docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel linux/amd64

docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel - 国内下载镜像源 浏览次数:7

这是一个包含PyTorch深度学习框架的Docker镜像。它提供了一个方便的环境,用于开发、训练和部署PyTorch模型。用户可以直接使用此镜像,无需自行安装PyTorch及其依赖项。

源镜像 docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel
镜像ID sha256:f7ab9f76ab8f4b3bc4d94db08281ac19abe1879f15718e0bc782bd8d90fb66a8
镜像TAG th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel
大小 15.57GB
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 7 次
贡献者 yu*******c@foxmail.com
镜像创建 2025-05-28T13:05:12.536000722Z
同步时间 2025-06-30 20:58
更新时间 2025-07-01 02:02
环境变量
PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/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>=12.4 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=12.4.127-1 NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-4 CUDA_VERSION=12.4.1 LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 NVIDIA_VISIBLE_DEVICES=all NVIDIA_DRIVER_CAPABILITIES=compute,utility NV_CUDA_LIB_VERSION=12.4.1-1 NV_NVTX_VERSION=12.4.127-1 NV_LIBNPP_VERSION=12.2.5.30-1 NV_LIBNPP_PACKAGE=libnpp-12-4=12.2.5.30-1 NV_LIBCUSPARSE_VERSION=12.3.1.170-1 NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-4 NV_LIBCUBLAS_VERSION=12.4.5.8-1 NV_LIBCUBLAS_PACKAGE=libcublas-12-4=12.4.5.8-1 NV_LIBNCCL_PACKAGE_NAME=libnccl2 NV_LIBNCCL_PACKAGE_VERSION=2.21.5-1 NCCL_VERSION=2.21.5-1 NV_LIBNCCL_PACKAGE=libnccl2=2.21.5-1+cuda12.4 NVIDIA_PRODUCT_NAME=CUDA NV_CUDA_CUDART_DEV_VERSION=12.4.127-1 NV_NVML_DEV_VERSION=12.4.127-1 NV_LIBCUSPARSE_DEV_VERSION=12.3.1.170-1 NV_LIBNPP_DEV_VERSION=12.2.5.30-1 NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-4=12.2.5.30-1 NV_LIBCUBLAS_DEV_VERSION=12.4.5.8-1 NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-4 NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-4=12.4.5.8-1 NV_CUDA_NSIGHT_COMPUTE_VERSION=12.4.1-1 NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-4=12.4.1-1 NV_NVPROF_VERSION=12.4.127-1 NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-4=12.4.127-1 NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev NV_LIBNCCL_DEV_PACKAGE_VERSION=2.21.5-1 NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.21.5-1+cuda12.4 LIBRARY_PATH=/usr/local/cuda/lib64/stubs PYTORCH_VERSION=2.6.0 MAX_JOBS=16 VLLM_WORKER_MULTIPROC_METHOD=spawn DEBIAN_FRONTEND=noninteractive NODE_OPTIONS= PIP_ROOT_USER_ACTION=ignore
镜像标签
nvidia_driver: com.nvidia.volumes.needed NVIDIA CORPORATION <cudatools@nvidia.com>: maintainer ubuntu: org.opencontainers.image.ref.name 22.04: org.opencontainers.image.version

Docker拉取命令

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel  docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel  docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel

Shell快速替换命令

sed -i 's#hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-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/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel  docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-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/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel  docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel'

镜像构建历史


# 2025-05-28 21:05:12  0.00B 执行命令并创建新的镜像层
RUN |2 APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple /bin/sh -c pip config unset global.index-url &&     pip config unset global.extra-index-url # buildkit
                        
# 2025-05-28 21:05:11  1.31GB 执行命令并创建新的镜像层
RUN |2 APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple /bin/sh -c wget -nv https://ghfast.top/https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.2.post1/flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl &&     pip install --no-cache-dir flashinfer_python-0.2.2.post1+cu124torch2.6-cp38-abi3-linux_x86_64.whl # buildkit
                        
