docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu linux/amd64

docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu - 国内下载镜像源 浏览次数:8
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Ray ML

基于Apache Ray的机器学习框架。支持分布式训练、在线服务和批处理预测等场景，提供高性能、高并发性和易用性的机器学习体验。

源镜像 docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu
镜像ID sha256:e07756170900334cf267d3a65fe557a5b03a15a6b46ac491e014fae84d11c96a
镜像TAG 2.40.0.160e35-py311-gpu
大小 21.81GB
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 /home/ray
OS/平台 linux/amd64
浏览量 8 次
贡献者
镜像创建 2024-12-27T23:36:52.207880455Z
同步时间 2026-03-14 03:53
环境变量
PATH=/home/ray/anaconda3/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>=12.1 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 NV_CUDA_CUDART_VERSION=12.1.105-1 NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-1 CUDA_VERSION=12.1.1 LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 NVIDIA_VISIBLE_DEVICES=all NVIDIA_DRIVER_CAPABILITIES=compute,utility NV_CUDA_LIB_VERSION=12.1.1-1 NV_NVTX_VERSION=12.1.105-1 NV_LIBNPP_VERSION=12.1.0.40-1 NV_LIBNPP_PACKAGE=libnpp-12-1=12.1.0.40-1 NV_LIBCUSPARSE_VERSION=12.1.0.106-1 NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-1 NV_LIBCUBLAS_VERSION=12.1.3.1-1 NV_LIBCUBLAS_PACKAGE=libcublas-12-1=12.1.3.1-1 NV_LIBNCCL_PACKAGE_NAME=libnccl2 NV_LIBNCCL_PACKAGE_VERSION=2.17.1-1 NCCL_VERSION=2.17.1-1 NV_LIBNCCL_PACKAGE=libnccl2=2.17.1-1+cuda12.1 NVIDIA_PRODUCT_NAME=CUDA NV_CUDA_CUDART_DEV_VERSION=12.1.105-1 NV_NVML_DEV_VERSION=12.1.105-1 NV_LIBCUSPARSE_DEV_VERSION=12.1.0.106-1 NV_LIBNPP_DEV_VERSION=12.1.0.40-1 NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-1=12.1.0.40-1 NV_LIBCUBLAS_DEV_VERSION=12.1.3.1-1 NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-1 NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-1=12.1.3.1-1 NV_CUDA_NSIGHT_COMPUTE_VERSION=12.1.1-1 NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-1=12.1.1-1 NV_NVPROF_VERSION=12.1.105-1 NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-1=12.1.105-1 NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev NV_LIBNCCL_DEV_PACKAGE_VERSION=2.17.1-1 NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.17.1-1+cuda12.1 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+cuda12.1 NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.0.131-1+cuda12.1 TZ=America/Los_Angeles LC_ALL=C.UTF-8 LANG=C.UTF-8 HOME=/home/ray
镜像标签
8.9.0.131: com.nvidia.cudnn.version NVIDIA CORPORATION <cudatools@nvidia.com>: maintainer ubuntu: org.opencontainers.image.ref.name 22.04: org.opencontainers.image.version
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Docker拉取命令 

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu  docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu

Containerd拉取命令 

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu  docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu

Shell快速替换命令 

sed -i 's#rayproject/ray-ml:2.40.0.160e35-py311-gpu#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu#' 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/rayproject/ray-ml:2.40.0.160e35-py311-gpu && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu  docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu'

Ansible快速分发-Containerd 

#ansible k8s -m shell -a 'ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu  docker.io/rayproject/ray-ml:2.40.0.160e35-py311-gpu'

镜像构建历史 


# 2024-12-28 07:36:52  9.95KB 执行命令并创建新的镜像层
RUN |3 WHEEL_PATH=.whl/ray-2.40.0-cp311-cp311-manylinux2014_x86_64.whl FIND_LINKS_PATH=.whl CONSTRAINTS_FILE=requirements_compiled.txt /bin/bash -c $HOME/anaconda3/bin/pip freeze > /home/ray/pip-freeze.txt # buildkit
                        
# 2024-12-28 07:36:51  267.58MB 执行命令并创建新的镜像层
RUN |3 WHEEL_PATH=.whl/ray-2.40.0-cp311-cp311-manylinux2014_x86_64.whl FIND_LINKS_PATH=.whl CONSTRAINTS_FILE=requirements_compiled.txt /bin/bash -c $HOME/anaconda3/bin/pip --no-cache-dir install -c $CONSTRAINTS_FILE     `basename $WHEEL_PATH`[all]     --find-links $FIND_LINKS_PATH && sudo rm `basename $WHEEL_PATH` # buildkit
                        
