docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel linux/amd64

docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel - 国内下载镜像源浏览次数:26

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:2.0.0-cuda11.7-cudnn8-devel
国内镜像	swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
镜像ID	sha256:5e7815e32cbc353587f1452afaf1da11cf257b93ecb8322600809f4769c6779a
镜像TAG	2.0.0-cuda11.7-cudnn8-devel
大小	13.10GB
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	26 次
贡献者
镜像创建	2023-03-20T11:08:45.700884784-07:00
同步时间	2025-01-11 00:22
更新时间	2025-01-14 20:45

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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>=11.7 brand=tesla,driver>=450,driver<451 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>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511 NV_CUDA_CUDART_VERSION=11.7.60-1 NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-7 CUDA_VERSION=11.7.0 LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 NVIDIA_VISIBLE_DEVICES=all NVIDIA_DRIVER_CAPABILITIES=compute,utility NV_CUDA_LIB_VERSION=11.7.0-1 NV_NVTX_VERSION=11.7.50-1 NV_LIBNPP_VERSION=11.7.3.21-1 NV_LIBNPP_PACKAGE=libnpp-11-7=11.7.3.21-1 NV_LIBCUSPARSE_VERSION=11.7.3.50-1 NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-7 NV_LIBCUBLAS_VERSION=11.10.1.25-1 NV_LIBCUBLAS_PACKAGE=libcublas-11-7=11.10.1.25-1 NV_LIBNCCL_PACKAGE_NAME=libnccl2 NV_LIBNCCL_PACKAGE_VERSION=2.13.4-1 NCCL_VERSION=2.13.4-1 NV_LIBNCCL_PACKAGE=libnccl2=2.13.4-1+cuda11.7 NVIDIA_PRODUCT_NAME=CUDA NVIDIA_CUDA_END_OF_LIFE=1 NV_CUDA_CUDART_DEV_VERSION=11.7.60-1 NV_NVML_DEV_VERSION=11.7.50-1 NV_LIBCUSPARSE_DEV_VERSION=11.7.3.50-1 NV_LIBNPP_DEV_VERSION=11.7.3.21-1 NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-7=11.7.3.21-1 NV_LIBCUBLAS_DEV_VERSION=11.10.1.25-1 NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-7 NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-7=11.10.1.25-1 NV_NVPROF_VERSION=11.7.50-1 NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-7=11.7.50-1 NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev NV_LIBNCCL_DEV_PACKAGE_VERSION=2.13.4-1 NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.13.4-1+cuda11.7 LIBRARY_PATH=/usr/local/cuda/lib64/stubs NV_CUDNN_VERSION=8.5.0.96 NV_CUDNN_PACKAGE_NAME=libcudnn8 NV_CUDNN_PACKAGE=libcudnn8=8.5.0.96-1+cuda11.7 NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.5.0.96-1+cuda11.7 PYTORCH_VERSION=v2.0.0

镜像标签

8.5.0.96: com.nvidia.cudnn.version nvidia_driver: com.nvidia.volumes.needed NVIDIA CORPORATION <cudatools@nvidia.com>: maintainer

镜像安全扫描查看Trivy扫描报告

系统OS: ubuntu 18.04 扫描引擎: Trivy 扫描时间: 2025-01-11 00:25

低危漏洞:47

中危漏洞:203

高危漏洞:26

严重漏洞:1

docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel (ubuntu 18.04) (ubuntu)

Python (python-pkg)

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:2.0.0-cuda11.7-cudnn8-devel
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel  docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel  docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel

Shell快速替换命令

sed -i 's#pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.0.0-cuda11.7-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:2.0.0-cuda11.7-cudnn8-devel && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel  docker.io/pytorch/pytorch:2.0.0-cuda11.7-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:2.0.0-cuda11.7-cudnn8-devel && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel  docker.io/pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel'

