ ${flag} == "g" ]]; then
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device="gpu"
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elif [[ ${flag} == "h" ]]; then
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usage
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||||
exit 0
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else
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||||
usage
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||||
exit 1
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||||
fi
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||||
done
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||||
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||||
if [[ -z ${DGL_HOME} ]]; then
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||||
echo "ERROR: Please make sure environment variable DGL_HOME is set correctly."
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||||
exit 1
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||||
fi
|
||||
|
||||
if [[ ! ${PWD} == ${DGL_HOME} ]]; then
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||||
echo "ERROR: This script only works properly from DGL root directory."
|
||||
echo " Current: ${PWD}"
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||||
echo "DGL_HOME: ${DGL_HOME}"
|
||||
exit 1
|
||||
fi
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||||
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||||
if [[ -z ${device} ]]; then
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echo "ERROR: Test device unspecified."
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||||
usage
|
||||
exit 1
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||||
fi
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||||
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||||
# Reset the index for non-option arguments.
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||||
shift $(($OPTIND-1))
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||||
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||||
export DGLBACKEND=pytorch
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export DGL_LIBRARY_PATH=${DGL_HOME}/build
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export PYTHONPATH=${DGL_HOME}/python:${DGL_HOME}/tests:${DGL_HOME}/tests/python/pytorch/graphbolt:$PYTHONPATH
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export DGLTESTDEV=${device}
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||||
export DGL_DOWNLOAD_DIR=${DGL_HOME}/_download
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||||
|
||||
if [[ -z $@ ]]; then
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||||
echo "ERROR: Missing test targets."
|
||||
usage
|
||||
exit 1
|
||||
fi
|
||||
|
||||
python3 -m pytest -v $@
|
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Reference in New Issue
Block a user