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:80
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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 80 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 2024-11-20 15:59
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'

镜像历史

大小 创建时间 层信息
10.62KB 2024-06-21 04:06:17 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
337.33MB 2024-06-21 04:06:16 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
93.55MB 2024-06-21 04:05:54 COPY .whl .whl # buildkit
65.97MB 2024-06-21 04:05:53 COPY .whl/ray-2.30.0-cp310-cp310-manylinux2014_x86_64.whl . # buildkit
59.91KB 2024-06-21 04:05:53 COPY requirements_compiled.txt ./ # buildkit
0.00B 2024-06-21 04:05:53 ARG CONSTRAINTS_FILE=requirements_compiled.txt
0.00B 2024-06-21 04:05:53 ARG FIND_LINKS_PATH=.whl
0.00B 2024-06-21 04:05:53 ARG WHEEL_PATH
0.00B 2024-06-18 04:14:20 RUN /bin/bash -c python -c "import tensorflow_probability" # buildkit
10.48KB 2024-06-18 04:14:20 RUN /bin/bash -c $HOME/anaconda3/bin/pip freeze > /home/ray/pip-freeze.txt # buildkit
10.45GB 2024-06-18 04:14:19 RUN /bin/bash -c sudo chmod +x install-ml-docker-requirements.sh && ./install-ml-docker-requirements.sh # buildkit
1.98KB 2024-06-18 04:07:25 COPY *install-ml-docker-requirements.sh docker/ray-ml/*install-ml-docker-requirements.sh ./ # buildkit
59.91KB 2024-06-18 04:07:25 COPY *requirements_compiled.txt python/*requirements_compiled.txt ./ # buildkit
6.47KB 2024-06-18 04:07:25 COPY *requirements.txt python/*requirements.txt python/requirements/ml/*requirements.txt python/requirements/docker/*requirements.txt ./ # buildkit
0.00B 2024-06-17 22:54:41 WORKDIR /home/ray
1.19GB 2024-06-17 22:54:41 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
0.00B 2024-06-17 22:53:12 SHELL [/bin/bash -c]
0.00B 2024-06-17 22:53:12 ENV HOME=/home/ray
0.00B 2024-06-17 22:53:12 USER 1000
6.53MB 2024-06-17 22:53:12 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
0.00B 2024-06-17 22:53:12 ARG RAY_GID=100
0.00B 2024-06-17 22:53:12 ARG RAY_UID=1000
0.00B 2024-06-17 22:53:12 ARG HOSTTYPE=x86_64
0.00B 2024-06-17 22:53:12 ARG PYTHON_VERSION=3.8.16
0.00B 2024-06-17 22:53:12 ARG DEBIAN_FRONTEND=noninteractive
0.00B 2024-06-17 22:53:12 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
0.00B 2024-06-17 22:53:12 ENV LANG=C.UTF-8
0.00B 2024-06-17 22:53:12 ENV LC_ALL=C.UTF-8
0.00B 2024-06-17 22:53:12 ENV TZ=America/Los_Angeles
0.00B 2024-06-17 22:53:12 ARG AUTOSCALER=autoscaler
0.00B 2024-06-17 22:53:12 ARG BASE_IMAGE
2.37GB 2023-11-10 15:16:58 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
0.00B 2023-11-10 15:16:58 LABEL com.nvidia.cudnn.version=8.9.6.50
0.00B 2023-11-10 15:16:58 LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
0.00B 2023-11-10 15:16:58 ARG TARGETARCH
0.00B 2023-11-10 15:16:58 ENV NV_CUDNN_PACKAGE_DEV=libcudnn8-dev=8.9.6.50-1+cuda11.8
