GPU builds without Docker (#2803)

Summary: 1. GPU builds use CircleCI base image, no docker 2. Switched to CUDA 11.4 (used to be 11.3) 3. Merged all build jobs into two parameterized targets: `build_cmake` and `build_conda`. 4. Cleaned up test execution, fixed bug of Python GPU tests not running on PRs Pull Request resolved: https://github.com/facebookresearch/faiss/pull/2803 Reviewed By: mlomeli1 Differential Revision: D44541714 Pulled By: algoriddle fbshipit-source-id: aa09ae638ecb6ef556d42f27a4bfaddad7355e50
2023-03-30 10:45:54 -07:00 · 2023-03-30 10:45:54 -07:00 · 7bf645e344
parent 2686183ee5
commit 7bf645e344
6 changed files with 167 additions and 308 deletions
--- a/.circleci/Dockerfile.faiss_gpu
+++ b/.circleci/Dockerfile.faiss_gpu
@ -1,28 +0,0 @@
-FROM nvidia/cuda:10.2-devel-ubuntu18.04
-
-# Install python3, wget, and openblas.
-RUN apt-get update && \
-        apt-get install -y python3-dev python3-pip libopenblas-dev wget libpcre3-dev
-
-# Install swig 4.0.2.
-RUN wget -nv -O - https://sourceforge.net/projects/swig/files/swig/swig-4.0.2/swig-4.0.2.tar.gz/download | tar zxf - && cd swig-4.0.2 && ./configure && make -j && make install
-
-# Install recent CMake.
-RUN wget -nv -O - https://github.com/Kitware/CMake/releases/download/v3.17.1/cmake-3.17.1-Linux-x86_64.tar.gz | tar xzf - --strip-components=1 -C /usr
-
-# Install numpy/scipy/pytorch for python tests.
-RUN pip3 install numpy scipy torch
-
-COPY . /faiss
-
-WORKDIR /faiss
-
-RUN cmake -B build \
-        -DFAISS_ENABLE_GPU=ON \
-        -DFAISS_ENABLE_C_API=ON \
-        -DFAISS_ENABLE_PYTHON=ON \
-        -DBUILD_TESTING=ON \
-        -DCMAKE_CUDA_FLAGS="-gencode arch=compute_75,code=sm_75" \
-        .
-
-RUN make -C build -j8
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@ -5,6 +5,12 @@ executors:
    docker:
      - image: continuumio/miniconda3
    resource_class: medium+
+  linux-x86_64-gpu:
+    environment:
+      CONDA_ARCH: Linux-x86_64
+    machine:
+      image: linux-cuda-11:2023.02.1
+    resource_class: gpu.nvidia.medium
  linux-arm64-cpu:
    environment:
      CONDA_ARCH: Linux-aarch64
@ -25,8 +31,8 @@ executors:
  windows-x86_64-cpu:
    machine:
      image: windows-server-2019-vs2019:stable
-      resource_class: windows.medium
      shell: bash.exe
+    resource_class: windows.medium

 jobs:
  format:
@ -54,11 +60,25 @@ jobs:

  build_conda:
    parameters:
+      label:
+        type: string
+        default: ""
+      cuda:
+        type: string
+        default: ""
+      cuda_archs:
+        type: string
+        default: ""
+      compiler_version:
+        type: string
+        default: ""
      exec:
        type: executor
    executor: << parameters.exec >>
    environment:
      OMP_NUM_THREADS: 10
+      PACKAGE_TYPE: <<parameters.label>>
+      CUDA_ARCHS: <<parameters.cuda_archs>>
    steps:
      - checkout
      - run:
@ -73,118 +93,52 @@ jobs:
      - run:
          name: Install conda build tools
          command: |
-            conda update -y conda
+            conda update -y -q conda
            conda install -y -q conda-build
-      - run:
-          name: Build/test
-          no_output_timeout: 30m
-          command: |
-            cd conda
-            conda build faiss --python 3.10 -c pytorch
-
-  deploy_conda:
-    parameters:
-      label:
-        type: string
-        default: main
-      exec:
-        type: executor
-    executor: << parameters.exec >>
-    steps:
-      - checkout
-      - run:
-          name: Install conda
-          command: |
-            if [ -n "${CONDA_ARCH}" ]
-            then
