docker.io/rayproject/ray-ml:2.30.0-py310-gpu linux/amd64

docker.io/rayproject/ray-ml:2.30.0-py310-gpu - 国内下载镜像源 浏览次数:147
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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:2.30.0-py310-gpu
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.30.0-py310-gpu
镜像ID sha256:0328e7e2dfd28cae48fde833e36f7df20f27e0e70a5a2f7f4d2b46bb39b3d726
镜像TAG 2.30.0-py310-gpu
大小 21.88GB
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 147 次
贡献者 28*******0@qq.com
镜像创建 2024-06-20T20:06:17.51366237Z
同步时间 2024-09-27 00:30
更新时间 2025-01-13 04:53
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>=11.8 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 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=8.9.6.50 NV_CUDNN_PACKAGE_NAME=libcudnn8 NV_CUDNN_PACKAGE=libcudnn8=8.9.6.50-1+cuda11.8 NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.6.50-1+cuda11.8 TZ=America/Los_Angeles LC_ALL=C.UTF-8 LANG=C.UTF-8 HOME=/home/ray
镜像标签
8.9.6.50: com.nvidia.cudnn.version NVIDIA CORPORATION <cudatools@nvidia.com>: maintainer ubuntu: org.opencontainers.image.ref.name 20.04: org.opencontainers.image.version

Docker拉取命令 无权限下载?点我修复 

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.30.0-py310-gpu
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.30.0-py310-gpu  docker.io/rayproject/ray-ml:2.30.0-py310-gpu

Containerd拉取命令 

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.30.0-py310-gpu
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.30.0-py310-gpu  docker.io/rayproject/ray-ml:2.30.0-py310-gpu

Shell快速替换命令 

sed -i 's#rayproject/ray-ml:2.30.0-py310-gpu#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.30.0-py310-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:2.30.0-py310-gpu && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.30.0-py310-gpu  docker.io/rayproject/ray-ml:2.30.0-py310-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:2.30.0-py310-gpu && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/rayproject/ray-ml:2.30.0-py310-gpu  docker.io/rayproject/ray-ml:2.30.0-py310-gpu'

镜像构建历史 


# 2024-06-21 04:06:17  10.62KB 执行命令并创建新的镜像层
RUN |3 WHEEL_PATH=.whl/ray-2.30.0-cp310-cp310-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
                        
# 2024-06-21 04:06:16  337.33MB 执行命令并创建新的镜像层
RUN |3 WHEEL_PATH=.whl/ray-2.30.0-cp310-cp310-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
                        
# 2024-06-21 04:05:54  93.55MB 复制新文件或目录到容器中
COPY .whl .whl # buildkit
                        
# 2024-06-21 04:05:53  65.97MB 复制新文件或目录到容器中
COPY .whl/ray-2.30.0-cp310-cp310-manylinux2014_x86_64.whl . # buildkit
                        
# 2024-06-21 04:05:53  59.91KB 复制新文件或目录到容器中
COPY requirements_compiled.txt ./ # buildkit
                        
# 2024-06-21 04:05:53  0.00B 定义构建参数
ARG CONSTRAINTS_FILE=requirements_compiled.txt
                        
# 2024-06-21 04:05:53  0.00B 定义构建参数
ARG FIND_LINKS_PATH=.whl
                        
# 2024-06-21 04:05:53  0.00B 定义构建参数
ARG WHEEL_PATH
                        
# 2024-06-18 04:14:20  0.00B 执行命令并创建新的镜像层
RUN /bin/bash -c python -c "import tensorflow_probability" # buildkit
                        
# 2024-06-18 04:14:20  10.48KB 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
                        
# 2024-06-18 04:14:19  10.45GB 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
                        
# 2024-06-18 04:07:25  1.98KB 复制新文件或目录到容器中
COPY *install-ml-docker-requirements.sh docker/ray-ml/*install-ml-docker-requirements.sh ./ # buildkit
                        
