ghcr.io/binary-husky/gpt_academic_with_all_capacity:master linux/amd64

Docker拉取命令 无权限下载?点我修复

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master  ghcr.io/binary-husky/gpt_academic_with_all_capacity:master

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master  ghcr.io/binary-husky/gpt_academic_with_all_capacity:master

Shell快速替换命令

sed -i 's#ghcr.io/binary-husky/gpt_academic_with_all_capacity:master#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master#' deployment.yaml

Ansible快速分发-Docker

#ansible k8s -m shell -a 'docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master  ghcr.io/binary-husky/gpt_academic_with_all_capacity:master'

Ansible快速分发-Containerd

#ansible k8s -m shell -a 'ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master  ghcr.io/binary-husky/gpt_academic_with_all_capacity:master'

镜像构建历史

# 2024-08-05 19:43:51  0.00B 设置默认要执行的命令
CMD ["python3" "-u" "main.py"]
                        
# 2024-08-05 19:43:51  1.90MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3  -c 'from check_proxy import warm_up_modules; warm_up_modules()' # buildkit
                        
# 2024-08-05 19:43:48  304.38MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install nougat-ocr # buildkit
                        
# 2024-08-05 19:43:39  141.67KB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install -r request_llms/requirements_newbing.txt # buildkit
                        
# 2024-08-05 19:43:37  0.00B 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install -r request_llms/requirements_chatglm.txt # buildkit
                        
# 2024-08-05 19:43:36  0.00B 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install -r request_llms/requirements_qwen.txt # buildkit
                        
# 2024-08-05 19:43:35  191.71MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install -r request_llms/requirements_moss.txt # buildkit
                        
# 2024-08-05 19:43:30  265.23MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install -r requirements.txt # buildkit
                        
# 2024-08-05 19:42:24  91.48MB 执行命令并创建新的镜像层
RUN /bin/sh -c git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss # buildkit
                        
# 2024-08-05 19:42:21  0.00B 设置工作目录为/gpt/gpt_academic
WORKDIR /gpt/gpt_academic
                        
# 2024-08-05 19:42:21  5.36MB 执行命令并创建新的镜像层
RUN /bin/sh -c git clone --depth=1 https://github.com/binary-husky/gpt_academic.git # buildkit
                        
# 2024-08-05 19:42:20  0.00B 设置工作目录为/gpt
WORKDIR /gpt
                        
# 2024-08-05 19:42:20  10.31MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git # buildkit
                        
# 2024-08-05 19:42:08  25.69MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install rarfile py7zr # buildkit
                        
# 2024-08-05 19:42:06  1.82GB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install zh_langchain==0.2.1 pypinyin # buildkit
                        
# 2024-08-05 19:41:20  171.85MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install python-docx moviepy pdfminer # buildkit
                        
# 2024-08-05 19:40:58  70.99MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install colorama Markdown pygments pymupdf # buildkit
                        
# 2024-08-05 19:40:55  119.14MB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install openai numpy arxiv rich # buildkit
                        
# 2024-08-05 19:40:49  7.92GB 执行命令并创建新的镜像层
RUN /bin/sh -c python3 -m pip install torch --extra-index-url https://download.pytorch.org/whl/cu113 # buildkit
                        
# 2024-08-05 19:39:28  23.98MB 执行命令并创建新的镜像层
RUN /bin/sh -c curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8 # buildkit
                        
# 2024-08-05 19:39:25  0.00B 设置工作目录为/gpt
WORKDIR /gpt
                        
# 2024-08-05 19:39:25  448.19MB 执行命令并创建新的镜像层
RUN /bin/sh -c apt update && apt install ffmpeg build-essential -y # buildkit
                        
# 2023-09-07 10:57:57  0.00B 
/bin/sh -c #(nop)  ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/texlive/2023/bin/x86_64-linux:/usr/local/texlive/2024/bin/x86_64-linux:/usr/local/texlive/2025/bin/x86_64-linux:/usr/local/texlive/2026/bin/x86_64-linux
                        