# 2025-05-28 21:04:06  794.18MB 执行命令并创建新的镜像层
RUN |2 APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple /bin/sh -c wget -nv https://ghfast.top/https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp311-cp311-linux_x86_64.whl &&     pip install --no-cache-dir flash_attn-2.7.4.post1+cu12torch2.6cxx11abiFALSE-cp311-cp311-linux_x86_64.whl # buildkit
                        
# 2025-05-28 21:03:35  13.41MB 执行命令并创建新的镜像层
RUN |2 APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple /bin/sh -c pip config set global.index-url "${PIP_INDEX}" &&     pip config set global.extra-index-url "${PIP_INDEX}" &&     python -m pip install --upgrade pip # buildkit
                        
# 2025-05-28 21:03:32  666.29KB 执行命令并创建新的镜像层
RUN |2 APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple /bin/sh -c apt-get update &&     apt-get install -y gcc g++ &&     apt-get clean # buildkit
                        
# 2025-05-28 21:03:27  68.32MB 执行命令并创建新的镜像层
RUN |2 APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple /bin/sh -c apt-get update &&     apt-get install -y git vim &&     apt-get clean # buildkit
                        
# 2025-05-28 21:03:19  161.10MB 执行命令并创建新的镜像层
RUN |2 APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple /bin/sh -c apt-get update &&     apt-get install -y -o Dpkg::Options::="--force-confdef" systemd wget &&     apt-get clean # buildkit
                        
# 2025-05-28 21:03:05  2.79KB 执行命令并创建新的镜像层
RUN |2 APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple /bin/sh -c cp /etc/apt/sources.list /etc/apt/sources.list.bak &&     {     echo "deb ${APT_SOURCE} jammy main restricted universe multiverse";     echo "deb ${APT_SOURCE} jammy-updates main restricted universe multiverse";     echo "deb ${APT_SOURCE} jammy-backports main restricted universe multiverse";     echo "deb ${APT_SOURCE} jammy-security main restricted universe multiverse";     } > /etc/apt/sources.list # buildkit
                        
# 2025-05-28 21:03:05  0.00B 定义构建参数
ARG PIP_INDEX=https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
                        
# 2025-05-28 21:03:05  0.00B 定义构建参数
ARG APT_SOURCE=https://mirrors.tuna.tsinghua.edu.cn/ubuntu/
                        
# 2025-05-28 21:03:05  0.00B 设置环境变量 PIP_ROOT_USER_ACTION
ENV PIP_ROOT_USER_ACTION=ignore
                        
# 2025-05-28 21:03:05  0.00B 设置环境变量 NODE_OPTIONS
ENV NODE_OPTIONS=
                        
# 2025-05-28 21:03:05  0.00B 设置环境变量 DEBIAN_FRONTEND
ENV DEBIAN_FRONTEND=noninteractive
                        
# 2025-05-28 21:03:05  0.00B 设置环境变量 VLLM_WORKER_MULTIPROC_METHOD
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn
                        
# 2025-05-28 21:03:05  0.00B 设置环境变量 MAX_JOBS
ENV MAX_JOBS=16
                        
# 2025-01-30 01:26:35  0.00B 设置工作目录为/workspace
WORKDIR /workspace
                        
# 2025-01-30 01:26:35  0.00B 设置环境变量 PYTORCH_VERSION
ENV PYTORCH_VERSION=2.6.0
                        
# 2025-01-30 01:26:35  0.00B 设置环境变量 PATH
ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2025-01-30 01:26:35  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2025-01-30 01:26:35  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2025-01-30 01:26:35  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2025-01-30 01:26:35  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
                        
# 2025-01-30 01:26:35  0.00B 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.6.0 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=12.4.1 /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
                        
# 2025-01-30 01:26:34  5.95GB 复制新文件或目录到容器中
COPY /opt/conda /opt/conda # buildkit
                        
# 2025-01-30 01:22:43  4.93MB 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.6.0 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=12.4.1 /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
                        
