docker.io/rayproject/ray:2.40.0.160e35-py312-cu123 linux/amd64

docker.io/rayproject/ray:2.40.0.160e35-py312-cu123 - 国内下载镜像源 浏览次数:7
_rayproject/ray_ RAY 是一个基于 Python 的高性能计算框架，可以在多种环境中运行，包括本地、云和集群。Ray 提供了高效的并行计算能力，并且可以与其他库和框架集成。 -Ray 的主要特点有： * 高性能：Ray 使用了高性能的编译器和执行引擎，可以在多种环境中运行。 * 可扩展性：Ray 可以轻松地 scales to thousands of machines and can handle large-scale computations. * 可组合性：Ray 可以与其他库和框架集成，例如 TensorFlow、PyTorch 和 scikit-learn。 总的来说，《RAY》是一个功能强大且灵活的计算框架，可以满足各种计算需求。
源镜像 docker.io/rayproject/ray:2.40.0.160e35-py312-cu123
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123
镜像ID sha256:868bbc29eb7586e0e34b206078116c5e04198c7a3f117bf8faa648f14236c4d5
镜像TAG 2.40.0.160e35-py312-cu123
大小 10.23GB
镜像源 docker.io
项目信息 Docker-Hub主页 🚀项目TAG 🚀
CMD
启动入口 /opt/nvidia/nvidia_entrypoint.sh
工作目录 /home/ray
OS/平台 linux/amd64
浏览量 7 次
贡献者
镜像创建 2024-12-27T23:53:44.070275776Z
同步时间 2025-01-18 01:27
更新时间 2025-01-18 09:50
环境变量
PATH=/home/ray/anaconda3/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin NVARCH=x86_64 NVIDIA_REQUIRE_CUDA=cuda>=12.3 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536 NV_CUDA_CUDART_VERSION=12.3.101-1 NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-3 CUDA_VERSION=12.3.2 LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 NVIDIA_VISIBLE_DEVICES=all NVIDIA_DRIVER_CAPABILITIES=compute,utility NV_CUDA_LIB_VERSION=12.3.2-1 NV_NVTX_VERSION=12.3.101-1 NV_LIBNPP_VERSION=12.2.3.2-1 NV_LIBNPP_PACKAGE=libnpp-12-3=12.2.3.2-1 NV_LIBCUSPARSE_VERSION=12.2.0.103-1 NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-3 NV_LIBCUBLAS_VERSION=12.3.4.1-1 NV_LIBCUBLAS_PACKAGE=libcublas-12-3=12.3.4.1-1 NV_LIBNCCL_PACKAGE_NAME=libnccl2 NV_LIBNCCL_PACKAGE_VERSION=2.20.3-1 NCCL_VERSION=2.20.3-1 NV_LIBNCCL_PACKAGE=libnccl2=2.20.3-1+cuda12.3 NVIDIA_PRODUCT_NAME=CUDA NV_CUDA_CUDART_DEV_VERSION=12.3.101-1 NV_NVML_DEV_VERSION=12.3.101-1 NV_LIBCUSPARSE_DEV_VERSION=12.2.0.103-1 NV_LIBNPP_DEV_VERSION=12.2.3.2-1 NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-3=12.2.3.2-1 NV_LIBCUBLAS_DEV_VERSION=12.3.4.1-1 NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-3 NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-3=12.3.4.1-1 NV_CUDA_NSIGHT_COMPUTE_VERSION=12.3.2-1 NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-3=12.3.2-1 NV_NVPROF_VERSION=12.3.101-1 NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-3=12.3.101-1 NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev NV_LIBNCCL_DEV_PACKAGE_VERSION=2.20.3-1 NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.20.3-1+cuda12.3 LIBRARY_PATH=/usr/local/cuda/lib64/stubs NV_CUDNN_VERSION=9.0.0.312 NV_CUDNN_PACKAGE_NAME=libcudnn9-cuda-12 NV_CUDNN_PACKAGE=libcudnn9-cuda-12=9.0.0.312-1 NV_CUDNN_PACKAGE_DEV=libcudnn9-dev-cuda-12=9.0.0.312-1 TZ=America/Los_Angeles LC_ALL=C.UTF-8 LANG=C.UTF-8 HOME=/home/ray
镜像标签
9.0.0.312: com.nvidia.cudnn.version NVIDIA CORPORATION <cudatools@nvidia.com>: maintainer ubuntu: org.opencontainers.image.ref.name 22.04: org.opencontainers.image.version

