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docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel linux/amd64

docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel - 国内下载镜像源 浏览次数:2086 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:2.1.0-cuda11.8-cudnn8-devel
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel
镜像ID sha256:a7108713a5fbdc821b0418301f30cdfb51727a4c28dbb902636b41732a9b0c7b
镜像TAG 2.1.0-cuda11.8-cudnn8-devel
大小 17.39GB
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 2086 次
贡献者
镜像创建 2023-10-04T23:07:45.820268209Z
同步时间 2024-10-02 00:43
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>=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=geforce,driver>=510,driver<511 brand=geforcertx,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=tesla,driver>=515,driver<516 brand=unknown,driver>=515,driver<516 brand=nvidia,driver>=515,driver<516 brand=nvidiartx,driver>=515,driver<516 brand=geforce,driver>=515,driver<516 brand=geforcertx,driver>=515,driver<516 brand=quadro,driver>=515,driver<516 brand=quadrortx,driver>=515,driver<516 brand=titan,driver>=515,driver<516 brand=titanrtx,driver>=515,driver<516 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=8.9.0.131 NV_CUDNN_PACKAGE_NAME=libcudnn8 NV_CUDNN_PACKAGE=libcudnn8=8.9.0.131-1+cuda11.8 NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.0.131-1+cuda11.8 PYTORCH_VERSION=2.1.0
镜像标签
8.9.0.131: 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: 2024-10-27 11:44

低危漏洞:185 中危漏洞:1564 高危漏洞:60 严重漏洞:0

Docker拉取命令

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel  docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel  docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel

Shell快速替换命令

sed -i 's#pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.0-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:2.1.0-cuda11.8-cudnn8-devel && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel  docker.io/pytorch/pytorch:2.1.0-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:2.1.0-cuda11.8-cudnn8-devel && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel  docker.io/pytorch/pytorch:2.1.0-cuda11.8-cudnn8-devel'

镜像构建历史


# 2023-10-05 07:07:45  0.00B 设置工作目录为/workspace
WORKDIR /workspace
                        
# 2023-10-05 07:07:45  0.00B 设置环境变量 PYTORCH_VERSION
ENV PYTORCH_VERSION=2.1.0
                        
# 2023-10-05 07:07:45  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2023-10-05 07:07:45  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2023-10-05 07:07:45  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2023-10-05 07:07:45  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
                        
# 2023-10-05 07:07:45  6.76KB 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.1.0 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
                        
# 2023-10-05 07:07:44  7.56GB 复制新文件或目录到容器中
COPY /opt/conda /opt/conda # buildkit
                        
# 2023-10-05 06:58:50  3.26MB 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.1.0 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
                        
# 2023-10-05 06:58:50  0.00B 添加元数据标签
LABEL com.nvidia.volumes.needed=nvidia_driver
                        
# 2023-10-05 06:58:50  0.00B 定义构建参数
ARG CUDA_VERSION
                        
# 2023-10-05 06:58:50  0.00B 定义构建参数
ARG TARGETPLATFORM
                        
# 2023-10-05 06:58:50  0.00B 定义构建参数
ARG TRITON_VERSION
                        
# 2023-10-05 06:58:50  0.00B 定义构建参数
ARG PYTORCH_VERSION
                        
# 2023-06-21 09:26:21  2.46GB 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
                        
# 2023-06-21 09:26:21  0.00B 添加元数据标签
LABEL com.nvidia.cudnn.version=8.9.0.131
                        
# 2023-06-21 09:26:21  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-06-21 09:26:21  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-06-21 09:26:21  0.00B 设置环境变量 NV_CUDNN_PACKAGE_DEV
ENV NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.0.131-1+cuda11.8
                        
# 2023-06-21 09:26:21  0.00B 设置环境变量 NV_CUDNN_PACKAGE
ENV NV_CUDNN_PACKAGE=libcudnn8=8.9.0.131-1+cuda11.8
                        
# 2023-06-21 09:26:21  0.00B 设置环境变量 NV_CUDNN_PACKAGE_NAME
ENV NV_CUDNN_PACKAGE_NAME=libcudnn8
                        
# 2023-06-21 09:26:21  0.00B 设置环境变量 NV_CUDNN_VERSION
ENV NV_CUDNN_VERSION=8.9.0.131
                        
# 2023-06-21 09:01:32  0.00B 设置环境变量 LIBRARY_PATH
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
                        
# 2023-06-21 09:01:32  377.31KB 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
                        
# 2023-06-21 09:01:31  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
                        
# 2023-06-21 09:01:31  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-06-21 09:01:31  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-06-21 09:01:31  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
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.16.2-1
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_VERSION
ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.16.2-1
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_NAME
ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
                        
# 2023-06-21 09:01:31  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
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_NVPROF_VERSION
ENV NV_NVPROF_VERSION=11.8.87-1
                        
# 2023-06-21 09:01:31  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
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_VERSION
ENV NV_CUDA_NSIGHT_COMPUTE_VERSION=11.8.0-1
                        
# 2023-06-21 09:01:31  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
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE_NAME
ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-8
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_VERSION
ENV NV_LIBCUBLAS_DEV_VERSION=11.11.3.6-1
                        
# 2023-06-21 09:01:31  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
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_LIBNPP_DEV_VERSION
ENV NV_LIBNPP_DEV_VERSION=11.8.0.86-1
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_LIBCUSPARSE_DEV_VERSION
ENV NV_LIBCUSPARSE_DEV_VERSION=11.7.5.86-1
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_NVML_DEV_VERSION
ENV NV_NVML_DEV_VERSION=11.8.86-1
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_CUDA_CUDART_DEV_VERSION
ENV NV_CUDA_CUDART_DEV_VERSION=11.8.89-1
                        