镜像构建历史


# 2023-03-21 02:08:45  0.00B 设置工作目录为/workspace
WORKDIR /workspace
                        
# 2023-03-21 02:08:45  0.00B 设置环境变量 PYTORCH_VERSION
ENV PYTORCH_VERSION=v2.0.0
                        
# 2023-03-21 02:08:45  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2023-03-21 02:08:45  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2023-03-21 02:08:45  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2023-03-21 02:08:45  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
                        
# 2023-03-21 02:08:45  0.00B 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=v2.0.0 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=11.7.0 /bin/sh -c rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2023-03-21 02:08:45  0.00B 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=v2.0.0 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=11.7.0 /bin/sh -c if test -n "${TRITON_VERSION}" -a "${TARGETPLATFORM}" != "linux/arm64"; then         apt install -y --no-install-recommends gcc;     fi # buildkit
                        
# 2023-03-21 02:08:44  6.30GB 复制新文件或目录到容器中
COPY /opt/conda /opt/conda # buildkit
                        
# 2023-03-21 02:04:03  49.18MB 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=v2.0.0 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=11.7.0 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends         ca-certificates         libjpeg-dev         libpng-dev # buildkit
                        
# 2023-03-21 02:04:03  0.00B 添加元数据标签
LABEL com.nvidia.volumes.needed=nvidia_driver
                        
# 2023-03-21 02:04:03  0.00B 定义构建参数
ARG CUDA_VERSION
                        
# 2023-03-21 02:04:03  0.00B 定义构建参数
ARG TARGETPLATFORM
                        
# 2023-03-21 02:04:03  0.00B 定义构建参数
ARG TRITON_VERSION
                        
# 2023-03-21 02:04:03  0.00B 定义构建参数
ARG PYTORCH_VERSION
                        
# 2022-12-17 08:57:55  1.94GB 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
                        
# 2022-12-17 08:57:55  0.00B 添加元数据标签
LABEL com.nvidia.cudnn.version=8.5.0.96
                        
# 2022-12-17 08:57:55  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-17 08:57:55  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-17 08:57:55  0.00B 设置环境变量 NV_CUDNN_PACKAGE_DEV
ENV NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.5.0.96-1+cuda11.7
                        
# 2022-12-17 08:57:55  0.00B 设置环境变量 NV_CUDNN_PACKAGE
ENV NV_CUDNN_PACKAGE=libcudnn8=8.5.0.96-1+cuda11.7
                        
# 2022-12-17 08:57:55  0.00B 设置环境变量 NV_CUDNN_PACKAGE_NAME
ENV NV_CUDNN_PACKAGE_NAME=libcudnn8
                        
# 2022-12-17 08:57:55  0.00B 设置环境变量 NV_CUDNN_VERSION
ENV NV_CUDNN_VERSION=8.5.0.96
                        
# 2022-12-15 04:02:14  0.00B 设置环境变量 LIBRARY_PATH
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
                        
# 2022-12-15 04:02:14  369.64KB 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
                        
# 2022-12-15 04:02:12  2.78GB 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-11-7=${NV_CUDA_CUDART_DEV_VERSION}     cuda-command-line-tools-11-7=${NV_CUDA_LIB_VERSION}     cuda-minimal-build-11-7=${NV_CUDA_LIB_VERSION}     cuda-libraries-dev-11-7=${NV_CUDA_LIB_VERSION}     cuda-nvml-dev-11-7=${NV_NVML_DEV_VERSION}     ${NV_NVPROF_DEV_PACKAGE}     ${NV_LIBNPP_DEV_PACKAGE}     libcusparse-dev-11-7=${NV_LIBCUSPARSE_DEV_VERSION}     ${NV_LIBCUBLAS_DEV_PACKAGE}     ${NV_LIBNCCL_DEV_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2022-12-15 04:02:12  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-15 04:02:12  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE
ENV NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.13.4-1+cuda11.7
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.13.4-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_VERSION
ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.13.4-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_NAME
ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_NVPROF_DEV_PACKAGE
ENV NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-7=11.7.50-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_NVPROF_VERSION
ENV NV_NVPROF_VERSION=11.7.50-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE
ENV NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-7=11.10.1.25-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE_NAME
ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-7
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_VERSION
ENV NV_LIBCUBLAS_DEV_VERSION=11.10.1.25-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBNPP_DEV_PACKAGE
ENV NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-7=11.7.3.21-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBNPP_DEV_VERSION
ENV NV_LIBNPP_DEV_VERSION=11.7.3.21-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_LIBCUSPARSE_DEV_VERSION
ENV NV_LIBCUSPARSE_DEV_VERSION=11.7.3.50-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_NVML_DEV_VERSION
ENV NV_NVML_DEV_VERSION=11.7.50-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_CUDA_CUDART_DEV_VERSION
ENV NV_CUDA_CUDART_DEV_VERSION=11.7.60-1
                        
