docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel linux/amd64

docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel - 国内下载镜像源 浏览次数:44 安全受验证的发布者-Pytorch

PyTorch是一个深度学习框架,旨在简化机器学习算法的实现和部署。该镜像提供了一个基于Python 3.x的环境,可以用于快速启动和测试PyTorch项目。

源镜像 docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
镜像ID sha256:4a2c50554384bb3baa52c66214bc4c098552f4a15a5f2a27d034f3a44462b764
镜像TAG 2.1.2-cuda11.8-cudnn8-devel
大小 17.33GB
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 /workspace
OS/平台 linux/amd64
浏览量 44 次
贡献者
镜像创建 2023-12-19T14:49:28.767787857Z
同步时间 2024-12-10 00:33
更新时间 2024-12-25 07:42
环境变量
PATH=/opt/conda/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>=11.8 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 NV_CUDA_CUDART_VERSION=11.8.89-1 NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-8 CUDA_VERSION=11.8.0 LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 NVIDIA_VISIBLE_DEVICES=all NVIDIA_DRIVER_CAPABILITIES=compute,utility NV_CUDA_LIB_VERSION=11.8.0-1 NV_NVTX_VERSION=11.8.86-1 NV_LIBNPP_VERSION=11.8.0.86-1 NV_LIBNPP_PACKAGE=libnpp-11-8=11.8.0.86-1 NV_LIBCUSPARSE_VERSION=11.7.5.86-1 NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-8 NV_LIBCUBLAS_VERSION=11.11.3.6-1 NV_LIBCUBLAS_PACKAGE=libcublas-11-8=11.11.3.6-1 NV_LIBNCCL_PACKAGE_NAME=libnccl2 NV_LIBNCCL_PACKAGE_VERSION=2.16.2-1 NCCL_VERSION=2.16.2-1 NV_LIBNCCL_PACKAGE=libnccl2=2.16.2-1+cuda11.8 NVIDIA_PRODUCT_NAME=CUDA NV_CUDA_CUDART_DEV_VERSION=11.8.89-1 NV_NVML_DEV_VERSION=11.8.86-1 NV_LIBCUSPARSE_DEV_VERSION=11.7.5.86-1 NV_LIBNPP_DEV_VERSION=11.8.0.86-1 NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-8=11.8.0.86-1 NV_LIBCUBLAS_DEV_VERSION=11.11.3.6-1 NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-8 NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-8=11.11.3.6-1 NV_CUDA_NSIGHT_COMPUTE_VERSION=11.8.0-1 NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-11-8=11.8.0-1 NV_NVPROF_VERSION=11.8.87-1 NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-8=11.8.87-1 NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev NV_LIBNCCL_DEV_PACKAGE_VERSION=2.16.2-1 NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.16.2-1+cuda11.8 LIBRARY_PATH=/usr/local/cuda/lib64/stubs NV_CUDNN_VERSION=8.9.6.50 NV_CUDNN_PACKAGE_NAME=libcudnn8 NV_CUDNN_PACKAGE=libcudnn8=8.9.6.50-1+cuda11.8 NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.6.50-1+cuda11.8 PYTORCH_VERSION=2.1.2
镜像标签
8.9.6.50: com.nvidia.cudnn.version nvidia_driver: com.nvidia.volumes.needed NVIDIA CORPORATION <cudatools@nvidia.com>: maintainer ubuntu: org.opencontainers.image.ref.name 20.04: org.opencontainers.image.version
镜像安全扫描 查看Trivy扫描报告

系统OS: ubuntu 20.04 扫描引擎: Trivy 扫描时间: 2024-12-10 00:37

低危漏洞:162 中危漏洞:1660 高危漏洞:40 严重漏洞:1

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/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel  docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel  docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel

Shell快速替换命令

sed -i 's#pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel#' 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/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel  docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel'

Ansible快速分发-Containerd

#ansible k8s -m shell -a 'ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel  docker.io/pytorch/pytorch:2.1.2-cuda11.8-cudnn8-devel'