Docker拉取命令 无权限下载?点我修复 

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123  docker.io/rayproject/ray:2.40.0.160e35-py312-cu123

Containerd拉取命令 

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123  docker.io/rayproject/ray:2.40.0.160e35-py312-cu123

Shell快速替换命令 

sed -i 's#rayproject/ray:2.40.0.160e35-py312-cu123#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123#' deployment.yaml

Ansible快速分发-Docker 

#ansible k8s -m shell -a 'docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123 && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123  docker.io/rayproject/ray:2.40.0.160e35-py312-cu123'

Ansible快速分发-Containerd 

#ansible k8s -m shell -a 'ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123 && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray:2.40.0.160e35-py312-cu123  docker.io/rayproject/ray:2.40.0.160e35-py312-cu123'

镜像构建历史 


# 2024-12-28 07:53:44  3.75KB 执行命令并创建新的镜像层
RUN |3 WHEEL_PATH=.whl/ray-2.40.0-cp312-cp312-manylinux2014_x86_64.whl FIND_LINKS_PATH=.whl CONSTRAINTS_FILE=requirements_compiled.txt /bin/bash -c $HOME/anaconda3/bin/pip freeze > /home/ray/pip-freeze.txt # buildkit
                        
# 2024-12-28 07:53:43  909.74MB 执行命令并创建新的镜像层
RUN |3 WHEEL_PATH=.whl/ray-2.40.0-cp312-cp312-manylinux2014_x86_64.whl FIND_LINKS_PATH=.whl CONSTRAINTS_FILE=requirements_compiled.txt /bin/bash -c $HOME/anaconda3/bin/pip --no-cache-dir install -c $CONSTRAINTS_FILE     `basename $WHEEL_PATH`[all]     --find-links $FIND_LINKS_PATH && sudo rm `basename $WHEEL_PATH` # buildkit
                        
# 2024-12-28 07:52:58  95.49MB 复制新文件或目录到容器中
COPY .whl .whl # buildkit
                        
# 2024-12-28 07:52:57  67.02MB 复制新文件或目录到容器中
COPY .whl/ray-2.40.0-cp312-cp312-manylinux2014_x86_64.whl . # buildkit
                        
# 2024-12-28 07:52:57  0.00B 定义构建参数
ARG CONSTRAINTS_FILE=requirements_compiled.txt
                        
# 2024-12-28 07:52:57  0.00B 定义构建参数
ARG FIND_LINKS_PATH=.whl
                        
# 2024-12-28 07:52:57  0.00B 定义构建参数
ARG WHEEL_PATH
                        
# 2024-12-28 06:53:07  0.00B 设置工作目录为/home/ray
WORKDIR /home/ray
                        
# 2024-12-28 06:53:07  826.38MB 执行命令并创建新的镜像层
RUN |7 BASE_IMAGE=nvidia/cuda:12.3.2-cudnn9-devel-ubuntu22.04 AUTOSCALER=autoscaler DEBIAN_FRONTEND=noninteractive PYTHON_VERSION=3.12 HOSTTYPE=x86_64 RAY_UID=1000 RAY_GID=100 /bin/bash -c /dev/pipes/EOF # buildkit
                        
# 2024-12-28 06:52:09  0.00B 
SHELL [/bin/bash -c]
                        
# 2024-12-28 06:52:09  59.55KB 复制新文件或目录到容器中
COPY python/requirements_compiled.txt /home/ray/requirements_compiled.txt # buildkit
                        
