docker.io/anchorxia/musev:latest linux/amd64

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docker.io/anchorxia/musev 镜像描述

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源镜像 docker.io/anchorxia/musev:latest
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest
镜像ID sha256:eea69ab0e0f4b9652562b889956854b1543f12d2a3c2d26f2accdb83b537696c
镜像TAG latest
大小 27.89GB
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 28 次
贡献者
镜像创建 2024-04-10T15:47:14.90153929+08:00
同步时间 2025-04-30 01:59
更新时间 2025-05-10 13:06
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=2.0.1
镜像标签
musev gpu runtime image, base docker is pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel: Description anchorxia@tencent.com: Email anchorxia: MAINTAINER 8.5.0.96: com.nvidia.cudnn.version nvidia_driver: com.nvidia.volumes.needed NVIDIA CORPORATION <cudatools@nvidia.com>: maintainer

Docker拉取命令

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest  docker.io/anchorxia/musev:latest

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest  docker.io/anchorxia/musev:latest

Shell快速替换命令

sed -i 's#anchorxia/musev:latest#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest#' 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/anchorxia/musev:latest && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest  docker.io/anchorxia/musev:latest'

Ansible快速分发-Containerd

#ansible k8s -m shell -a 'ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/anchorxia/musev:latest  docker.io/anchorxia/musev:latest'

镜像构建历史


# 2024-04-10 15:47:14  0.00B 指定运行容器时使用的用户
USER root
                        
# 2024-04-10 15:47:14  80.82MB 执行命令并创建新的镜像层
RUN |1 DEBIAN_FRONTEND=noninteractive /bin/bash --login -c . /opt/conda/etc/profile.d/conda.sh      && echo "source activate musev" >> ~/.bashrc     && conda activate musev     && conda env list     && pip --no-cache-dir install cuid gradio==4.12 spaces # buildkit
                        
# 2024-04-10 15:47:14  0.00B 
SHELL [/bin/bash --login -c]
                        
# 2024-04-10 15:47:14  0.00B 指定运行容器时使用的用户
USER root
                        
# 2024-04-10 15:47:14  0.00B 定义构建参数
ARG DEBIAN_FRONTEND=noninteractive
                        
# 2024-04-10 15:47:14  0.00B 添加元数据标签
LABEL Description=musev gpu runtime image, base docker is pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel
                        
# 2024-04-10 15:47:14  0.00B 添加元数据标签
LABEL Email=anchorxia@tencent.com
                        
# 2024-04-10 15:47:14  0.00B 添加元数据标签
LABEL MAINTAINER=anchorxia
                        
# 2024-03-21 23:30:32  0.00B 指定运行容器时使用的用户
USER root
                        
# 2024-03-21 23:30:32  12.66GB 执行命令并创建新的镜像层
RUN |1 DEBIAN_FRONTEND=noninteractive /bin/bash --login -c conda create -n musev python=3.10.6 -y     && . /opt/conda/etc/profile.d/conda.sh      && echo "source activate musev" >> ~/.bashrc     && conda activate musev     && pip install tensorflow==2.12.0 tensorboard==2.12.0     && pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118     && pip install ninja==1.11.1     && pip install --no-cache-dir transformers==4.33.1 bitsandbytes==0.41.1 decord==0.6.0 accelerate==0.22.0 xformers==0.0.21 omegaconf einops imageio==2.31.1     && pip install --no-cache-dir pandas h5py matplotlib modelcards==0.1.6 pynvml==11.5.0 black pytest moviepy==1.0.3 torch-tb-profiler==0.4.1 scikit-learn librosa ffmpeg easydict webp mediapipe==0.10.3     && pip install --no-cache-dir cython==3.0.2 easydict gdown infomap==2.7.1 insightface==0.7.3 ipython librosa==0.10.1 onnx==1.14.1 onnxruntime==1.15.1 onnxsim==0.4.33 opencv_python Pillow protobuf==3.20.3 pytube==15.0.0 PyYAML     && pip install --no-cache-dir requests scipy six tqdm gradio==3.43.2 albumentations==1.3.1 opencv-contrib-python==4.8.0.76 imageio-ffmpeg==0.4.8 pytorch-lightning==2.0.8 test-tube==0.7.5     && pip install --no-cache-dir timm addict yapf prettytable safetensors==0.3.3 basicsr fvcore pycocotools wandb==0.15.10 wget ffmpeg-python     && pip install --no-cache-dir streamlit webdataset kornia==0.7.0 open_clip_torch==2.20.0 streamlit-drawable-canvas==0.9.3 torchmetrics==1.1.1     && pip install --no-cache-dir invisible-watermark==0.1.5 gdown==4.5.3 ftfy==6.1.1 modelcards==0.1.6     && pip install ipywidgets==8.0.3     && python -m ipykernel install --user --name projectv --display-name "python(projectv)"     && pip install --no-cache-dir matplotlib==3.6.2 redis==4.5.1  pydantic[dotenv]==1.10.2 loguru==0.6.0 IProgress==0.4     && pip install git+https://github.com/tencent-ailab/IP-Adapter.git     && pip install -U openmim     && mim install mmengine     && mim install "mmcv>=2.0.1"     && mim install "mmdet>=3.1.0"     && mim install "mmpose>=1.1.0"     && pip install --no-cache-dir  markupsafe==2.0.1 # buildkit
                        