镜像历史


# 2023-12-19 22:49:28  0.00B 设置工作目录为/workspace
WORKDIR /workspace
                        
# 2023-12-19 22:49:28  0.00B 设置环境变量 PYTORCH_VERSION
ENV PYTORCH_VERSION=2.1.2
                        
# 2023-12-19 22:49:28  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2023-12-19 22:49:28  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2023-12-19 22:49:28  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2023-12-19 22:49:28  0.00B 设置环境变量 PATH
ENV PATH=/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2023-12-19 22:49:28  6.76KB 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.1.2 TRITON_VERSION=2.1.0+e6216047b8 TARGETPLATFORM=linux/amd64 CUDA_VERSION=11.8.0 /bin/sh -c if test -n "${TRITON_VERSION}" -a "${TARGETPLATFORM}" != "linux/arm64"; then         DEBIAN_FRONTEND=noninteractive apt install -y --no-install-recommends gcc;         rm -rf /var/lib/apt/lists/*;     fi # buildkit
                        
# 2023-12-19 22:49:27  7.59GB 复制新文件或目录到容器中
COPY /opt/conda /opt/conda # buildkit
                        
# 2023-12-19 22:41:29  3.26MB 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.1.2 TRITON_VERSION=2.1.0+e6216047b8 TARGETPLATFORM=linux/amd64 CUDA_VERSION=11.8.0 /bin/sh -c apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends         ca-certificates         libjpeg-dev         libpng-dev         && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2023-12-19 22:41:29  0.00B 添加元数据标签
LABEL com.nvidia.volumes.needed=nvidia_driver
                        
# 2023-12-19 22:41:29  0.00B 定义构建参数
ARG CUDA_VERSION
                        
# 2023-12-19 22:41:29  0.00B 定义构建参数
ARG TARGETPLATFORM
                        
# 2023-12-19 22:41:29  0.00B 定义构建参数
ARG TRITON_VERSION
                        
# 2023-12-19 22:41:29  0.00B 定义构建参数
ARG PYTORCH_VERSION
                        
# 2023-11-10 15:16:58  2.37GB 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
                        
# 2023-11-10 15:16:58  0.00B 添加元数据标签
LABEL com.nvidia.cudnn.version=8.9.6.50
                        
# 2023-11-10 15:16:58  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 15:16:58  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 15:16:58  0.00B 设置环境变量 NV_CUDNN_PACKAGE_DEV
ENV NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.6.50-1+cuda11.8
                        
# 2023-11-10 15:16:58  0.00B 设置环境变量 NV_CUDNN_PACKAGE
ENV NV_CUDNN_PACKAGE=libcudnn8=8.9.6.50-1+cuda11.8
                        
# 2023-11-10 15:16:58  0.00B 设置环境变量 NV_CUDNN_PACKAGE_NAME
ENV NV_CUDNN_PACKAGE_NAME=libcudnn8
                        
# 2023-11-10 15:16:58  0.00B 设置环境变量 NV_CUDNN_VERSION
ENV NV_CUDNN_VERSION=8.9.6.50
                        
# 2023-11-10 14:55:34  0.00B 设置环境变量 LIBRARY_PATH
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
                        
# 2023-11-10 14:55:34  377.32KB 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
                        
# 2023-11-10 14:55:29  4.71GB 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     libtinfo5 libncursesw5     cuda-cudart-dev-11-8=${NV_CUDA_CUDART_DEV_VERSION}     cuda-command-line-tools-11-8=${NV_CUDA_LIB_VERSION}     cuda-minimal-build-11-8=${NV_CUDA_LIB_VERSION}     cuda-libraries-dev-11-8=${NV_CUDA_LIB_VERSION}     cuda-nvml-dev-11-8=${NV_NVML_DEV_VERSION}     ${NV_NVPROF_DEV_PACKAGE}     ${NV_LIBNPP_DEV_PACKAGE}     libcusparse-dev-11-8=${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
                        