# 2024-12-28 06:52:09  0.00B 设置环境变量 HOME
ENV HOME=/home/ray
                        
# 2024-12-28 06:52:09  0.00B 指定运行容器时使用的用户
USER 1000
                        
# 2024-12-28 06:52:09  234.70MB 执行命令并创建新的镜像层
RUN |7 BASE_IMAGE=nvidia/cuda:12.3.2-cudnn9-devel-ubuntu22.04 AUTOSCALER=autoscaler DEBIAN_FRONTEND=noninteractive PYTHON_VERSION=3.12 HOSTTYPE=x86_64 RAY_UID=1000 RAY_GID=100 /bin/sh -c /dev/pipes/EOF # buildkit
                        
# 2024-12-28 06:52:09  0.00B 定义构建参数
ARG RAY_GID=100
                        
# 2024-12-28 06:52:09  0.00B 定义构建参数
ARG RAY_UID=1000
                        
# 2024-12-28 06:52:09  0.00B 定义构建参数
ARG HOSTTYPE=x86_64
                        
# 2024-12-28 06:52:09  0.00B 定义构建参数
ARG PYTHON_VERSION=3.9
                        
# 2024-12-28 06:52:09  0.00B 定义构建参数
ARG DEBIAN_FRONTEND=noninteractive
                        
# 2024-12-28 06:52:09  0.00B 设置环境变量 PATH
ENV PATH=/home/ray/anaconda3/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2024-12-28 06:52:09  0.00B 设置环境变量 LANG
ENV LANG=C.UTF-8
                        
# 2024-12-28 06:52:09  0.00B 设置环境变量 LC_ALL
ENV LC_ALL=C.UTF-8
                        
# 2024-12-28 06:52:09  0.00B 设置环境变量 TZ
ENV TZ=America/Los_Angeles
                        
# 2024-12-28 06:52:09  0.00B 定义构建参数
ARG AUTOSCALER=autoscaler
                        
# 2024-12-28 06:52:09  0.00B 定义构建参数
ARG BASE_IMAGE
                        
# 2024-02-28 08:15:56  1.03GB 执行命令并创建新的镜像层
RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends     ${NV_CUDNN_PACKAGE}     ${NV_CUDNN_PACKAGE_DEV}     && apt-mark hold ${NV_CUDNN_PACKAGE_NAME}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-02-28 08:15:56  0.00B 添加元数据标签
LABEL com.nvidia.cudnn.version=9.0.0.312
                        
# 2024-02-28 08:15:56  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2024-02-28 08:15:56  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2024-02-28 08:15:56  0.00B 设置环境变量 NV_CUDNN_PACKAGE_DEV
ENV NV_CUDNN_PACKAGE_DEV=libcudnn9-dev-cuda-12=9.0.0.312-1
                        
# 2024-02-28 08:15:56  0.00B 设置环境变量 NV_CUDNN_PACKAGE
ENV NV_CUDNN_PACKAGE=libcudnn9-cuda-12=9.0.0.312-1
                        
# 2024-02-28 08:15:56  0.00B 设置环境变量 NV_CUDNN_PACKAGE_NAME
ENV NV_CUDNN_PACKAGE_NAME=libcudnn9-cuda-12
                        
# 2024-02-28 08:15:56  0.00B 设置环境变量 NV_CUDNN_VERSION
ENV NV_CUDNN_VERSION=9.0.0.312
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 LIBRARY_PATH
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
                        
# 2024-02-28 08:14:09  388.00KB 执行命令并创建新的镜像层
RUN |1 TARGETARCH=amd64 /bin/sh -c apt-mark hold ${NV_LIBCUBLAS_DEV_PACKAGE_NAME} ${NV_LIBNCCL_DEV_PACKAGE_NAME} # buildkit
                        
