ghcr.io/binary-husky/gpt_academic_with_all_capacity:master linux/amd64
ghcr.io/binary-husky/gpt_academic_with_all_capacity:master - 国内下载镜像源 浏览次数:278GPT Academic with All Capacity
这是一款基于 GPT-3 的学术语言模型容器镜像,具备所有能力,用于处理各种学术任务,如文本生成、摘要、翻译等。源镜像 | ghcr.io/binary-husky/gpt_academic_with_all_capacity:master |
国内镜像 | swr.cn-north-4.myhuaweicloud.com/ddn-k8s/ghcr.io/binary-husky/gpt_academic_with_all_capacity:master |
镜像ID | sha256:934b91034593d4f5b1a7e90b1d3a84c3f4cb1746dae6c20e87bd7214a614e394 |
镜像TAG | master |
大小 | 18.12GB |
镜像源 | ghcr.io |
CMD | python3 -u main.py |
启动入口 | /opt/nvidia/nvidia_entrypoint.sh |
工作目录 | /gpt/gpt_academic |
OS/平台 | linux/amd64 |
浏览量 | 278 次 |
贡献者 | |
镜像创建 | 2024-08-05T11:43:51.53772482Z |
同步时间 | 2024-08-10 23:03 |
更新时间 | 2025-01-06 21:10 |
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
{
"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"
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"RepoDigests": [
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"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",
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],
"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"
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},
"Architecture": "amd64",
"Os": "linux",
"Size": 18117622315,
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}
更多版本
ghcr.io/binary-husky/gpt_academic_with_all_capacity:master