# 2024-06-18 04:07:25  59.91KB 复制新文件或目录到容器中
COPY *requirements_compiled.txt python/*requirements_compiled.txt ./ # buildkit
                        
# 2024-06-18 04:07:25  6.47KB 复制新文件或目录到容器中
COPY *requirements.txt python/*requirements.txt python/requirements/ml/*requirements.txt python/requirements/docker/*requirements.txt ./ # buildkit
                        
# 2024-06-17 22:54:41  0.00B 设置工作目录为/home/ray
WORKDIR /home/ray
                        
# 2024-06-17 22:54:41  1.19GB 执行命令并创建新的镜像层
RUN |7 BASE_IMAGE=nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04 AUTOSCALER=autoscaler DEBIAN_FRONTEND=noninteractive PYTHON_VERSION=3.10 HOSTTYPE=x86_64 RAY_UID=1000 RAY_GID=100 /bin/bash -c sudo apt-get update -y && sudo apt-get upgrade -y     && sudo apt-get install -y         git         libjemalloc-dev         wget         cmake         g++         zlib1g-dev         $(if [ "$AUTOSCALER" = "autoscaler" ]; then echo         tmux         screen         rsync         netbase         openssh-client         gnupg; fi)     && wget --quiet         "https://repo.anaconda.com/miniconda/Miniconda3-py311_23.10.0-1-Linux-${HOSTTYPE}.sh"         -O /tmp/miniconda.sh     && /bin/bash /tmp/miniconda.sh -b -u -p $HOME/anaconda3     && $HOME/anaconda3/bin/conda init     && echo 'export PATH=$HOME/anaconda3/bin:$PATH' >> /home/ray/.bashrc     && rm /tmp/miniconda.sh      && $HOME/anaconda3/bin/conda install -y libgcc-ng python=$PYTHON_VERSION     && $HOME/anaconda3/bin/conda install -y -c conda-forge libffi=3.4.2     && $HOME/anaconda3/bin/conda clean -y --all     && $HOME/anaconda3/bin/pip install --no-cache-dir         flatbuffers         cython==0.29.37         numpy\>=1.20         psutil     && $HOME/anaconda3/bin/pip uninstall -y dask     && sudo apt-get autoremove -y cmake zlib1g-dev         $(if [[ "$BASE_IMAGE" == "ubuntu:focal" && "$HOSTTYPE" == "x86_64" ]]; then echo         g++; fi)     && sudo rm -rf /var/lib/apt/lists/*     && sudo apt-get clean     && (if [ "$AUTOSCALER" = "autoscaler" ];         then $HOME/anaconda3/bin/pip --no-cache-dir install         "redis>=3.5.0,<4.0.0"         "six==1.13.0"         "boto3==1.26.76"         "pyOpenSSL==22.1.0"         "cryptography==38.0.1"         "google-api-python-client==1.7.8"         "google-oauth"         "azure-cli-core==2.40.0"         "azure-identity==1.10.0"         "azure-mgmt-compute==23.1.0"         "azure-mgmt-network==19.0.0"         "azure-mgmt-resource==20.0.0"         "msrestazure==0.6.4";     fi;) # buildkit
                        
# 2024-06-17 22:53:12  0.00B 
SHELL [/bin/bash -c]
                        
# 2024-06-17 22:53:12  0.00B 设置环境变量 HOME
ENV HOME=/home/ray
                        
# 2024-06-17 22:53:12  0.00B 指定运行容器时使用的用户
USER 1000
                        
# 2024-06-17 22:53:12  6.53MB 执行命令并创建新的镜像层
RUN |7 BASE_IMAGE=nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04 AUTOSCALER=autoscaler DEBIAN_FRONTEND=noninteractive PYTHON_VERSION=3.10 HOSTTYPE=x86_64 RAY_UID=1000 RAY_GID=100 /bin/sh -c apt-get update -y     && apt-get install -y sudo tzdata     && useradd -ms /bin/bash -d /home/ray ray --uid $RAY_UID --gid $RAY_GID     && usermod -aG sudo ray     && echo 'ray ALL=NOPASSWD: ALL' >> /etc/sudoers     && rm -rf /var/lib/apt/lists/*     && apt-get clean # buildkit
                        