-              curl https://repo.anaconda.com/miniconda/Miniconda3-latest-${CONDA_ARCH}.sh --output miniconda.sh
-              bash miniconda.sh -b -p $HOME/miniconda
-              ~/miniconda/bin/conda init
-            fi
-      - run:
-          name: Install conda build tools
-          command: |
-            conda update -y conda
-            conda install -y -q conda-build anaconda-client
-            conda config --set anaconda_upload yes
-      - run:
-          name: Build/test/upload
-          no_output_timeout: 30m
-          environment:
-            PACKAGE_TYPE: <<parameters.label>>
-          command: |
-            cd conda
-            conda build faiss --user pytorch --label <<parameters.label>> -c pytorch
-
-  deploy_linux_gpu:
-    parameters:
-      label:
-        type: string
-        default: main
-      cuda:
-        type: string
-      cuda_archs:
-        type: string
-      compiler_version:
-        type: string
-    machine:
-      resource_class: gpu.nvidia.medium
-      image: ubuntu-2004-cuda-11.4:202110-01
-      docker_layer_caching: true
-    steps:
-      - checkout
-      - run:
-          name: Build packages
-          command: |
-            docker build -t faiss -f conda/Dockerfile.cuda<<parameters.cuda>> .
-            docker run --gpus all \
-              -e PACKAGE_TYPE="<<parameters.label>>" \
-              -e CUDA_ARCHS="<<parameters.cuda_archs>>" \
-              -e ANACONDA_API_TOKEN=$ANACONDA_API_TOKEN \
-              faiss \
-              conda build faiss-gpu --variants '{ "cudatoolkit": "<<parameters.cuda>>", "c_compiler_version": "<<parameters.compiler_version>>", "cxx_compiler_version": "<<parameters.compiler_version>>" }' \
-                --user pytorch --label <<parameters.label>> -c pytorch
-          no_output_timeout: 60m
-
-  deploy_linux_gpu_v2:
-    parameters:
-      label:
-        type: string
-        default: main
-      cuda:
-        type: string
-      cuda_archs:
-        type: string
-      compiler_version:
-        type: string
-    machine:
-      resource_class: gpu.nvidia.medium
-      image: linux-cuda-11:2023.02.1
-    steps:
-      - checkout
-      - run:
-          name: Install conda
-          command: |
-            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh --output miniconda.sh
-            bash miniconda.sh -b -p $HOME/miniconda
-            ~/miniconda/bin/conda init
-      - run:
-          name: Install conda build tools
-          command: |
-            conda update -y conda
-            conda install -y -q conda-build 
-            # anaconda-client
-            # conda config --set anaconda_upload yes
-      - run:
-          name: Build/test/upload
-          no_output_timeout: 60m
-          environment:
-            PACKAGE_TYPE: <<parameters.label>>
-            CUDA_ARCHS: <<parameters.cuda_archs>>
-          command: |
-            cd conda
-            conda build faiss-gpu --variants '{ "cudatoolkit": "<<parameters.cuda>>", "c_compiler_version": "<<parameters.compiler_version>>", "cxx_compiler_version": "<<parameters.compiler_version>>" }' \
-                --user pytorch --label <<parameters.label>> -c pytorch -c nvidia
+      - when:
+          condition: << parameters.label >>
+          steps:
+            - run:
+                name: Enable anaconda uploads
+                command: |
+                  conda install -y -q anaconda-client
+                  conda config --set anaconda_upload yes
+      - when:
+          condition:
+              not: << parameters.label >>
+          steps:
