@@ -61,7 +61,7 @@ setup_linux_system_environment: &setup_linux_system_environment
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pytorch_tutorial_build_defaults : &pytorch_tutorial_build_defaults
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machine :
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- image : ubuntu-1604:201903 -01
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+ image : ubuntu-1604-cuda-10.2:202012 -01
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steps :
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- checkout
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- run :
@@ -72,45 +72,14 @@ pytorch_tutorial_build_defaults: &pytorch_tutorial_build_defaults
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command : |
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set -e
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- # Set up NVIDIA docker repo
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- curl -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
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- echo "deb https://nvidia.github.io/libnvidia-container/ubuntu16.04/amd64 /" | sudo tee -a /etc/apt/sources.list.d/nvidia-docker.list
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- echo "deb https://nvidia.github.io/nvidia-container-runtime/ubuntu16.04/amd64 /" | sudo tee -a /etc/apt/sources.list.d/nvidia-docker.list
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- echo "deb https://nvidia.github.io/nvidia-docker/ubuntu16.04/amd64 /" | sudo tee -a /etc/apt/sources.list.d/nvidia-docker.list
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-
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sudo apt-get -y update
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- sudo apt-get -y remove linux-image-generic linux-headers-generic linux-generic docker-ce
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- # WARNING: Docker version is hardcoded here; you must update the
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- # version number below for docker-ce and nvidia-docker2 to get newer
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- # versions of Docker. We hardcode these numbers because we kept
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- # getting broken CI when Docker would update their docker version,
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- # and nvidia-docker2 would be out of date for a day until they
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- # released a newer version of their package.
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- #
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- # How to figure out what the correct versions of these packages are?
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- # My preferred method is to start a Docker instance of the correct
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- # Ubuntu version (e.g., docker run -it ubuntu:16.04) and then ask
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- # apt what the packages you need are. Note that the CircleCI image
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- # comes with Docker.
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- sudo apt-get -y install \
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- linux-headers-$(uname -r) \
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- linux-image-generic \
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- moreutils \
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- docker-ce=5:18.09.4~3-0~ubuntu-xenial \
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- nvidia-container-runtime=2.0.0+docker18.09.4-1 \
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- nvidia-docker2=2.0.3+docker18.09.4-1 \
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- expect-dev
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-
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- sudo pkill -SIGHUP dockerd
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+ sudo apt-get -y install expect-dev moreutils
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sudo pip -q install awscli==1.16.35
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- if [ -n "${CUDA_VERSION}" ]; then
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- DRIVER_FN="NVIDIA-Linux-x86_64-460.39.run"
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- wget "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN"
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- sudo /bin/bash "$DRIVER_FN" -s --no-drm || (sudo cat /var/log/nvidia-installer.log && false)
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- nvidia-smi
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- fi
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+ if [ -n "${CUDA_VERSION}" ]; then
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+ nvidia-smi
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+ fi
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# This IAM user only allows read-write access to ECR
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export AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_ECR_READ_ONLY}
@@ -138,7 +107,7 @@ pytorch_tutorial_build_defaults: &pytorch_tutorial_build_defaults
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echo "DOCKER_IMAGE: "${DOCKER_IMAGE}
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docker pull ${DOCKER_IMAGE} >/dev/null
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if [ -n "${CUDA_VERSION}" ]; then
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- export id=$(docker run --runtime=nvidia -t -d -w /var/lib/jenkins ${DOCKER_IMAGE})
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+ export id=$(docker run --gpus all -t -d -w /var/lib/jenkins ${DOCKER_IMAGE})
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else
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export id=$(docker run -t -d -w /var/lib/jenkins ${DOCKER_IMAGE})
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fi
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