# 2024-03-21 22:58:32  0.00B 
SHELL [/bin/bash --login -c]
                        
# 2024-03-21 22:58:32  524.57MB 执行命令并创建新的镜像层
RUN |1 DEBIAN_FRONTEND=noninteractive /bin/sh -c apt install -y wget git curl tmux cmake htop iotop git-lfs zip     && apt install -y autojump     && apt install -y libasound-dev portaudio19-dev libportaudio2 libportaudiocpp0 ffmpeg     && apt clean # buildkit
                        
# 2024-03-21 20:21:45  1.45GB 执行命令并创建新的镜像层
RUN |1 DEBIAN_FRONTEND=noninteractive /bin/sh -c apt -y update     && apt -y upgrade # buildkit
                        
# 2024-03-21 20:21:45  0.00B 指定运行容器时使用的用户
USER root
                        
# 2024-03-21 20:21:45  0.00B 定义构建参数
ARG DEBIAN_FRONTEND=noninteractive
                        
# 2024-03-21 20:21:45  0.00B 添加元数据标签
LABEL Description=gpu development image, from docker pull pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel
                        
# 2024-03-21 20:21:45  0.00B 添加元数据标签
LABEL Email=anchorxia@tencent.com
                        
# 2024-03-21 20:21:45  0.00B 添加元数据标签
LABEL MAINTAINER=anchorxia
                        
# 2023-05-13 07:31:56  0.00B 设置工作目录为/workspace
WORKDIR /workspace
                        
# 2023-05-13 07:31:56  0.00B 设置环境变量 PYTORCH_VERSION
ENV PYTORCH_VERSION=2.0.1
                        
# 2023-05-13 07:31:56  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2023-05-13 07:31:56  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2023-05-13 07:31:56  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2023-05-13 07:31:56  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
                        
# 2023-05-13 07:31:56  0.00B 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.0.1 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=11.7.0 /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
                        
# 2023-05-13 07:31:56  6.38GB 复制新文件或目录到容器中
COPY /opt/conda /opt/conda # buildkit
                        
# 2023-05-13 07:25:22  3.25MB 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.0.1 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=11.7.0 /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
                        
# 2023-05-13 07:25:22  0.00B 添加元数据标签
LABEL com.nvidia.volumes.needed=nvidia_driver
                        
# 2023-05-13 07:25:22  0.00B 定义构建参数
ARG CUDA_VERSION
                        
# 2023-05-13 07:25:22  0.00B 定义构建参数
ARG TARGETPLATFORM
                        
# 2023-05-13 07:25:22  0.00B 定义构建参数
ARG TRITON_VERSION
                        
# 2023-05-13 07:25:22  0.00B 定义构建参数
ARG PYTORCH_VERSION
                        
# 2022-12-17 09:09:24  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 09:09:24  0.00B 添加元数据标签
LABEL com.nvidia.cudnn.version=8.5.0.96
                        
# 2022-12-17 09:09:24  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-17 09:09:24  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-17 09:09:24  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 09:09:24  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 09:09:24  0.00B 设置环境变量 NV_CUDNN_PACKAGE_NAME
ENV NV_CUDNN_PACKAGE_NAME=libcudnn8
                        
# 2022-12-17 09:09:24  0.00B 设置环境变量 NV_CUDNN_VERSION
ENV NV_CUDNN_VERSION=8.5.0.96
                        
# 2022-12-15 04:13:58  0.00B 设置环境变量 LIBRARY_PATH
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
                        
# 2022-12-15 04:13:58  374.63KB 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 04:13:57  2.82GB 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     libtinfo5 libncursesw5     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 04:13:57  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-15 04:13:57  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-15 04:13:57  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 04:13:57  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.13.4-1
                        
# 2022-12-15 04:13:57  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 04:13:57  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_NAME
ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
                        
# 2022-12-15 04:13:57  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 04:13:57  0.00B 设置环境变量 NV_NVPROF_VERSION
ENV NV_NVPROF_VERSION=11.7.50-1
                        
# 2022-12-15 04:13:57  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 04:13:57  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 04:13:57  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 04:13:57  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 04:13:57  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 04:13:57  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 04:13:57  0.00B 设置环境变量 NV_NVML_DEV_VERSION
ENV NV_NVML_DEV_VERSION=11.7.50-1
                        
# 2022-12-15 04:13:57  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 04:13:57  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.7.0-1
                        
