linux笔记: centos+cuda+tf镜像制作和验证

备注：

cuda的docker镜像，启动使用宿主机的驱动，所以，不用安装，只需要安装相应的cuda和cudnn就可以。在容器启动的时候，挂载一下宿主机nvidia驱动库所在目录就行。

centos7

dockerfile地址

获取rootfs的tar包：

wget https://github.com/CentOS/sig-cloud-instance-images/raw/CentOS-7/docker/centos-7-docker.tar.xz

dockerfile 片段：

FROM scratch
ADD centos-7-docker.tar.xz /
  
LABEL org.label-schema.schema-version="1.0" \
    org.label-schema.name="CentOS Base Image" \
    org.label-schema.vendor="CentOS" \
    org.label-schema.license="GPLv2" \
    org.label-schema.build-date="20181205"
  
CMD ["/bin/bash"]

cuda10.1

dockerfile git地址

cuda10.1 基础镜像dockerfile：

FROM centos:7
RUN NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \
    curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/7fa2af80.pub | sed '/^Version/d' > /etc/pki/rpm-gpg/RPM-GPG-KEY-NVIDIA && \
    echo "$NVIDIA_GPGKEY_SUM  /etc/pki/rpm-gpg/RPM-GPG-KEY-NVIDIA" | sha256sum -c --strict  -
COPY cuda.repo /etc/yum.repos.d/cuda.repo
COPY nvidia-ml.repo /etc/yum.repos.d/nvidia-ml.repo
  
ENV CUDA_VERSION 10.1.243
ENV CUDA_PKG_VERSION 10-1-$CUDA_VERSION-1
  
# For libraries in the cuda-compat-* package: https://docs.nvidia.com/cuda/eula/index.html#attachment-a
RUN yum upgrade -y && yum install -y \
    cuda-cudart-$CUDA_PKG_VERSION \
    cuda-compat-10-1 \
    && ln -s cuda-10.1 /usr/local/cuda \
    && yum clean all \
    && rm -rf /var/cache/yum/*
  
# nvidia-docker 1.0
RUN echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && \
    echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf
  
ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:${PATH}
ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
  
# nvidia-container-runtime
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
ENV NVIDIA_REQUIRE_CUDA "cuda>=10.1 brand=tesla,driver>=396,driver<397 brand=tesla,driver>=410,driver<411 brand=tesla,driver>=418,driver<419"

cuda runtime:

ENV NCCL_VERSION 2.7.8
  
# setopt flag prevents yum from auto upgrading. See https://gitlab.com/nvidia/container-images/cuda/-/issues/88
RUN yum install --setopt=obsoletes=0 -y \
    cuda-libraries-$CUDA_PKG_VERSION \
    cuda-nvtx-$CUDA_PKG_VERSION \
    cuda-npp-$CUDA_PKG_VERSION \
    libcublas10-10.2.1.243-1 \
    libnccl-2.7.8-1+cuda10.1 \
    && yum clean all \
    && rm -rf /var/cache/yum/*
  
RUN yum install -y yum-plugin-versionlock && yum versionlock libcublas10

cudnn7 library:

ENV CUDNN_VERSION 7.6.5.32
LABEL com.nvidia.cudnn.version="${CUDNN_VERSION}"
  
RUN yum install -y \
    libcudnn7-${CUDNN_VERSION}-1.cuda10.1 \
    && yum clean all \
    && rm -rf /var/cache/yum/*

使用tf进行验证

安装tf

pip install --upgrade pip
pip install --upgrade setuptools
## 最新tf2.1.0
pip install tensorflow-gpu

验证

python -c 'import tensorflow.compat.v1 as tf; import os; tf.disable_v2_behavior();os.environ["CUDA_VISIBLE_DEVICES"] ="0"; sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))'
   
python -c "import tensorflow as tf; tf.test.is_gpu_available(cuda_only=False,min_cuda_compute_capability=None)"

容器启动命令

docker run \
--cap-add=NET_ADMIN \
--cap-add=SYS_ADMIN \
-it \
-v <nvidia_driver>:/usr/local/nvidia:ro \
tf2.1 

centos7/cuda10.1/cudnn7.6.5/tf2.1镜像制作

centos7

cuda10.1

使用tf进行验证

容器启动命令

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