# 2022-12-15 04:02:12  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.7.0-1
                        
# 2022-12-15 03:58:47  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NVIDIA_CUDA_END_OF_LIFE
ENV NVIDIA_CUDA_END_OF_LIFE=1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2022-12-15 03:58:47  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2022-12-15 03:58:47  3.04KB 复制新文件或目录到容器中
COPY entrypoint.d/ /opt/nvidia/entrypoint.d/ # buildkit
                        
# 2022-12-15 03:58:47  256.97KB 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
                        
# 2022-12-15 03:58:47  1.82GB 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-11-7=${NV_CUDA_LIB_VERSION}     ${NV_LIBNPP_PACKAGE}     cuda-nvtx-11-7=${NV_NVTX_VERSION}     libcusparse-11-7=${NV_LIBCUSPARSE_VERSION}     ${NV_LIBCUBLAS_PACKAGE}     ${NV_LIBNCCL_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2022-12-15 03:58:47  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-15 03:58:47  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE
ENV NV_LIBNCCL_PACKAGE=libnccl2=2.13.4-1+cuda11.7
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.13.4-1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.13.4-1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE
ENV NV_LIBCUBLAS_PACKAGE=libcublas-11-7=11.10.1.25-1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=11.10.1.25-1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-7
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=11.7.3.50-1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBNPP_PACKAGE
ENV NV_LIBNPP_PACKAGE=libnpp-11-7=11.7.3.21-1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=11.7.3.21-1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=11.7.50-1
                        
# 2022-12-15 03:58:47  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.7.0-1
                        
# 2022-12-15 03:56:36  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2022-12-15 03:56:36  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2022-12-15 03:56:36  16.05KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2022-12-15 03:56:36  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2022-12-15 03:56:36  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
                        
# 2022-12-15 03:56:36  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
                        
# 2022-12-15 03:56:35  119.70MB 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-11-7=${NV_CUDA_CUDART_VERSION}     ${NV_CUDA_COMPAT_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2022-12-15 03:55:39  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=11.7.0
                        
# 2022-12-15 03:55:39  16.52MB 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/ubuntu1804/${NVARCH}/3bf863cc.pub | apt-key add - &&     echo "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/${NVARCH} /" > /etc/apt/sources.list.d/cuda.list &&     apt-get purge --autoremove -y curl     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2022-12-15 03:55:39  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-15 03:55:39  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-15 03:55:39  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-7
                        
# 2022-12-15 03:55:39  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=11.7.60-1
                        
# 2022-12-15 03:55:39  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
ENV NVIDIA_REQUIRE_CUDA=cuda>=11.7 brand=tesla,driver>=450,driver<451 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>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511
                        
# 2022-12-15 03:55:39  0.00B 设置环境变量 NVARCH
ENV NVARCH=x86_64
                        
# 2022-12-09 09:20:12  0.00B 
/bin/sh -c #(nop)  CMD ["bash"]
                        
# 2022-12-09 09:20:11  63.15MB 
/bin/sh -c #(nop) ADD file:3c88cea17de40599dc8b8da90adc8439635066835e930f9573ec6cc856c5c096 in /

镜像信息

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