# 2024-12-28 07:36:37  95.44MB 复制新文件或目录到容器中
COPY .whl .whl # buildkit
                        
# 2024-12-28 07:36:37  66.97MB 复制新文件或目录到容器中
COPY .whl/ray-2.40.0-cp311-cp311-manylinux2014_x86_64.whl . # buildkit
                        
# 2024-12-28 07:36:37  0.00B 定义构建参数
ARG CONSTRAINTS_FILE=requirements_compiled.txt
                        
# 2024-12-28 07:36:37  0.00B 定义构建参数
ARG FIND_LINKS_PATH=.whl
                        
# 2024-12-28 07:36:37  0.00B 定义构建参数
ARG WHEEL_PATH
                        
# 2024-12-28 07:04:49  0.00B 执行命令并创建新的镜像层
RUN /bin/bash -c python -c "import tensorflow_probability" # buildkit
                        
# 2024-12-28 07:04:49  9.76KB 执行命令并创建新的镜像层
RUN /bin/bash -c $HOME/anaconda3/bin/pip freeze > /home/ray/pip-freeze.txt # buildkit
                        
# 2024-12-28 07:04:48  10.79GB 执行命令并创建新的镜像层
RUN /bin/bash -c sudo chmod +x install-ml-docker-requirements.sh     && ./install-ml-docker-requirements.sh # buildkit
                        
# 2024-12-28 06:57:18  2.05KB 复制新文件或目录到容器中
COPY docker/ray-ml/install-ml-docker-requirements.sh ./ # buildkit
                        
# 2024-12-28 06:57:18  6.60KB 复制新文件或目录到容器中
COPY python/*requirements.txt python/requirements/ml/*requirements.txt python/requirements/docker/*requirements.txt ./ # buildkit
                        
# 2024-12-28 06:53:16  0.00B 设置工作目录为/home/ray
WORKDIR /home/ray
                        
# 2024-12-28 06:53:16  793.63MB 执行命令并创建新的镜像层
RUN |7 BASE_IMAGE=nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04 AUTOSCALER=autoscaler DEBIAN_FRONTEND=noninteractive PYTHON_VERSION=3.11 HOSTTYPE=x86_64 RAY_UID=1000 RAY_GID=100 /bin/bash -c /dev/pipes/EOF # buildkit
                        
# 2024-12-28 06:52:21  0.00B 
SHELL [/bin/bash -c]
                        
# 2024-12-28 06:52:21  59.55KB 复制新文件或目录到容器中
COPY python/requirements_compiled.txt /home/ray/requirements_compiled.txt # buildkit
                        
# 2024-12-28 06:52:21  0.00B 设置环境变量 HOME
ENV HOME=/home/ray
                        
# 2024-12-28 06:52:21  0.00B 指定运行容器时使用的用户
USER 1000
                        
# 2024-12-28 06:52:21  307.90MB 执行命令并创建新的镜像层
RUN |7 BASE_IMAGE=nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04 AUTOSCALER=autoscaler DEBIAN_FRONTEND=noninteractive PYTHON_VERSION=3.11 HOSTTYPE=x86_64 RAY_UID=1000 RAY_GID=100 /bin/sh -c /dev/pipes/EOF # buildkit
                        
# 2024-12-28 06:52:21  0.00B 定义构建参数
ARG RAY_GID=100
                        
# 2024-12-28 06:52:21  0.00B 定义构建参数
ARG RAY_UID=1000
                        
# 2024-12-28 06:52:21  0.00B 定义构建参数
ARG HOSTTYPE=x86_64
                        
# 2024-12-28 06:52:21  0.00B 定义构建参数
ARG PYTHON_VERSION=3.9
                        
# 2024-12-28 06:52:21  0.00B 定义构建参数
ARG DEBIAN_FRONTEND=noninteractive
                        
# 2024-12-28 06:52:21  0.00B 设置环境变量 PATH
ENV PATH=/home/ray/anaconda3/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2024-12-28 06:52:21  0.00B 设置环境变量 LANG
ENV LANG=C.UTF-8
                        
# 2024-12-28 06:52:21  0.00B 设置环境变量 LC_ALL
ENV LC_ALL=C.UTF-8
                        
# 2024-12-28 06:52:21  0.00B 设置环境变量 TZ
ENV TZ=America/Los_Angeles
                        
# 2024-12-28 06:52:21  0.00B 定义构建参数
ARG AUTOSCALER=autoscaler
                        
# 2024-12-28 06:52:21  0.00B 定义构建参数
ARG BASE_IMAGE
                        
# 2023-11-10 13:52:16  2.45GB 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 13:52:16  0.00B 添加元数据标签
LABEL com.nvidia.cudnn.version=8.9.0.131
                        