# 2023-06-21 09:01:31  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.8.0-1
                        
# 2023-06-21 08:51:50  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2023-06-21 08:51:50  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2023-06-21 08:51:50  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2023-06-21 08:51:50  3.06KB 复制新文件或目录到容器中
COPY entrypoint.d/ /opt/nvidia/entrypoint.d/ # buildkit
                        
# 2023-06-21 08:51:49  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
                        
# 2023-06-21 08:51:49  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
                        
# 2023-06-21 08:51:49  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-06-21 08:51:49  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE
ENV NV_LIBNCCL_PACKAGE=libnccl2=2.16.2-1+cuda11.8
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.16.2-1
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.16.2-1
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE
ENV NV_LIBCUBLAS_PACKAGE=libcublas-11-8=11.11.3.6-1
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=11.11.3.6-1
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-8
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=11.7.5.86-1
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBNPP_PACKAGE
ENV NV_LIBNPP_PACKAGE=libnpp-11-8=11.8.0.86-1
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=11.8.0.86-1
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=11.8.86-1
                        
# 2023-06-21 08:51:49  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.8.0-1
                        
# 2023-06-21 08:41:06  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2023-06-21 08:41:06  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2023-06-21 08:41:06  17.29KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2023-06-21 08:41:06  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2023-06-21 08:41:06  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
                        
# 2023-06-21 08:41:06  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
                        
# 2023-06-21 08:41:01  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
                        
# 2023-06-21 08:40:21  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=11.8.0
                        
# 2023-06-21 08:40:21  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
                        
# 2023-06-21 08:40:21  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-06-21 08:40:21  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-06-21 08:40:21  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-8
                        
# 2023-06-21 08:40:21  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=11.8.89-1
                        
# 2023-06-21 08:40:21  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 brand
ENV NVIDIA_REQUIRE_CUDA=cuda>=11.8 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=geforce,driver>=510,driver<511 brand=geforcertx,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=tesla,driver>=515,driver<516 brand=unknown,driver>=515,driver<516 brand=nvidia,driver>=515,driver<516 brand=nvidiartx,driver>=515,driver<516 brand=geforce,driver>=515,driver<516 brand=geforcertx,driver>=515,driver<516 brand=quadro,driver>=515,driver<516 brand=quadrortx,driver>=515,driver<516 brand=titan,driver>=515,driver<516 brand=titanrtx,driver>=515,driver<516
                        
# 2023-06-21 08:40:21  0.00B 设置环境变量 NVARCH
ENV NVARCH=x86_64
                        
# 2023-06-06 01:08:58  0.00B 
/bin/sh -c #(nop)  CMD ["/bin/bash"]
                        
# 2023-06-06 01:08:58  72.79MB 
/bin/sh -c #(nop) ADD file:655d373cb551d0dd5d7867f88a4f98908dc3f16190986f693e88c423e6f21b8d in / 
                        
# 2023-06-06 01:08:57  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.version=20.04
                        
# 2023-06-06 01:08:57  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.ref.name=ubuntu
                        
# 2023-06-06 01:08:57  0.00B 
/bin/sh -c #(nop)  ARG LAUNCHPAD_BUILD_ARCH
                        
# 2023-06-06 01:08:57  0.00B 
/bin/sh -c #(nop)  ARG RELEASE
                        
                    

镜像信息

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}

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linux/amd64 docker.io12.86GB2025-11-01 00:22
460

docker.io/pytorch/pytorch:2.9.0-cuda13.0-cudnn9-runtime

linux/amd64 docker.io5.68GB2025-11-06 02:58
923

docker.io/pytorch/pytorch:1.13.0-cuda11.6-cudnn8-devel

linux/amd64 docker.io18.57GB2025-11-10 00:45
319

docker.io/pytorch/pytorch:2.4.0-cuda12.1-cudnn9-runtime

linux/amd64 docker.io8.06GB2025-11-22 01:41
479

docker.io/pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime

linux/amd64 docker.io7.20GB2025-12-04 00:11
456

docker.io/pytorch/pytorch:2.8.0-cuda12.9-cudnn9-runtime

linux/amd64 docker.io8.38GB2025-12-10 01:22
465

docker.io/pytorch/pytorch:2.9.1-cuda12.8-cudnn9-runtime

linux/amd64 docker.io7.97GB2025-12-12 00:37
966

docker.io/pytorch/pytorch:2.9.1-cuda12.8-cudnn9-devel

linux/amd64 docker.io17.21GB2025-12-12 01:02
873

docker.io/pytorch/pytorch:2.7.0-cuda12.6-cudnn9-runtime

linux/amd64 docker.io6.35GB2025-12-13 01:45
569

docker.io/pytorch/pytorch:1.7.1-cuda11.0-cudnn8-devel

linux/amd64 docker.io12.86GB2025-12-16 01:48
373

docker.io/pytorch/pytorch:1.7.0-cuda11.0-cudnn8-devel

linux/amd64 docker.io11.97GB2025-12-16 02:38
235

docker.io/pytorch/pytorch:2.9.1-cuda12.6-cudnn9-runtime

linux/amd64 docker.io7.12GB2026-01-11 00:39
346

docker.io/pytorch/pytorch:1.8.1-cuda11.1-cudnn8-devel

linux/amd64 docker.io16.47GB2026-01-21 01:16
240

docker.io/pytorch/pytorch:2.6.0-cuda11.8-cudnn9-devel

linux/amd64 docker.io13.52GB2026-02-28 02:06
109

docker.io/pytorch/pytorch:2.10.0-cuda12.8-cudnn9-devel

linux/amd64 docker.io17.09GB2026-03-11 02:39
190

docker.io/pytorch/pytorch:2.9.1-cuda13.0-cudnn9-devel

linux/amd64 docker.io12.94GB2026-03-20 02:29
147