# 2024-06-17 22:53:12  0.00B 定义构建参数
ARG RAY_GID=100
                        
# 2024-06-17 22:53:12  0.00B 定义构建参数
ARG RAY_UID=1000
                        
# 2024-06-17 22:53:12  0.00B 定义构建参数
ARG HOSTTYPE=x86_64
                        
# 2024-06-17 22:53:12  0.00B 定义构建参数
ARG PYTHON_VERSION=3.8.16
                        
# 2024-06-17 22:53:12  0.00B 定义构建参数
ARG DEBIAN_FRONTEND=noninteractive
                        
# 2024-06-17 22:53:12  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
                        
# 2024-06-17 22:53:12  0.00B 设置环境变量 LANG
ENV LANG=C.UTF-8
                        
# 2024-06-17 22:53:12  0.00B 设置环境变量 LC_ALL
ENV LC_ALL=C.UTF-8
                        
# 2024-06-17 22:53:12  0.00B 设置环境变量 TZ
ENV TZ=America/Los_Angeles
                        
# 2024-06-17 22:53:12  0.00B 定义构建参数
ARG AUTOSCALER=autoscaler
                        
# 2024-06-17 22:53:12  0.00B 定义构建参数
ARG BASE_IMAGE
                        
# 2023-11-10 15:16:58  2.37GB 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 15:16:58  0.00B 添加元数据标签
LABEL com.nvidia.cudnn.version=8.9.6.50
                        
# 2023-11-10 15:16:58  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 15:16:58  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 15:16:58  0.00B 设置环境变量 NV_CUDNN_PACKAGE_DEV
ENV NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.6.50-1+cuda11.8
                        
# 2023-11-10 15:16:58  0.00B 设置环境变量 NV_CUDNN_PACKAGE
ENV NV_CUDNN_PACKAGE=libcudnn8=8.9.6.50-1+cuda11.8
                        
# 2023-11-10 15:16:58  0.00B 设置环境变量 NV_CUDNN_PACKAGE_NAME
ENV NV_CUDNN_PACKAGE_NAME=libcudnn8
                        
# 2023-11-10 15:16:58  0.00B 设置环境变量 NV_CUDNN_VERSION
ENV NV_CUDNN_VERSION=8.9.6.50
                        
# 2023-11-10 14:55:34  0.00B 设置环境变量 LIBRARY_PATH
ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
                        
# 2023-11-10 14:55:34  377.32KB 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 14:55:29  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
                        
# 2023-11-10 14:55:29  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 14:55:29  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 14:55:29  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
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.16.2-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_VERSION
ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.16.2-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBNCCL_DEV_PACKAGE_NAME
ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
                        
# 2023-11-10 14:55:29  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
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_NVPROF_VERSION
ENV NV_NVPROF_VERSION=11.8.87-1
                        
# 2023-11-10 14:55:29  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
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_CUDA_NSIGHT_COMPUTE_VERSION
ENV NV_CUDA_NSIGHT_COMPUTE_VERSION=11.8.0-1
                        
# 2023-11-10 14:55:29  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
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_PACKAGE_NAME
ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-8
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBCUBLAS_DEV_VERSION
ENV NV_LIBCUBLAS_DEV_VERSION=11.11.3.6-1
                        
# 2023-11-10 14:55:29  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
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBNPP_DEV_VERSION
ENV NV_LIBNPP_DEV_VERSION=11.8.0.86-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_LIBCUSPARSE_DEV_VERSION
ENV NV_LIBCUSPARSE_DEV_VERSION=11.7.5.86-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_NVML_DEV_VERSION
ENV NV_NVML_DEV_VERSION=11.8.86-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_CUDA_CUDART_DEV_VERSION
ENV NV_CUDA_CUDART_DEV_VERSION=11.8.89-1
                        