# 2023-11-10 13:52:16  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 13:52:16  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 13:52:16  0.00B 设置环境变量 NV_CUDNN_PACKAGE_DEV
ENV NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.0.131-1+cuda12.1
                        
# 2023-11-10 13:52:16  0.00B 设置环境变量 NV_CUDNN_PACKAGE
ENV NV_CUDNN_PACKAGE=libcudnn8=8.9.0.131-1+cuda12.1
                        
# 2023-11-10 13:52:16  0.00B 设置环境变量 NV_CUDNN_PACKAGE_NAME
ENV NV_CUDNN_PACKAGE_NAME=libcudnn8
                        
# 2023-11-10 13:52:16  0.00B 设置环境变量 NV_CUDNN_VERSION
ENV NV_CUDNN_VERSION=8.9.0.131
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 LIBRARY_PATH
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
                        
# 2023-11-10 13:25:51  385.69KB 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 13:25:51  4.79GB 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-12-1=${NV_CUDA_CUDART_DEV_VERSION}     cuda-command-line-tools-12-1=${NV_CUDA_LIB_VERSION}     cuda-minimal-build-12-1=${NV_CUDA_LIB_VERSION}     cuda-libraries-dev-12-1=${NV_CUDA_LIB_VERSION}     cuda-nvml-dev-12-1=${NV_NVML_DEV_VERSION}     ${NV_NVPROF_DEV_PACKAGE}     ${NV_LIBNPP_DEV_PACKAGE}     libcusparse-dev-12-1=${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 13:25:51  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 13:25:51  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE
ENV NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.17.1-1+cuda12.1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.17.1-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_VERSION
ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.17.1-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_NAME
ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_NVPROF_DEV_PACKAGE
ENV NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-1=12.1.105-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_NVPROF_VERSION
ENV NV_NVPROF_VERSION=12.1.105-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE
ENV NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-1=12.1.1-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_VERSION
ENV NV_CUDA_NSIGHT_COMPUTE_VERSION=12.1.1-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE
ENV NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-1=12.1.3.1-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE_NAME
ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_VERSION
ENV NV_LIBCUBLAS_DEV_VERSION=12.1.3.1-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBNPP_DEV_PACKAGE
ENV NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-1=12.1.0.40-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBNPP_DEV_VERSION
ENV NV_LIBNPP_DEV_VERSION=12.1.0.40-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_LIBCUSPARSE_DEV_VERSION
ENV NV_LIBCUSPARSE_DEV_VERSION=12.1.0.106-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_NVML_DEV_VERSION
ENV NV_NVML_DEV_VERSION=12.1.105-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_CUDA_CUDART_DEV_VERSION
ENV NV_CUDA_CUDART_DEV_VERSION=12.1.105-1
                        
# 2023-11-10 13:25:51  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=12.1.1-1
                        
# 2023-11-10 13:13:35  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2023-11-10 13:13:35  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2023-11-10 13:13:35  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 13:13:35  261.40KB 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 13:13:35  2.01GB 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-12-1=${NV_CUDA_LIB_VERSION}     ${NV_LIBNPP_PACKAGE}     cuda-nvtx-12-1=${NV_NVTX_VERSION}     libcusparse-12-1=${NV_LIBCUSPARSE_VERSION}     ${NV_LIBCUBLAS_PACKAGE}     ${NV_LIBNCCL_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2023-11-10 13:13:35  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 13:13:35  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE
ENV NV_LIBNCCL_PACKAGE=libnccl2=2.17.1-1+cuda12.1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.17.1-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.17.1-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE
ENV NV_LIBCUBLAS_PACKAGE=libcublas-12-1=12.1.3.1-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=12.1.3.1-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=12.1.0.106-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBNPP_PACKAGE
ENV NV_LIBNPP_PACKAGE=libnpp-12-1=12.1.0.40-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=12.1.0.40-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=12.1.105-1
                        
# 2023-11-10 13:13:35  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=12.1.1-1
                        
# 2023-11-10 13:08:12  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2023-11-10 13:08:12  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2023-11-10 13:08:12  17.29KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2023-11-10 13:08:12  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 13:08:12  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 13:08:12  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 13:08:11  149.59MB 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-12-1=${NV_CUDA_CUDART_VERSION}     ${NV_CUDA_COMPAT_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2023-11-10 13:07:58  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=12.1.1
                        
# 2023-11-10 13:07:58  10.56MB 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_1.0-1_all.deb &&     dpkg -i cuda-keyring_1.0-1_all.deb &&     apt-get purge --autoremove -y curl     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2023-11-10 13:07:58  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 13:07:58  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 13:07:58  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-1
                        