0.00B 2023-11-10 15:16:58 ENV NV_CUDNN_PACKAGE=libcudnn8=8.9.6.50-1+cuda11.8
0.00B 2023-11-10 15:16:58 ENV NV_CUDNN_PACKAGE_NAME=libcudnn8
0.00B 2023-11-10 15:16:58 ENV NV_CUDNN_VERSION=8.9.6.50
0.00B 2023-11-10 14:55:34 ENV LIBRARY_PATH=/usr/local/cuda/lib64/stubs
377.32KB 2023-11-10 14:55:34 RUN |1 TARGETARCH=amd64 /bin/sh -c apt-mark hold ${NV_LIBCUBLAS_DEV_PACKAGE_NAME} ${NV_LIBNCCL_DEV_PACKAGE_NAME} # buildkit
4.71GB 2023-11-10 14:55:29 RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends libtinfo5 libncursesw5 cuda-cudart-dev-11-8=${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
0.00B 2023-11-10 14:55:29 LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
0.00B 2023-11-10 14:55:29 ARG TARGETARCH
0.00B 2023-11-10 14:55:29 ENV NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.16.2-1+cuda11.8
0.00B 2023-11-10 14:55:29 ENV NCCL_VERSION=2.16.2-1
0.00B 2023-11-10 14:55:29 ENV NV_LIBNCCL_DEV_PACKAGE_VERSION=2.16.2-1
0.00B 2023-11-10 14:55:29 ENV NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
0.00B 2023-11-10 14:55:29 ENV NV_NVPROF_DEV_PACKAGE=cuda-nvprof-11-8=11.8.87-1
0.00B 2023-11-10 14:55:29 ENV NV_NVPROF_VERSION=11.8.87-1
0.00B 2023-11-10 14:55:29 ENV NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-11-8=11.8.0-1
0.00B 2023-11-10 14:55:29 ENV NV_CUDA_NSIGHT_COMPUTE_VERSION=11.8.0-1
0.00B 2023-11-10 14:55:29 ENV NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-11-8=11.11.3.6-1
0.00B 2023-11-10 14:55:29 ENV NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-11-8
0.00B 2023-11-10 14:55:29 ENV NV_LIBCUBLAS_DEV_VERSION=11.11.3.6-1
0.00B 2023-11-10 14:55:29 ENV NV_LIBNPP_DEV_PACKAGE=libnpp-dev-11-8=11.8.0.86-1
0.00B 2023-11-10 14:55:29 ENV NV_LIBNPP_DEV_VERSION=11.8.0.86-1
0.00B 2023-11-10 14:55:29 ENV NV_LIBCUSPARSE_DEV_VERSION=11.7.5.86-1
0.00B 2023-11-10 14:55:29 ENV NV_NVML_DEV_VERSION=11.8.86-1
0.00B 2023-11-10 14:55:29 ENV NV_CUDA_CUDART_DEV_VERSION=11.8.89-1
0.00B 2023-11-10 14:55:29 ENV NV_CUDA_LIB_VERSION=11.8.0-1
0.00B 2023-11-10 14:43:29 ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
0.00B 2023-11-10 14:43:29 ENV NVIDIA_PRODUCT_NAME=CUDA
2.53KB 2023-11-10 14:43:29 COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
3.06KB 2023-11-10 14:43:29 COPY entrypoint.d/ /opt/nvidia/entrypoint.d/ # buildkit
258.26KB 2023-11-10 14:43:29 RUN |1 TARGETARCH=amd64 /bin/sh -c apt-mark hold ${NV_LIBCUBLAS_PACKAGE_NAME} ${NV_LIBNCCL_PACKAGE_NAME} # buildkit
2.42GB 2023-11-10 14:43:29 RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends cuda-libraries-11-8=${NV_CUDA_LIB_VERSION} ${NV_LIBNPP_PACKAGE} cuda-nvtx-11-8=${NV_NVTX_VERSION} libcusparse-11-8=${NV_LIBCUSPARSE_VERSION} ${NV_LIBCUBLAS_PACKAGE} ${NV_LIBNCCL_PACKAGE} && rm -rf /var/lib/apt/lists/* # buildkit
0.00B 2023-11-10 14:43:29 LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
0.00B 2023-11-10 14:43:29 ARG TARGETARCH