# 2022-12-15 04:03:03  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2022-12-15 04:03:03  0.00B 设置环境变量 NVIDIA_CUDA_END_OF_LIFE
ENV NVIDIA_CUDA_END_OF_LIFE=1
                        
# 2022-12-15 04:03:03  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2022-12-15 04:03:03  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2022-12-15 04:03:03  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 04:03:03  258.24KB 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 04:03:02  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 04:03:02  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-15 04:03:02  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-15 04:03:02  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 04:03:02  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.13.4-1
                        
# 2022-12-15 04:03:02  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.13.4-1
                        
# 2022-12-15 04:03:02  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2022-12-15 04:03:02  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 04:03:02  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=11.10.1.25-1
                        
# 2022-12-15 04:03:02  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-7
                        
# 2022-12-15 04:03:02  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=11.7.3.50-1
                        
# 2022-12-15 04:03:02  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 04:03:02  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=11.7.3.21-1
                        
# 2022-12-15 04:03:02  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=11.7.50-1
                        
# 2022-12-15 04:03:02  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.7.0-1
                        
# 2022-12-15 03:58:37  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2022-12-15 03:58:37  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2022-12-15 03:58:37  16.05KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2022-12-15 03:58:37  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 03:58:37  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 03:58:37  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 03:58:37  119.68MB 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-11-7=${NV_CUDA_CUDART_VERSION}     ${NV_CUDA_COMPAT_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2022-12-15 03:58:22  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=11.7.0
                        
# 2022-12-15 03:58:22  18.28MB 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
                        
# 2022-12-15 03:58:22  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-15 03:58:22  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-15 03:58:22  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-7
                        
# 2022-12-15 03:58:22  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=11.7.60-1
                        
# 2022-12-15 03:58:22  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 03:58:22  0.00B 设置环境变量 NVARCH
ENV NVARCH=x86_64
                        
# 2022-12-09 09:20:21  0.00B 
/bin/sh -c #(nop)  CMD ["bash"]
                        
# 2022-12-09 09:20:21  72.79MB 
/bin/sh -c #(nop) ADD file:9d282119af0c42bc823c95b4192a3350cf2cad670622017356dd2e637762e425 in / 
                        
                    

镜像信息

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    "RepoTags": [
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    "Parent": "",
    "Comment": "buildkit.dockerfile.v0",
    "Created": "2024-04-10T15:47:14.90153929+08:00",
    "Container": "",
    "ContainerConfig": null,
    "DockerVersion": "",
    "Author": "",
    "Config": {
        "Hostname": "",
        "Domainname": "",
        "User": "root",
        "AttachStdin": false,
        "AttachStdout": false,
        "AttachStderr": false,
        "Tty": false,
        "OpenStdin": false,
        "StdinOnce": false,
        "Env": [
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            "NVARCH=x86_64",
            "NVIDIA_REQUIRE_CUDA=cuda\u003e=11.7 brand=tesla,driver\u003e=450,driver\u003c451 brand=tesla,driver\u003e=470,driver\u003c471 brand=unknown,driver\u003e=470,driver\u003c471 brand=nvidia,driver\u003e=470,driver\u003c471 brand=nvidiartx,driver\u003e=470,driver\u003c471 brand=geforce,driver\u003e=470,driver\u003c471 brand=geforcertx,driver\u003e=470,driver\u003c471 brand=quadro,driver\u003e=470,driver\u003c471 brand=quadrortx,driver\u003e=470,driver\u003c471 brand=titan,driver\u003e=470,driver\u003c471 brand=titanrtx,driver\u003e=470,driver\u003c471 brand=tesla,driver\u003e=510,driver\u003c511 brand=unknown,driver\u003e=510,driver\u003c511 brand=nvidia,driver\u003e=510,driver\u003c511 brand=nvidiartx,driver\u003e=510,driver\u003c511 brand=quadro,driver\u003e=510,driver\u003c511 brand=quadrortx,driver\u003e=510,driver\u003c511 brand=titan,driver\u003e=510,driver\u003c511 brand=titanrtx,driver\u003e=510,driver\u003c511 brand=geforce,driver\u003e=510,driver\u003c511 brand=geforcertx,driver\u003e=510,driver\u003c511",
            "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=2.0.1"
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        "Cmd": null,
        "Image": "",
        "Volumes": null,
        "WorkingDir": "/workspace",
        "Entrypoint": [
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        "OnBuild": null,
        "Labels": {
            "Description": "musev gpu runtime image, base docker is pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel",
            "Email": "anchorxia@tencent.com",
            "MAINTAINER": "anchorxia",
            "com.nvidia.cudnn.version": "8.5.0.96",
            "com.nvidia.volumes.needed": "nvidia_driver",
            "maintainer": "NVIDIA CORPORATION \u003ccudatools@nvidia.com\u003e"
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        "Shell": [
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}

更多版本

docker.io/anchorxia/musev:latest

linux/amd64 docker.io27.89GB2025-04-30 01:59
27