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Unpin gxx_linux-64 requirement (#3655)
Summary: The TestPartitioning.TestPartitioningBigRange test case fails on gcc version 13.2. We can avoid this by requiring gcc version 11.2 where the test case works. Pull Request resolved: #3655 Test Plan: STEP 1: Create conda env and install dependencies ``` conda create --name faiss_debug conda activate faiss_debug conda install -y -q python=3.11 cmake make swig mkl=2023 mkl-devel=2023 numpy scipy pytest gxx_linux-64 sysroot_linux-64 -c conda-forge ``` STEP 3: CMAKE ``` cmake -B build \ -DBUILD_TESTING=ON \ -DBUILD_SHARED_LIBS=ON \ -DFAISS_ENABLE_GPU=OFF \ -DFAISS_ENABLE_RAFT=OFF \ -DFAISS_OPT_LEVEL=avx2 \ -DFAISS_ENABLE_C_API=ON \ -DPYTHON_EXECUTABLE=$(which python) \ -DCMAKE_BUILD_TYPE=Release \ -DBLA_VENDOR=Intel10_64_dyn \ -DCMAKE_CUDA_FLAGS="-gencode arch=compute_75,code=sm_75" \ . ``` STEP 4: build ``` make -k -C build -j$(nproc) cd build ctest -R TestPartitioning.TestPartitioningBigRange ``` {F1769051341} Differential Revision: D59988036 Pulled By: gtwang01
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.circleci/config.yml

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@@ -37,7 +37,7 @@ jobs:
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- run:
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name: Install env using main channel
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command: |
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conda install -y -q python=3.11 cmake make swig mkl=2023 mkl-devel=2023 numpy scipy pytest gxx_linux-64=11.2 sysroot_linux-64
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conda install -y -q python=3.11 cmake make swig mkl=2023 mkl-devel=2023 numpy scipy pytest gxx_linux-64 sysroot_linux-64
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- run:
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name: Build all targets
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no_output_timeout: 30m

.github/actions/build_cmake/action.yml

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# install base packages for X86_64
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if [ "${{ runner.arch }}" = "X64" ]; then
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# TODO: unpin versions for gxx_linux-64 and sysroot_linux-64 and merge it with ARM64 below
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conda install -y -q -c conda-forge gxx_linux-64=11.2 sysroot_linux-64=2.28
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conda install -y -q -c conda-forge gxx_linux-64 sysroot_linux-64
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conda install -y -q mkl=2023 mkl-devel=2023
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fi
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# install CUDA packages
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if [ "${{ inputs.gpu }}" = "ON" ] && [ "${{ inputs.raft }}" = "OFF" ]; then
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conda install -y -q cuda-toolkit -c "nvidia/label/cuda-11.8.0"
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conda install -y -q cuda-toolkit -c "nvidia/label/cuda-12.1"
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fi
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# install RAFT packages
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if [ "${{ inputs.raft }}" = "ON" ]; then
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conda install -y -q libraft cuda-version=11.8 cuda-toolkit -c rapidsai-nightly -c "nvidia/label/cuda-11.8.0" -c conda-forge
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conda install -y -q libraft cuda-version=12.1 cuda-toolkit -c rapidsai-nightly -c "nvidia/label/cuda-12.1" -c conda-forge
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fi
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# install test packages
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conda install -y pytest
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if [ "${{ inputs.gpu }}" = "ON" ]; then
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conda install -y -q pytorch pytorch-cuda=11.8 -c pytorch -c nvidia/label/cuda-11.8.0
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conda install -y -q pytorch pytorch-cuda=12.1 -c pytorch -c nvidia/label/cuda-12.1
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else
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conda install -y -q pytorch -c pytorch
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fi

.github/workflows/build.yml

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with:
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label: main
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cuda: "11.4.4"
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compiler_version: "11.2"
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linux-x86_64-GPU-RAFT-packages-CUDA11-8-0:
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name: Linux x86_64 GPU w/ RAFT packages (CUDA 11.8.0)
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if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
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label: main
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raft: "ON"
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cuda: "11.8.0"
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compiler_version: "11.2"
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linux-x86_64-GPU-packages-CUDA-12-1-1:
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name: Linux x86_64 GPU packages (CUDA 12.1.1)
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if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
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with:
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label: main
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cuda: "12.1.1"
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compiler_version: "11.2"
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linux-x86_64-GPU-RAFT-packages-CUDA12-1-1:
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name: Linux x86_64 GPU w/ RAFT packages (CUDA 12.1.1)
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if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
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label: main
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raft: "ON"
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cuda: "12.1.1"
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compiler_version: "11.2"
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windows-x86_64-packages:
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name: Windows x86_64 packages
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if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')

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