0.00B 2023-11-10 14:43:29 ENV NV_LIBNCCL_PACKAGE=libnccl2=2.16.2-1+cuda11.8
0.00B 2023-11-10 14:43:29 ENV NCCL_VERSION=2.16.2-1
0.00B 2023-11-10 14:43:29 ENV NV_LIBNCCL_PACKAGE_VERSION=2.16.2-1
0.00B 2023-11-10 14:43:29 ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
0.00B 2023-11-10 14:43:29 ENV NV_LIBCUBLAS_PACKAGE=libcublas-11-8=11.11.3.6-1
0.00B 2023-11-10 14:43:29 ENV NV_LIBCUBLAS_VERSION=11.11.3.6-1
0.00B 2023-11-10 14:43:29 ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-8
0.00B 2023-11-10 14:43:29 ENV NV_LIBCUSPARSE_VERSION=11.7.5.86-1
0.00B 2023-11-10 14:43:29 ENV NV_LIBNPP_PACKAGE=libnpp-11-8=11.8.0.86-1
0.00B 2023-11-10 14:43:29 ENV NV_LIBNPP_VERSION=11.8.0.86-1
0.00B 2023-11-10 14:43:29 ENV NV_NVTX_VERSION=11.8.86-1
0.00B 2023-11-10 14:43:29 ENV NV_CUDA_LIB_VERSION=11.8.0-1
0.00B 2023-11-10 14:37:17 ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
0.00B 2023-11-10 14:37:17 ENV NVIDIA_VISIBLE_DEVICES=all
17.29KB 2023-11-10 14:37:17 COPY NGC-DL-CONTAINER-LICENSE / # buildkit
0.00B 2023-11-10 14:37:17 ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
0.00B 2023-11-10 14:37:17 ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
46.00B 2023-11-10 14:37:17 RUN |1 TARGETARCH=amd64 /bin/sh -c echo "/usr/local/nvidia/lib" >> /etc/ld.so.conf.d/nvidia.conf && echo "/usr/local/nvidia/lib64" >> /etc/ld.so.conf.d/nvidia.conf # buildkit
150.68MB 2023-11-10 14:37:17 RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends cuda-cudart-11-8=${NV_CUDA_CUDART_VERSION} ${NV_CUDA_COMPAT_PACKAGE} && rm -rf /var/lib/apt/lists/* # buildkit
0.00B 2023-11-10 14:37:03 ENV CUDA_VERSION=11.8.0
18.32MB 2023-11-10 14:37:03 RUN |1 TARGETARCH=amd64 /bin/sh -c apt-get update && apt-get install -y --no-install-recommends gnupg2 curl ca-certificates && curl -fsSL https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/${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
0.00B 2023-11-10 14:37:03 LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
0.00B 2023-11-10 14:37:03 ARG TARGETARCH
0.00B 2023-11-10 14:37:03 ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-8
0.00B 2023-11-10 14:37:03 ENV NV_CUDA_CUDART_VERSION=11.8.89-1
0.00B 2023-11-10 14:37:03 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
0.00B 2023-11-10 14:37:03 ENV NVARCH=x86_64
0.00B 2023-10-03 18:45:52 /bin/sh -c #(nop) CMD ["/bin/bash"]
72.79MB 2023-10-03 18:45:51 /bin/sh -c #(nop) ADD file:4809da414c2d478b4d991cbdaa2df457f2b3d07d0ff6cf673f09a66f90833e81 in /
0.00B 2023-10-03 18:45:50 /bin/sh -c #(nop) LABEL org.opencontainers.image.version=20.04
0.00B 2023-10-03 18:45:50 /bin/sh -c #(nop) LABEL org.opencontainers.image.ref.name=ubuntu
0.00B 2023-10-03 18:45:50 /bin/sh -c #(nop) ARG LAUNCHPAD_BUILD_ARCH
0.00B 2023-10-03 18:45:50 /bin/sh -c #(nop) ARG RELEASE

镜像信息 

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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
79