# 2024-02-28 08:14:09  4.85GB 执行命令并创建新的镜像层
RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends     cuda-cudart-dev-12-3=${NV_CUDA_CUDART_DEV_VERSION}     cuda-command-line-tools-12-3=${NV_CUDA_LIB_VERSION}     cuda-minimal-build-12-3=${NV_CUDA_LIB_VERSION}     cuda-libraries-dev-12-3=${NV_CUDA_LIB_VERSION}     cuda-nvml-dev-12-3=${NV_NVML_DEV_VERSION}     ${NV_NVPROF_DEV_PACKAGE}     ${NV_LIBNPP_DEV_PACKAGE}     libcusparse-dev-12-3=${NV_LIBCUSPARSE_DEV_VERSION}     ${NV_LIBCUBLAS_DEV_PACKAGE}     ${NV_LIBNCCL_DEV_PACKAGE}     ${NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-02-28 08:14:09  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2024-02-28 08:14:09  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE
ENV NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.20.3-1+cuda12.3
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.20.3-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_VERSION
ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.20.3-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_NAME
ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_NVPROF_DEV_PACKAGE
ENV NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-3=12.3.101-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_NVPROF_VERSION
ENV NV_NVPROF_VERSION=12.3.101-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE
ENV NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-3=12.3.2-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_VERSION
ENV NV_CUDA_NSIGHT_COMPUTE_VERSION=12.3.2-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE
ENV NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-3=12.3.4.1-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE_NAME
ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-3
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_VERSION
ENV NV_LIBCUBLAS_DEV_VERSION=12.3.4.1-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBNPP_DEV_PACKAGE
ENV NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-3=12.2.3.2-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBNPP_DEV_VERSION
ENV NV_LIBNPP_DEV_VERSION=12.2.3.2-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_LIBCUSPARSE_DEV_VERSION
ENV NV_LIBCUSPARSE_DEV_VERSION=12.2.0.103-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_NVML_DEV_VERSION
ENV NV_NVML_DEV_VERSION=12.3.101-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_CUDA_CUDART_DEV_VERSION
ENV NV_CUDA_CUDART_DEV_VERSION=12.3.101-1
                        
# 2024-02-28 08:14:09  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=12.3.2-1
                        
# 2024-02-28 08:07:42  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2024-02-28 08:07:42  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2024-02-28 08:07:42  3.06KB 复制新文件或目录到容器中
COPY entrypoint.d/ /opt/nvidia/entrypoint.d/ # buildkit
                        
# 2024-02-28 08:07:42  262.02KB 执行命令并创建新的镜像层
RUN |1 TARGETARCH=amd64 /bin/sh -c apt-mark hold ${NV_LIBCUBLAS_PACKAGE_NAME} ${NV_LIBNCCL_PACKAGE_NAME} # buildkit
                        
# 2024-02-28 08:07:42  1.97GB 执行命令并创建新的镜像层
RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends     cuda-libraries-12-3=${NV_CUDA_LIB_VERSION}     ${NV_LIBNPP_PACKAGE}     cuda-nvtx-12-3=${NV_NVTX_VERSION}     libcusparse-12-3=${NV_LIBCUSPARSE_VERSION}     ${NV_LIBCUBLAS_PACKAGE}     ${NV_LIBNCCL_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-02-28 08:07:42  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2024-02-28 08:07:42  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE
ENV NV_LIBNCCL_PACKAGE=libnccl2=2.20.3-1+cuda12.3
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.20.3-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.20.3-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE
ENV NV_LIBCUBLAS_PACKAGE=libcublas-12-3=12.3.4.1-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=12.3.4.1-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-3
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=12.2.0.103-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBNPP_PACKAGE
ENV NV_LIBNPP_PACKAGE=libnpp-12-3=12.2.3.2-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=12.2.3.2-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=12.3.101-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=12.3.2-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2024-02-28 08:07:42  17.29KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 PATH
ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2024-02-28 08:07:42  46.00B 执行命令并创建新的镜像层
RUN |1 TARGETARCH=amd64 /bin/sh -c echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf     && echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf # buildkit
                        