# 2023-11-10 14:55:29  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.8.0-1
                        
# 2023-11-10 14:43:29  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2023-11-10 14:43:29  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2023-11-10 14:43:29  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 14:43:29  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
                        
# 2023-11-10 14:43:29  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
                        
# 2023-11-10 14:43:29  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 14:43:29  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE
ENV NV_LIBNCCL_PACKAGE=libnccl2=2.16.2-1+cuda11.8
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.16.2-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.16.2-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE
ENV NV_LIBCUBLAS_PACKAGE=libcublas-11-8=11.11.3.6-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=11.11.3.6-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-8
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=11.7.5.86-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNPP_PACKAGE
ENV NV_LIBNPP_PACKAGE=libnpp-11-8=11.8.0.86-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=11.8.0.86-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=11.8.86-1
                        
# 2023-11-10 14:43:29  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.8.0-1
                        
# 2023-11-10 14:37:17  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2023-11-10 14:37:17  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2023-11-10 14:37:17  17.29KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2023-11-10 14:37:17  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 14:37:17  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 14:37:17  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 14:37:17  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
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=11.8.0
                        
# 2023-11-10 14:37:03  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
                        
# 2023-11-10 14:37:03  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2023-11-10 14:37:03  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-8
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=11.8.89-1
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 NVIDIA_REQUIRE_CUDA brand brand brand brand brand brand brand brand brand brand
ENV NVIDIA_REQUIRE_CUDA=cuda>=11.8 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
                        
# 2023-11-10 14:37:03  0.00B 设置环境变量 NVARCH
ENV NVARCH=x86_64
                        
# 2023-10-03 18:45:52  0.00B 
/bin/sh -c #(nop)  CMD ["/bin/bash"]
                        
# 2023-10-03 18:45:51  72.79MB 
/bin/sh -c #(nop) ADD file:4809da414c2d478b4d991cbdaa2df457f2b3d07d0ff6cf673f09a66f90833e81 in / 
                        
# 2023-10-03 18:45:50  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.version=20.04
                        
# 2023-10-03 18:45:50  0.00B 
/bin/sh -c #(nop)  LABEL org.opencontainers.image.ref.name=ubuntu
                        
# 2023-10-03 18:45:50  0.00B 
/bin/sh -c #(nop)  ARG LAUNCHPAD_BUILD_ARCH
                        
# 2023-10-03 18:45:50  0.00B 
/bin/sh -c #(nop)  ARG RELEASE

镜像信息 

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    "Parent": "",
    "Comment": "buildkit.dockerfile.v0",
    "Created": "2024-06-20T20:06:17.51366237Z",
    "Container": "",
    "ContainerConfig": null,
    "DockerVersion": "",
    "Author": "",
    "Config": {
        "Hostname": "",
        "Domainname": "",
        "User": "1000",
        "AttachStdin": false,
        "AttachStdout": false,
        "AttachStderr": false,
        "Tty": false,
        "OpenStdin": false,
        "StdinOnce": false,
        "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",
            "NVARCH=x86_64",
            "NVIDIA_REQUIRE_CUDA=cuda\u003e=11.8 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",
            "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=8.9.6.50",
            "NV_CUDNN_PACKAGE_NAME=libcudnn8",
            "NV_CUDNN_PACKAGE=libcudnn8=8.9.6.50-1+cuda11.8",
            "NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.6.50-1+cuda11.8",
            "TZ=America/Los_Angeles",
            "LC_ALL=C.UTF-8",
            "LANG=C.UTF-8",
            "HOME=/home/ray"
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        "Cmd": null,
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        "WorkingDir": "/home/ray",
        "Entrypoint": [
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        "Labels": {
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}

更多版本

docker.io/rayproject/ray-ml:2.30.0-py310-gpu

linux/amd64 docker.io21.88GB2024-09-27 00:30
146