+            - run:
+                name: Conda build (CPU)
+                no_output_timeout: 30m
+                command: |
+                  cd conda
+                  conda build faiss --python 3.10 -c pytorch
+      - when:
+          condition:
+            and:
+              - << parameters.label >>
+              - not: << parameters.cuda >>
+          steps:
+            - run:
+                name: Conda build (CPU) w/ anaconda upload
+                no_output_timeout: 30m
+                command: |
+                  cd conda
+                  conda build faiss --user pytorch --label <<parameters.label>> -c pytorch
+      - when:
+          condition:
+            and:
+              - << parameters.label >>
+              - << parameters.cuda >>
+          steps:
+            - run:
+                name: Conda build (GPU) w/ anaconda upload
+                no_output_timeout: 60m
+                command: |
+                  sudo update-alternatives --set cuda /usr/local/cuda-<<parameters.cuda>>
+                  cd conda
+                  conda build faiss-gpu --variants '{ "cudatoolkit": "<<parameters.cuda>>", "c_compiler_version": "<<parameters.compiler_version>>", "cxx_compiler_version": "<<parameters.compiler_version>>" }' \
+                      --user pytorch --label <<parameters.label>> -c pytorch -c nvidia

  build_cmake:
    parameters:
@ -193,13 +147,16 @@ jobs:
      opt_level:
        type: string
        default: generic
+      gpu:
+        type: string
+        default: "OFF"
    executor: << parameters.exec >>
    environment:
      OMP_NUM_THREADS: 10
      MKL_THREADING_LAYER: GNU
    steps:
      - checkout
-      - run: 
+      - run:
          name: Install conda
          command: |
            if [ -n "${CONDA_ARCH}" ]
@ -208,109 +165,80 @@ jobs:
              bash miniconda.sh -b -p $HOME/miniconda
              ~/miniconda/bin/conda init
            fi
-      - run: 
+      - when:
+          condition:
+            equal: [ "ON", << parameters.gpu >> ]
+          steps:
+            - run:
+                name: Configure CUDA
+                command: sudo update-alternatives --set cuda /usr/local/cuda-11.4
+      - run:
          name: Set up environment
          command: |
            conda update -y -q conda
-            conda install -y -q cmake make swig mkl numpy scipy pytest gxx_linux-64
-            conda install -y -q pytorch -c pytorch
+            conda install -y -q cmake make swig mkl=2021 mkl-devel=2021 numpy scipy pytest gxx_linux-64
      - run:
-          name: Build faiss library
+          name: Build all targets
          no_output_timeout: 30m
          command: |
-            source ~/.bashrc
-            cmake -B build -DBUILD_TESTING=ON -DFAISS_ENABLE_GPU=OFF \
+            eval "$(conda shell.bash hook)"
+            conda activate
+            cmake -B build \
+                  -DBUILD_TESTING=ON \
+                  -DBUILD_SHARED_LIBS=OFF \
+                  -DFAISS_ENABLE_GPU=<< parameters.gpu >> \
                  -DFAISS_OPT_LEVEL=<< parameters.opt_level >> \
-                  -DFAISS_ENABLE_C_API=ON -DPYTHON_EXECUTABLE=$(which python3)\
-                  -DCMAKE_BUILD_TYPE=Release -DBLA_VENDOR=Intel10_64_dyn .
-            make -k -C build -j$(nproc) faiss
+                  -DFAISS_ENABLE_C_API=ON \
+                  -DPYTHON_EXECUTABLE=$(which python) \
+                  -DCMAKE_BUILD_TYPE=Release \
+                  -DBLA_VENDOR=Intel10_64_dyn \
+                  -DCMAKE_CUDA_FLAGS="-gencode arch=compute_75,code=sm_75" \
+                  .