# 2023-09-07 10:57:57  195.49MB 
/bin/sh -c apt-get install -y git python python3 python-dev python3-dev --fix-missing
                        
# 2023-09-07 10:56:52  195.40MB 
/bin/sh -c apt-get install -y curl proxychains curl gcc
                        
# 2023-09-07 10:55:31  47.72MB 
/bin/sh -c apt-get update
                        
# 2023-09-07 10:53:18  4.34GB 
/bin/sh -c #(nop) COPY dir:b20e39a090252a9f1b7b8dc626c86ce320875b616bbebd9c36b6a1009dd282a0 in /usr/local/texlive/ 
                        
# 2022-12-15 07:29:50  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
                        
# 2022-12-15 07:29:50  0.00B 设置环境变量 NVIDIA_PRODUCT_NAME
ENV NVIDIA_PRODUCT_NAME=CUDA
                        
# 2022-12-15 07:29:50  2.53KB 复制新文件或目录到容器中
COPY nvidia_entrypoint.sh /opt/nvidia/ # buildkit
                        
# 2022-12-15 07:29:50  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 07:29:49  256.85KB 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 07:29:49  1.74GB 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-11-3=${NV_CUDA_LIB_VERSION}     ${NV_LIBNPP_PACKAGE}     cuda-nvtx-11-3=${NV_NVTX_VERSION}     libcusparse-11-3=${NV_LIBCUSPARSE_VERSION}     ${NV_LIBCUBLAS_PACKAGE}     ${NV_LIBNCCL_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2022-12-15 07:29:49  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-15 07:29:49  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE
ENV NV_LIBNCCL_PACKAGE=libnccl2=2.9.9-1+cuda11.3
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NCCL_VERSION
ENV NCCL_VERSION=2.9.9-1
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_VERSION
ENV NV_LIBNCCL_PACKAGE_VERSION=2.9.9-1
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBNCCL_PACKAGE_NAME
ENV NV_LIBNCCL_PACKAGE_NAME=libnccl2
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE
ENV NV_LIBCUBLAS_PACKAGE=libcublas-11-3=11.5.1.109-1
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBCUBLAS_VERSION
ENV NV_LIBCUBLAS_VERSION=11.5.1.109-1
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBCUBLAS_PACKAGE_NAME
ENV NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-3
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBCUSPARSE_VERSION
ENV NV_LIBCUSPARSE_VERSION=11.6.0.109-1
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBNPP_PACKAGE
ENV NV_LIBNPP_PACKAGE=libnpp-11-3=11.3.3.95-1
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_LIBNPP_VERSION
ENV NV_LIBNPP_VERSION=11.3.3.95-1
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_NVTX_VERSION
ENV NV_NVTX_VERSION=11.3.109-1
                        
# 2022-12-15 07:29:49  0.00B 设置环境变量 NV_CUDA_LIB_VERSION
ENV NV_CUDA_LIB_VERSION=11.3.1-1
                        
# 2022-12-15 07:25:05  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2022-12-15 07:25:05  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2022-12-15 07:25:05  16.05KB 复制新文件或目录到容器中
COPY NGC-DL-CONTAINER-LICENSE / # buildkit
                        
# 2022-12-15 07:25:05  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 07:25:05  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 07:25:05  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 07:25:05  34.23MB 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-11-3=${NV_CUDA_CUDART_VERSION}     ${NV_CUDA_COMPAT_PACKAGE}     && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2022-12-15 07:24:56  0.00B 设置环境变量 CUDA_VERSION
ENV CUDA_VERSION=11.3.1
                        
# 2022-12-15 07:24:56  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 07:24:56  0.00B 添加元数据标签
LABEL maintainer=NVIDIA CORPORATION <cudatools@nvidia.com>
                        
# 2022-12-15 07:24:56  0.00B 定义构建参数
ARG TARGETARCH
                        
# 2022-12-15 07:24:56  0.00B 设置环境变量 NV_CUDA_COMPAT_PACKAGE
ENV NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-3
                        
# 2022-12-15 07:24:56  0.00B 设置环境变量 NV_CUDA_CUDART_VERSION
ENV NV_CUDA_CUDART_VERSION=11.3.109-1
                        
# 2022-12-15 07:24:56  0.00B 设置环境变量 NVIDIA_REQUIRE_CUDA brand driver>
ENV NVIDIA_REQUIRE_CUDA=cuda>=11.3 brand=tesla,driver>=418,driver<419 driver>=450
                        