# 2025-01-30 01:22:43  0.00B 添加元数据标签
LABEL com.nvidia.volumes.needed=nvidia_driver
                        
# 2025-01-30 01:22:43  0.00B 定义构建参数
ARG CUDA_VERSION=12.4.1
                        
# 2025-01-30 01:22:43  0.00B 定义构建参数
ARG TARGETPLATFORM=linux/amd64
                        
# 2025-01-30 01:22:43  0.00B 定义构建参数
ARG TRITON_VERSION=
                        
# 2025-01-30 01:22:43  0.00B 定义构建参数
ARG PYTORCH_VERSION=2.6.0
                        
# 2024-04-23 07:54:42  0.00B 设置环境变量 LIBRARY_PATH
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
                        
# 2024-04-23 07:54:42  389.87KB 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
                        
# 2024-04-23 07:54:41  4.98GB 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-12-4=${NV_CUDA_CUDART_DEV_VERSION}     cuda-command-line-tools-12-4=${NV_CUDA_LIB_VERSION}     cuda-minimal-build-12-4=${NV_CUDA_LIB_VERSION}     cuda-libraries-dev-12-4=${NV_CUDA_LIB_VERSION}     cuda-nvml-dev-12-4=${NV_NVML_DEV_VERSION}     ${NV_NVPROF_DEV_PACKAGE}     ${NV_LIBNPP_DEV_PACKAGE}     libcusparse-dev-12-4=${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
                        
# 2024-04-23 07:54:41  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2024-04-23 07:54:41  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE
ENV NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.21.5-1+cuda12.4
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.21.5-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_VERSION
ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.21.5-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_NAME
ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_NVPROF_DEV_PACKAGE
ENV NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-4=12.4.127-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_NVPROF_VERSION
ENV NV_NVPROF_VERSION=12.4.127-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE
ENV NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-4=12.4.1-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_VERSION
ENV NV_CUDA_NSIGHT_COMPUTE_VERSION=12.4.1-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE
ENV NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-4=12.4.5.8-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE_NAME
ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-4
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_VERSION
ENV NV_LIBCUBLAS_DEV_VERSION=12.4.5.8-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBNPP_DEV_PACKAGE
ENV NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-4=12.2.5.30-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBNPP_DEV_VERSION
ENV NV_LIBNPP_DEV_VERSION=12.2.5.30-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_LIBCUSPARSE_DEV_VERSION
ENV NV_LIBCUSPARSE_DEV_VERSION=12.3.1.170-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_NVML_DEV_VERSION
ENV NV_NVML_DEV_VERSION=12.4.127-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_CUDA_CUDART_DEV_VERSION
ENV NV_CUDA_CUDART_DEV_VERSION=12.4.127-1
                        
# 2024-04-23 07:54:41  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=12.4.1-1
                        
# 2024-04-23 07:46:26  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2024-04-23 07:46:26  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2024-04-23 07:46:26  3.06KB 复制新文件或目录到容器中
COPY entrypoint.d/ /opt/nvidia/entrypoint.d/ # buildkit
                        
# 2024-04-23 07:46:26  263.02KB 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
                        
# 2024-04-23 07:46:26  2.05GB 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-12-4=${NV_CUDA_LIB_VERSION}     ${NV_LIBNPP_PACKAGE}     cuda-nvtx-12-4=${NV_NVTX_VERSION}     libcusparse-12-4=${NV_LIBCUSPARSE_VERSION}     ${NV_LIBCUBLAS_PACKAGE}     ${NV_LIBNCCL_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-04-23 07:46:26  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2024-04-23 07:46:26  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE
ENV NV_LIBNCCL_PACKAGE=libnccl2=2.21.5-1+cuda12.4
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.21.5-1
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.21.5-1
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE
ENV NV_LIBCUBLAS_PACKAGE=libcublas-12-4=12.4.5.8-1
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=12.4.5.8-1
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-4
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=12.3.1.170-1
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBNPP_PACKAGE
ENV NV_LIBNPP_PACKAGE=libnpp-12-4=12.2.5.30-1
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=12.2.5.30-1
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=12.4.127-1
                        
# 2024-04-23 07:46:26  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=12.4.1-1
                        
# 2024-04-23 07:42:28  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2024-04-23 07:42:28  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2024-04-23 07:42:28  17.29KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2024-04-23 07:42:28  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2024-04-23 07:42:28  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
                        