+            make -k -C build -j$(nproc)
+      - run:
+          name: C++ tests
+          command: |
+            export GTEST_OUTPUT="xml:$(realpath .)/test-results/googletest/"
+            make -C build test
+      - run:
+          name: Install Python extension
+          command: |
+            cd build/faiss/python
+            python setup.py install
+      - when:
+          condition:
+            equal: [ "OFF", << parameters.gpu >> ]
+          steps:
+            - run:
+                name: Python tests (CPU only)
+                command: |
+                  conda install -y -q pytorch -c pytorch
+                  pytest --junitxml=test-results/pytest/results.xml tests/test_*.py
+                  pytest --junitxml=test-results/pytest/results-torch.xml tests/torch_*.py
+      - when:
+          condition:
+            equal: [ "ON", << parameters.gpu >> ]
+          steps:
+            - run:
+                name: Python tests (CPU + GPU)
+                command: |
+                  conda install -y -q pytorch pytorch-cuda -c pytorch -c nvidia
+                  pytest --junitxml=test-results/pytest/results.xml tests/test_*.py
+                  pytest --junitxml=test-results/pytest/results-torch.xml tests/torch_*.py
+                  cp tests/common_faiss_tests.py faiss/gpu/test
+                  pytest --junitxml=test-results/pytest/results-gpu.xml faiss/gpu/test/test_*.py
+                  pytest --junitxml=test-results/pytest/results-gpu-torch.xml faiss/gpu/test/torch_*.py
      - when:
          condition:
            equal: [ "avx2", << parameters.opt_level >> ]
          steps:
            - run:
-                name: Build faiss_avx2 library
-                no_output_timeout: 30m
-                command: make -k -C build -j$(nproc) faiss_avx2 swigfaiss_avx2
-      - run:
-          name: Test faiss library
-          command: |
-            make -C build -j$(nproc) faiss_test
-            export GTEST_OUTPUT="xml:$(realpath .)/test-results/googletest/"
-            make -C build test
-      - run:
-          name: Build python extension
-          command: |
-            make -C build -j$(nproc) swigfaiss
-            cd build/faiss/python
-            python3 setup.py build
-      - run:
-          name: Test python extension
-          command: |
-            export PYTHONPATH="$(ls -d ./build/faiss/python/build/lib*/)"
-            pytest --junitxml=test-results/pytest/results.xml tests/test_*.py
-            pytest --junitxml=test-results/pytest/results-torch.xml tests/torch_*.py
+                name: Test avx2 loading
+                command: |
+                  FAISS_DISABLE_CPU_FEATURES=AVX2 LD_DEBUG=libs python -c "import faiss" 2>&1 | grep faiss.so
+                  LD_DEBUG=libs python -c "import faiss" 2>&1 | grep faiss_avx2.so
      - store_test_results:
          path: test-results
-      - run:
-          name: Build C API
-          command: |
-            make -k -C build -j faiss_c
-
-  build_linux_gpu:
-    machine:
-      resource_class: gpu.nvidia.medium
-      image: ubuntu-2004-cuda-11.4:202110-01
-      docker_layer_caching: true
-    steps:
-      - checkout
-      - run:
-          name: Build/test
-          command: |
-            docker build -t faiss -f .circleci/Dockerfile.faiss_gpu .
-            docker run --gpus all faiss make -C build test
-            docker run --gpus all faiss sh -c '(pwd; find)'
-            docker run --gpus all faiss sh -c '(cd build/faiss/python; python3 setup.py install) && cp tests/common_faiss_tests.py faiss/gpu/test && python3 -m unittest discover -s faiss/gpu/test -p "test_*"'
-            docker run --gpus all faiss sh -c '(cd build/faiss/python; python3 setup.py install) && cp tests/common_faiss_tests.py faiss/gpu/test && python3 -m unittest discover -s faiss/gpu/test -p "torch_*.py"'
-          no_output_timeout: 60m
-
-  build_linux_gpu_v2:
-    machine:
-      resource_class: gpu.nvidia.medium
-      image: linux-cuda-11:2023.02.1
-    steps:
-      - checkout
-      - run:
-          name: Install conda
-          command: |
-            curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh --output miniconda.sh
-            bash miniconda.sh -b -p $HOME/miniconda
-            ~/miniconda/bin/conda init
-      - run: 
-          name: Set up environment
-          command: |
-            conda update -y -q conda
-            conda install -y -q cmake make swig mkl numpy scipy pytest gxx_linux-64
-            conda install -y -q pytorch -c pytorch
-      - run:
-          name: Build
-          no_output_timeout: 60m
-          command: |
-            source ~/.bashrc
-            cmake -B build \
-                    -DFAISS_ENABLE_GPU=ON \
-                    -DFAISS_ENABLE_C_API=ON \
-                    -DFAISS_ENABLE_PYTHON=ON \
-                    -DBUILD_TESTING=ON \
-                    -DCMAKE_CUDA_FLAGS="-gencode arch=compute_75,code=sm_75" \
-                    .