# 2023-11-10 14:55:29  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 14:55:29  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE
ENV NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.16.2-1+cuda11.8
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.16.2-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_VERSION
ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.16.2-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_NAME
ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_NVPROF_DEV_PACKAGE
ENV NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-8=11.8.87-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_NVPROF_VERSION
ENV NV_NVPROF_VERSION=11.8.87-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE
ENV NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-11-8=11.8.0-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_VERSION
ENV NV_CUDA_NSIGHT_COMPUTE_VERSION=11.8.0-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE
ENV NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-8=11.11.3.6-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE_NAME
ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-8
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_VERSION
ENV NV_LIBCUBLAS_DEV_VERSION=11.11.3.6-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBNPP_DEV_PACKAGE
ENV NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-8=11.8.0.86-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBNPP_DEV_VERSION
ENV NV_LIBNPP_DEV_VERSION=11.8.0.86-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBCUSPARSE_DEV_VERSION
ENV NV_LIBCUSPARSE_DEV_VERSION=11.7.5.86-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_NVML_DEV_VERSION
ENV NV_NVML_DEV_VERSION=11.8.86-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_CUDA_CUDART_DEV_VERSION
ENV NV_CUDA_CUDART_DEV_VERSION=11.8.89-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.8.0-1
                        
# 2023-11-10 14:43:29  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2023-11-10 14:43:29  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2023-11-10 14:43:29  3.06KB 复制新文件或目录到容器中
COPY entrypoint.d/ /opt/nvidia/entrypoint.d/ # buildkit
                        
# 2023-11-10 14:43:29  258.26KB 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
                        
# 2023-11-10 14:43:29  2.42GB 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-11-8=${NV_CUDA_LIB_VERSION}     ${NV_LIBNPP_PACKAGE}     cuda-nvtx-11-8=${NV_NVTX_VERSION}     libcusparse-11-8=${NV_LIBCUSPARSE_VERSION}     ${NV_LIBCUBLAS_PACKAGE}     ${NV_LIBNCCL_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2023-11-10 14:43:29  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 14:43:29  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE
ENV NV_LIBNCCL_PACKAGE=libnccl2=2.16.2-1+cuda11.8
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.16.2-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.16.2-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE
ENV NV_LIBCUBLAS_PACKAGE=libcublas-11-8=11.11.3.6-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=11.11.3.6-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-8
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=11.7.5.86-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNPP_PACKAGE
ENV NV_LIBNPP_PACKAGE=libnpp-11-8=11.8.0.86-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=11.8.0.86-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=11.8.86-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.8.0-1
                        
# 2023-11-10 14:37:17  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2023-11-10 14:37:17  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2023-11-10 14:37:17  17.29KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2023-11-10 14:37:17  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2023-11-10 14:37:17  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
                        
# 2023-11-10 14:37:17  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
                        
# 2023-11-10 14:37:17  150.68MB 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-11-8=${NV_CUDA_CUDART_VERSION}     ${NV_CUDA_COMPAT_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=11.8.0
                        
# 2023-11-10 14:37:03  18.32MB 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 -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/${NVARCH}/3bf863cc.pub | apt-key add - &&     echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/${NVARCH} /" > /etc/apt/sources.list.d/cuda.list &&     apt-get purge --autoremove -y curl     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2023-11-10 14:37:03  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 14:37:03  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-8
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=11.8.89-1
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 NVIDIA_REQUIRE_CUDA brand brand brand brand brand brand brand brand brand brand
ENV NVIDIA_REQUIRE_CUDA=cuda>=11.8 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
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 NVARCH
ENV NVARCH=x86_64
                        
# 2023-10-03 18:45:52  0.00B 
/bin/sh -c #(nop)  CMD ["/bin/bash"]
                        
# 2023-10-03 18:45:51  72.79MB 
/bin/sh -c #(nop) ADD file:4809da414c2d478b4d991cbdaa2df457f2b3d07d0ff6cf673f09a66f90833e81 in / 
                        
# 2023-10-03 18:45:50  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.version=20.04
                        
# 2023-10-03 18:45:50  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.ref.name=ubuntu
                        
# 2023-10-03 18:45:50  0.00B 
/bin/sh -c #(nop)  ARG LAUNCHPAD_BUILD_ARCH
                        
# 2023-10-03 18:45:50  0.00B 
/bin/sh -c #(nop)  ARG RELEASE
                        
                    

镜像信息

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            "NV_LIBNPP_DEV_VERSION=11.8.0.86-1",
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            "NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.16.2-1+cuda11.8",
            "LIBRARY_PATH=/usr/local/cuda/lib64/stubs",
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            "NV_CUDNN_PACKAGE_NAME=libcudnn8",
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            "NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.6.50-1+cuda11.8",
            "PYTORCH_VERSION=2.1.2"
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}

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