# 2024-04-23 07:42:28  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
                        
# 2024-04-23 07:42:28  155.93MB 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-12-4=${NV_CUDA_CUDART_VERSION}     ${NV_CUDA_COMPAT_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-04-23 07:42:16  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=12.4.1
                        
# 2024-04-23 07:42:16  10.57MB 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_1.1-1_all.deb &&     dpkg -i cuda-keyring_1.1-1_all.deb &&     apt-get purge --autoremove -y curl     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-04-23 07:42:16  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2024-04-23 07:42:16  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2024-04-23 07:42:16  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-4
                        
# 2024-04-23 07:42:16  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=12.4.127-1
                        
# 2024-04-23 07:42:16  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>=12.4 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
                        
# 2024-04-23 07:42:16  0.00B 设置环境变量 NVARCH
ENV NVARCH=x86_64
                        
# 2024-04-11 02:52:04  0.00B 
/bin/sh -c #(nop)  CMD ["/bin/bash"]
                        
# 2024-04-11 02:52:04  77.86MB 
/bin/sh -c #(nop) ADD file:3bd10da0673e2e72cb06a1f64a9df49a36341df39b0f762e3d1b38ee4de296fa in / 
                        
# 2024-04-11 02:52:02  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.version=22.04
                        
# 2024-04-11 02:52:02  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.ref.name=ubuntu
                        
# 2024-04-11 02:52:02  0.00B 
/bin/sh -c #(nop)  ARG LAUNCHPAD_BUILD_ARCH
                        
# 2024-04-11 02:52:02  0.00B 
/bin/sh -c #(nop)  ARG RELEASE
                        
                    

镜像信息

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    "Created": "2025-05-28T13:05:12.536000722Z",
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        "User": "",
        "AttachStdin": false,
        "AttachStdout": false,
        "AttachStderr": false,
        "Tty": false,
        "OpenStdin": false,
        "StdinOnce": false,
        "Env": [
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            "NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-4",
            "CUDA_VERSION=12.4.1",
            "LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64",
            "NVIDIA_VISIBLE_DEVICES=all",
            "NVIDIA_DRIVER_CAPABILITIES=compute,utility",
            "NV_CUDA_LIB_VERSION=12.4.1-1",
            "NV_NVTX_VERSION=12.4.127-1",
            "NV_LIBNPP_VERSION=12.2.5.30-1",
            "NV_LIBNPP_PACKAGE=libnpp-12-4=12.2.5.30-1",
            "NV_LIBCUSPARSE_VERSION=12.3.1.170-1",
            "NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-4",
            "NV_LIBCUBLAS_VERSION=12.4.5.8-1",
            "NV_LIBCUBLAS_PACKAGE=libcublas-12-4=12.4.5.8-1",
            "NV_LIBNCCL_PACKAGE_NAME=libnccl2",
            "NV_LIBNCCL_PACKAGE_VERSION=2.21.5-1",
            "NCCL_VERSION=2.21.5-1",
            "NV_LIBNCCL_PACKAGE=libnccl2=2.21.5-1+cuda12.4",
            "NVIDIA_PRODUCT_NAME=CUDA",
            "NV_CUDA_CUDART_DEV_VERSION=12.4.127-1",
            "NV_NVML_DEV_VERSION=12.4.127-1",
            "NV_LIBCUSPARSE_DEV_VERSION=12.3.1.170-1",
            "NV_LIBNPP_DEV_VERSION=12.2.5.30-1",
            "NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-4=12.2.5.30-1",
            "NV_LIBCUBLAS_DEV_VERSION=12.4.5.8-1",
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            "NV_CUDA_NSIGHT_COMPUTE_VERSION=12.4.1-1",
            "NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-4=12.4.1-1",
            "NV_NVPROF_VERSION=12.4.127-1",
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}

更多版本

docker.io/hiyouga/pytorch:th2.6.0-cu124-flashattn2.7.4-cxx11abi0-devel

linux/amd64 docker.io15.57GB2025-06-30 20:58
6