-            make -C build -j8
-      - run:
-          name: Test
-          command: |
-            make -C build test
-            (cd build/faiss/python; python3 setup.py install) && cp tests/common_faiss_tests.py faiss/gpu/test && python3 -m unittest discover -s faiss/gpu/test -p "test_*"'
-            (cd build/faiss/python; python3 setup.py install) && cp tests/common_faiss_tests.py faiss/gpu/test && python3 -m unittest discover -s faiss/gpu/test -p "torch_*.py"'

 workflows:
  version: 2
@ -322,11 +250,13 @@ workflows:
          name: Linux x86_64 (cmake)
          exec: linux-x86_64-cpu
      - build_cmake:
-          name: Linux x86_64 w/ AVX2 (cmake)
+          name: Linux x86_64 AVX2 (cmake)
          exec: linux-x86_64-cpu
          opt_level: "avx2"
-      - build_linux_gpu:
+      - build_cmake:
          name: Linux x86_64 GPU (cmake)
+          exec: linux-x86_64-gpu
+          gpu: "ON"
          requires:
            - Linux x86_64 (cmake)
      - build_conda:
@ -336,7 +266,7 @@ workflows:
          name: OSX x86_64 (conda)
          exec: macosx-x86_64-cpu
      - build_conda:
-          name: Windows (conda)
+          name: Windows x86_64 (conda)
          exec: windows-x86_64-cpu
      - build_conda:
          name: OSX arm64 (conda)
@ -344,51 +274,58 @@ workflows:
      - build_conda:
          name: Linux arm64 (conda)
          exec: linux-arm64-cpu
-      - deploy_conda:
+      - build_conda:
          name: Linux x86_64 packages
          exec: linux-x86_64-cpu
+          label: main
          filters:
            tags:
              only: /^v.*/
            branches:
              ignore: /.*/
-      - deploy_linux_gpu:
-          name: Linux GPU packages (CUDA 11.3)
-          cuda: "11.3"
+      - build_conda:
+          name: Linux x86_64 GPU packages (CUDA 11.4)
+          exec: linux-x86_64-gpu
+          label: main
+          cuda: "11.4"
          cuda_archs: "60;61;70;72;75;80;86"
-          compiler_version: "9.3"
+          compiler_version: "11.2"
          filters:
            tags:
              only: /^v.*/
            branches:
              ignore: /.*/
-      - deploy_conda:
+      - build_conda:
          name: Windows x86_64 packages
          exec: windows-x86_64-cpu
+          label: main
          filters:
            tags:
              only: /^v.*/
            branches:
              ignore: /.*/
-      - deploy_conda:
+      - build_conda:
          name: OSX x86_64 packages
          exec: macosx-x86_64-cpu
+          label: main
          filters:
            tags:
              only: /^v.*/
            branches:
              ignore: /.*/
-      - deploy_conda:
+      - build_conda:
          name: OSX arm64 packages
          exec: macosx-arm64-cpu
+          label: main
          filters:
            tags:
              only: /^v.*/
            branches:
              ignore: /.*/
-      - deploy_conda:
+      - build_conda:
          name: Linux arm64 packages
          exec: linux-arm64-cpu
+          label: main
          filters:
            tags:
              only: /^v.*/
@ -404,29 +341,30 @@ workflows:
              only:
                - main
    jobs:
-      - deploy_conda:
+      - build_conda:
          name: Linux x86_64 nightlies
          exec: linux-x86_64-cpu
          label: nightly
-      - deploy_linux_gpu:
-          name: Linux x86_64 GPU nightlies (CUDA 11.3)
-          cuda: "11.3"
+      - build_conda:
+          name: Linux x86_64 GPU nightlies (CUDA 11.4)
+          exec: linux-x86_64-gpu
+          cuda: "11.4"
          cuda_archs: "60;61;70;72;75;80;86"
-          compiler_version: "9.3"
+          compiler_version: "11.2"
          label: nightly