# 2024-02-28 08:07:42  154.88MB 执行命令并创建新的镜像层
RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends     cuda-cudart-12-3=${NV_CUDA_CUDART_VERSION}     ${NV_CUDA_COMPAT_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=12.3.2
                        
# 2024-02-28 08:07:42  10.56MB 执行命令并创建新的镜像层
RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends     gnupg2 curl ca-certificates &&     curl -fsSLO https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/${NVARCH}/cuda-keyring_1.0-1_all.deb &&     dpkg -i cuda-keyring_1.0-1_all.deb &&     apt-get purge --autoremove -y curl     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-02-28 08:07:42  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2024-02-28 08:07:42  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-3
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=12.3.101-1
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NVIDIA_REQUIRE_CUDA brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand brand
ENV NVIDIA_REQUIRE_CUDA=cuda>=12.3 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536
                        
# 2024-02-28 08:07:42  0.00B 设置环境变量 NVARCH
ENV NVARCH=x86_64
                        
# 2024-02-13 18:06:28  0.00B 
/bin/sh -c #(nop)  CMD ["/bin/bash"]
                        
# 2024-02-13 18:06:28  77.86MB 
/bin/sh -c #(nop) ADD file:7f9a3c5a4231ed19174c21d17ce05d84d568cff6d3a0c2a1d7c3a9be5e45c02c in / 
                        
# 2024-02-13 18:06:26  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.version=22.04
                        
# 2024-02-13 18:06:26  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.ref.name=ubuntu
                        
# 2024-02-13 18:06:26  0.00B 
/bin/sh -c #(nop)  ARG LAUNCHPAD_BUILD_ARCH
                        
# 2024-02-13 18:06:26  0.00B 
/bin/sh -c #(nop)  ARG RELEASE

镜像信息 

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    "Comment": "buildkit.dockerfile.v0",
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    "Config": {
        "Hostname": "",
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        "Env": [
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            "NVARCH=x86_64",
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            "NV_CUDA_CUDART_VERSION=12.3.101-1",
            "NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-3",
            "CUDA_VERSION=12.3.2",
            "LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64",
            "NVIDIA_VISIBLE_DEVICES=all",
            "NVIDIA_DRIVER_CAPABILITIES=compute,utility",
            "NV_CUDA_LIB_VERSION=12.3.2-1",
            "NV_NVTX_VERSION=12.3.101-1",
            "NV_LIBNPP_VERSION=12.2.3.2-1",
            "NV_LIBNPP_PACKAGE=libnpp-12-3=12.2.3.2-1",
            "NV_LIBCUSPARSE_VERSION=12.2.0.103-1",
            "NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-3",
            "NV_LIBCUBLAS_VERSION=12.3.4.1-1",
            "NV_LIBCUBLAS_PACKAGE=libcublas-12-3=12.3.4.1-1",
            "NV_LIBNCCL_PACKAGE_NAME=libnccl2",
            "NV_LIBNCCL_PACKAGE_VERSION=2.20.3-1",
            "NCCL_VERSION=2.20.3-1",
            "NV_LIBNCCL_PACKAGE=libnccl2=2.20.3-1+cuda12.3",
            "NVIDIA_PRODUCT_NAME=CUDA",
            "NV_CUDA_CUDART_DEV_VERSION=12.3.101-1",
            "NV_NVML_DEV_VERSION=12.3.101-1",
            "NV_LIBCUSPARSE_DEV_VERSION=12.2.0.103-1",
            "NV_LIBNPP_DEV_VERSION=12.2.3.2-1",
            "NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-3=12.2.3.2-1",
            "NV_LIBCUBLAS_DEV_VERSION=12.3.4.1-1",
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            "NV_NVPROF_VERSION=12.3.101-1",
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}

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