# 2022-12-15 07:24:56  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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    "Id": "sha256:934b91034593d4f5b1a7e90b1d3a84c3f4cb1746dae6c20e87bd7214a614e394",
    "RepoTags": [
        "ghcr.io/binary-husky/gpt_academic_with_all_capacity:master",
        "swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master"
    ],
    "RepoDigests": [
        "ghcr.io/binary-husky/gpt_academic_with_all_capacity@sha256:7fafbbb370645a664ca9aaa0efb9efd27b3da99900f0f6f9aa8b4dbba953b1ea",
        "swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity@sha256:7fafbbb370645a664ca9aaa0efb9efd27b3da99900f0f6f9aa8b4dbba953b1ea"
    ],
    "Parent": "",
    "Comment": "buildkit.dockerfile.v0",
    "Created": "2024-08-05T11:43:51.53772482Z",
    "Container": "",
    "ContainerConfig": null,
    "DockerVersion": "",
    "Author": "",
    "Config": {
        "Hostname": "",
        "Domainname": "",
        "User": "",
        "AttachStdin": false,
        "AttachStdout": false,
        "AttachStderr": false,
        "Tty": false,
        "OpenStdin": false,
        "StdinOnce": false,
        "Env": [
            "PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/local/texlive/2023/bin/x86_64-linux:/usr/local/texlive/2024/bin/x86_64-linux:/usr/local/texlive/2025/bin/x86_64-linux:/usr/local/texlive/2026/bin/x86_64-linux",
            "NVARCH=x86_64",
            "NVIDIA_REQUIRE_CUDA=cuda\u003e=11.3 brand=tesla,driver\u003e=418,driver\u003c419 driver\u003e=450",
            "NV_CUDA_CUDART_VERSION=11.3.109-1",
            "NV_CUDA_COMPAT_PACKAGE=cuda-compat-11-3",
            "CUDA_VERSION=11.3.1",
            "LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64",
            "NVIDIA_VISIBLE_DEVICES=all",
            "NVIDIA_DRIVER_CAPABILITIES=compute,utility",
            "NV_CUDA_LIB_VERSION=11.3.1-1",
            "NV_NVTX_VERSION=11.3.109-1",
            "NV_LIBNPP_VERSION=11.3.3.95-1",
            "NV_LIBNPP_PACKAGE=libnpp-11-3=11.3.3.95-1",
            "NV_LIBCUSPARSE_VERSION=11.6.0.109-1",
            "NV_LIBCUBLAS_PACKAGE_NAME=libcublas-11-3",
            "NV_LIBCUBLAS_VERSION=11.5.1.109-1",
            "NV_LIBCUBLAS_PACKAGE=libcublas-11-3=11.5.1.109-1",
            "NV_LIBNCCL_PACKAGE_NAME=libnccl2",
            "NV_LIBNCCL_PACKAGE_VERSION=2.9.9-1",
            "NCCL_VERSION=2.9.9-1",
            "NV_LIBNCCL_PACKAGE=libnccl2=2.9.9-1+cuda11.3",
            "NVIDIA_PRODUCT_NAME=CUDA"
        ],
        "Cmd": [
            "python3",
            "-u",
            "main.py"
        ],
        "ArgsEscaped": true,
        "Image": "",
        "Volumes": null,
        "WorkingDir": "/gpt/gpt_academic",
        "Entrypoint": [
            "/opt/nvidia/nvidia_entrypoint.sh"
        ],
        "OnBuild": null,
        "Labels": {
            "maintainer": "NVIDIA CORPORATION \u003ccudatools@nvidia.com\u003e",
            "org.opencontainers.image.created": "2024-08-05T11:37:32.644Z",
            "org.opencontainers.image.description": "为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮\u0026函数插件，支持Python和C++等项目剖析\u0026自译解功能，PDF/LaTex论文翻译\u0026总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。",
            "org.opencontainers.image.licenses": "GPL-3.0",
            "org.opencontainers.image.revision": "6fe5f6ee6e42b9f5df212bab682d38bcf479c225",
            "org.opencontainers.image.source": "https://github.com/binary-husky/gpt_academic",
            "org.opencontainers.image.title": "gpt_academic",
            "org.opencontainers.image.url": "https://github.com/binary-husky/gpt_academic",
            "org.opencontainers.image.version": "master"
        }
    },
    "Architecture": "amd64",
    "Os": "linux",
    "Size": 18117622315,
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