-      - deploy_conda:
+      - build_conda:
          name: Windows x86_64 nightlies
          exec: windows-x86_64-cpu
          label: nightly
-      - deploy_conda:
+      - build_conda:
          name: OSX x86_64 nightlies
          exec: macosx-x86_64-cpu
          label: nightly
-      - deploy_conda:
+      - build_conda:
          name: OSX arm64 nightlies
          exec: macosx-arm64-cpu
          label: nightly
-      - deploy_conda:
+      - build_conda:
          name: Linux arm64 nightlies
          exec: linux-arm64-cpu
          label: nightly
--- a/29
+++ b/29
@ -1,29 +0,0 @@
-FROM nvidia/cuda:8.0-devel-centos7
-
-# Install MKL
-RUN yum-config-manager --add-repo https://yum.repos.intel.com/mkl/setup/intel-mkl.repo
-RUN rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
-RUN yum install -y intel-mkl-2019.3-062
-ENV LD_LIBRARY_PATH /opt/intel/mkl/lib/intel64:$LD_LIBRARY_PATH
-ENV LIBRARY_PATH /opt/intel/mkl/lib/intel64:$LIBRARY_PATH
-ENV LD_PRELOAD /usr/lib64/libgomp.so.1:/opt/intel/mkl/lib/intel64/libmkl_def.so:\
-/opt/intel/mkl/lib/intel64/libmkl_avx2.so:/opt/intel/mkl/lib/intel64/libmkl_core.so:\
-/opt/intel/mkl/lib/intel64/libmkl_intel_lp64.so:/opt/intel/mkl/lib/intel64/libmkl_gnu_thread.so
-
-# Install necessary build tools
-RUN yum install -y gcc-c++ make swig3
-
-# Install necessary headers/libs
-RUN yum install -y python-devel numpy
-
-COPY . /opt/faiss
-
-WORKDIR /opt/faiss
-
-# --with-cuda=/usr/local/cuda-8.0 
-RUN ./configure --prefix=/usr --libdir=/usr/lib64 --without-cuda
-RUN make -j $(nproc)
-RUN make -C python
-RUN make test
-RUN make install
-RUN make -C demos demo_ivfpq_indexing && ./demos/demo_ivfpq_indexing
--- a/conda/Dockerfile.cuda11.3
+++ b/conda/Dockerfile.cuda11.3
@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-FROM nvidia/cuda:11.3.1-devel-centos8
-
-RUN sed -i 's/mirrorlist/#mirrorlist/g' /etc/yum.repos.d/CentOS-*
-
-RUN sed -i 's|#baseurl=http://mirror.centos.org|baseurl=http://vault.centos.org|g' /etc/yum.repos.d/CentOS-*
-
-RUN yum update -y --nogpgcheck
-
-RUN yum install -y --nogpgcheck wget git libcublas-devel-11-3
-
-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
-        bash Miniconda3-latest-Linux-x86_64.sh -b -p ~/miniconda3
-ENV PATH="/root/miniconda3/condabin:${PATH}"
-
-RUN conda install -y -q conda-build anaconda-client
-RUN conda config --set anaconda_upload yes
-
-COPY ./ faiss
-WORKDIR /faiss/conda
--- a/conda/faiss-gpu/meta.yaml
+++ b/conda/faiss-gpu/meta.yaml
@ -79,9 +79,11 @@ outputs:
        - scipy
        - pytorch
      commands:
-        - python -m unittest discover tests/
+        - python -X faulthandler -m unittest discover -v -s tests/ -p "test_*"
+        - python -X faulthandler -m unittest discover -v -s tests/ -p "torch_*"
        - cp tests/common_faiss_tests.py faiss/gpu/test
-        - python -m unittest discover faiss/gpu/test/
+        - python -X faulthandler -m unittest discover -v -s faiss/gpu/test/ -p "test_*"
+        - python -X faulthandler -m unittest discover -v -s faiss/gpu/test/ -p "torch_*"
        - sh test_cpu_dispatch.sh  # [linux]
      files:
        - test_cpu_dispatch.sh  # [linux]
--- a/conda/faiss/meta.yaml
+++ b/conda/faiss/meta.yaml
@ -80,8 +80,8 @@ outputs:
        - scipy
        - pytorch
      commands:
-        - python -X faulthandler -m unittest discover -v -s tests -p "test_*"
-        - python -X faulthandler -m unittest discover -v -s tests -p "torch_*"
+        - python -X faulthandler -m unittest discover -v -s tests/ -p "test_*"
+        - python -X faulthandler -m unittest discover -v -s tests/ -p "torch_*"
        - sh test_cpu_dispatch.sh  # [linux64]
      files:
        - test_cpu_dispatch.sh  # [linux64]