# 2023-11-10 13:07:58  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=12.1.105-1
                        
# 2023-11-10 13:07:58  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
ENV NVIDIA_REQUIRE_CUDA=cuda>=12.1 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
                        
# 2023-11-10 13:07:58  0.00B 设置环境变量 NVARCH
ENV NVARCH=x86_64
                        
# 2023-10-05 15:33:32  0.00B 
/bin/sh -c #(nop)  CMD ["/bin/bash"]
                        
# 2023-10-05 15:33:32  77.82MB 
/bin/sh -c #(nop) ADD file:63d5ab3ef0aab308c0e71cb67292c5467f60deafa9b0418cbb220affcd078444 in / 
                        
# 2023-10-05 15:33:30  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.version=22.04
                        
# 2023-10-05 15:33:30  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.ref.name=ubuntu
                        
# 2023-10-05 15:33:30  0.00B 
/bin/sh -c #(nop)  ARG LAUNCHPAD_BUILD_ARCH
                        
# 2023-10-05 15:33:30  0.00B 
/bin/sh -c #(nop)  ARG RELEASE

镜像信息 

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    "RepoTags": [
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    ],
    "Parent": "",
    "Comment": "buildkit.dockerfile.v0",
    "Created": "2024-12-27T23:36:52.207880455Z",
    "Container": "",
    "ContainerConfig": null,
    "DockerVersion": "",
    "Author": "",
    "Config": {
        "Hostname": "",
        "Domainname": "",
        "User": "1000",
        "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=12.1 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=525,driver\u003c526 brand=unknown,driver\u003e=525,driver\u003c526 brand=nvidia,driver\u003e=525,driver\u003c526 brand=nvidiartx,driver\u003e=525,driver\u003c526 brand=geforce,driver\u003e=525,driver\u003c526 brand=geforcertx,driver\u003e=525,driver\u003c526 brand=quadro,driver\u003e=525,driver\u003c526 brand=quadrortx,driver\u003e=525,driver\u003c526 brand=titan,driver\u003e=525,driver\u003c526 brand=titanrtx,driver\u003e=525,driver\u003c526",
            "NV_CUDA_CUDART_VERSION=12.1.105-1",
            "NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-1",
            "CUDA_VERSION=12.1.1",
            "LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64",
            "NVIDIA_VISIBLE_DEVICES=all",
            "NVIDIA_DRIVER_CAPABILITIES=compute,utility",
            "NV_CUDA_LIB_VERSION=12.1.1-1",
            "NV_NVTX_VERSION=12.1.105-1",
            "NV_LIBNPP_VERSION=12.1.0.40-1",
            "NV_LIBNPP_PACKAGE=libnpp-12-1=12.1.0.40-1",
            "NV_LIBCUSPARSE_VERSION=12.1.0.106-1",
            "NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-1",
            "NV_LIBCUBLAS_VERSION=12.1.3.1-1",
            "NV_LIBCUBLAS_PACKAGE=libcublas-12-1=12.1.3.1-1",
            "NV_LIBNCCL_PACKAGE_NAME=libnccl2",
            "NV_LIBNCCL_PACKAGE_VERSION=2.17.1-1",
            "NCCL_VERSION=2.17.1-1",
            "NV_LIBNCCL_PACKAGE=libnccl2=2.17.1-1+cuda12.1",
            "NVIDIA_PRODUCT_NAME=CUDA",
            "NV_CUDA_CUDART_DEV_VERSION=12.1.105-1",
            "NV_NVML_DEV_VERSION=12.1.105-1",
            "NV_LIBCUSPARSE_DEV_VERSION=12.1.0.106-1",
            "NV_LIBNPP_DEV_VERSION=12.1.0.40-1",
            "NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-1=12.1.0.40-1",
            "NV_LIBCUBLAS_DEV_VERSION=12.1.3.1-1",
            "NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-1",
            "NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-1=12.1.3.1-1",
            "NV_CUDA_NSIGHT_COMPUTE_VERSION=12.1.1-1",
            "NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-1=12.1.1-1",
            "NV_NVPROF_VERSION=12.1.105-1",
            "NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-1=12.1.105-1",
            "NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev",
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            "NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.17.1-1+cuda12.1",
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            "NV_CUDNN_PACKAGE_NAME=libcudnn8",
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            "NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.0.131-1+cuda12.1",
            "TZ=America/Los_Angeles",
            "LC_ALL=C.UTF-8",
            "LANG=C.UTF-8",
            "HOME=/home/ray"
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        "Cmd": null,
        "Image": "",
        "Volumes": null,
        "WorkingDir": "/home/ray",
        "Entrypoint": [
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        "OnBuild": null,
        "Labels": {
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            "maintainer": "NVIDIA CORPORATION \u003ccudatools@nvidia.com\u003e",
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}

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