docker.io/approachingai/ktransformers:v0.2.3post2-AVX512 linux/amd64

docker.io/approachingai/ktransformers:v0.2.3post2-AVX512 - 国内下载镜像源 浏览次数:189

approachingai/ktransformers 镜像描述

这是一个包含Keras Transformer层的Docker镜像。Keras Transformer提供了一组用于构建Transformer模型的预构建层,方便开发者使用Keras构建各种Transformer架构,例如BERT、GPT等。使用此镜像可以方便地进行Transformer模型的训练和推理,无需手动安装和配置相关的依赖项。

源镜像 docker.io/approachingai/ktransformers:v0.2.3post2-AVX512
国内镜像 swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512
镜像ID sha256:3a13d41cc6a52e2d7a986e74c097dabdc401b7012fc0eb39181f22f13d5c4f18
镜像TAG v0.2.3post2-AVX512
大小 14.14GB
镜像源 docker.io
项目信息 Docker-Hub主页 🚀项目TAG 🚀
CMD
启动入口 tail -f /dev/null
工作目录 /workspace
OS/平台 linux/amd64
浏览量 189 次
贡献者
镜像创建 2025-03-15T17:52:32.12642889Z
同步时间 2025-03-19 15:27
更新时间 2025-05-31 00:51
环境变量
PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/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 PYTORCH_VERSION=2.5.1 CUDA_HOME=/usr/local/cuda
镜像标签
nvidia_driver: com.nvidia.volumes.needed NVIDIA CORPORATION <cudatools@nvidia.com>: maintainer ubuntu: org.opencontainers.image.ref.name 22.04: org.opencontainers.image.version

Docker拉取命令

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512  docker.io/approachingai/ktransformers:v0.2.3post2-AVX512

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512  docker.io/approachingai/ktransformers:v0.2.3post2-AVX512

Shell快速替换命令

sed -i 's#approachingai/ktransformers:v0.2.3post2-AVX512#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512#' 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/approachingai/ktransformers:v0.2.3post2-AVX512 && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512  docker.io/approachingai/ktransformers:v0.2.3post2-AVX512'

Ansible快速分发-Containerd

#ansible k8s -m shell -a 'ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512 && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512  docker.io/approachingai/ktransformers:v0.2.3post2-AVX512'

镜像构建历史


# 2025-03-16 01:52:32  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["tail" "-f" "/dev/null"]
                        
# 2025-03-16 01:52:32  1.28GB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c apt update -y &&  apt install -y  --no-install-recommends \
    git \
    wget \
    vim \
    gcc \
    g++ \
    cmake && 
rm -rf /var/lib/apt/lists/* &&
cd ktransformers &&
git submodule init &&
git submodule update &&
pip install --upgrade pip &&
pip install ninja pyproject numpy cpufeature &&
pip install flash-attn &&
CPU_INSTRUCT=${CPU_INSTRUCT}  KTRANSFORMERS_FORCE_BUILD=TRUE TORCH_CUDA_ARCH_LIST="8.0;8.6;8.7;8.9;9.0+PTX" pip install . --no-build-isolation --verbose &&
pip cache purge &&
cp /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /opt/conda/lib/
 # buildkit
                        
# 2025-03-16 01:09:28  23.83MB 复制新文件或目录到容器中
COPY /home/ktransformers /workspace/ktransformers # buildkit
                        
# 2025-03-16 01:09:28  0.00B 设置环境变量 CUDA_HOME
ENV CUDA_HOME=/usr/local/cuda
                        
# 2025-03-16 01:09:28  0.00B 设置工作目录为/workspace
WORKDIR /workspace
                        
# 2025-03-16 01:09:28  0.00B 定义构建参数
ARG CPU_INSTRUCT=AVX512
                        
# 2024-10-30 02:08:14  0.00B 设置工作目录为/workspace
WORKDIR /workspace
                        
# 2024-10-30 02:08:14  0.00B 设置环境变量 PYTORCH_VERSION
ENV PYTORCH_VERSION=2.5.1
                        
# 2024-10-30 02:08:14  0.00B 设置环境变量 PATH
ENV PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2024-10-30 02:08:14  0.00B 设置环境变量 LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
                        
# 2024-10-30 02:08:14  0.00B 设置环境变量 NVIDIA_DRIVER_CAPABILITIES
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
                        
# 2024-10-30 02:08:14  0.00B 设置环境变量 NVIDIA_VISIBLE_DEVICES
ENV NVIDIA_VISIBLE_DEVICES=all
                        
# 2024-10-30 02:08:14  0.00B 设置环境变量 PATH
ENV PATH=/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
                        
# 2024-10-30 02:08:14  0.00B 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.5.1 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=12.1.1 /bin/sh -c if test -n "${TRITON_VERSION}" -a "${TARGETPLATFORM}" != "linux/arm64"; then         DEBIAN_FRONTEND=noninteractive apt install -y --no-install-recommends gcc;         rm -rf /var/lib/apt/lists/*;     fi # buildkit
                        
# 2024-10-30 02:08:14  5.80GB 复制新文件或目录到容器中
COPY /opt/conda /opt/conda # buildkit
                        
# 2024-10-30 02:04:07  4.92MB 执行命令并创建新的镜像层
RUN |4 PYTORCH_VERSION=2.5.1 TRITON_VERSION= TARGETPLATFORM=linux/amd64 CUDA_VERSION=12.1.1 /bin/sh -c apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends         ca-certificates         libjpeg-dev         libpng-dev         && rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2024-10-30 02:04:07  0.00B 添加元数据标签
LABEL com.nvidia.volumes.needed=nvidia_driver
                        
# 2024-10-30 02:04:07  0.00B 定义构建参数
ARG CUDA_VERSION=12.1.1
                        
# 2024-10-30 02:04:07  0.00B 定义构建参数
ARG TARGETPLATFORM=linux/amd64
                        
# 2024-10-30 02:04:07  0.00B 定义构建参数
ARG TRITON_VERSION=
                        
# 2024-10-30 02:04:07  0.00B 定义构建参数
ARG PYTORCH_VERSION=2.5.1
                        
# 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

{
    "Id": "sha256:3a13d41cc6a52e2d7a986e74c097dabdc401b7012fc0eb39181f22f13d5c4f18",
    "RepoTags": [
        "approachingai/ktransformers:v0.2.3post2-AVX512",
        "swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.2.3post2-AVX512"
    ],
    "RepoDigests": [
        "approachingai/ktransformers@sha256:6b668321de38c5dec964a4b92f485498b5b43728bff939eda92ae63e3a8dd897",
        "swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers@sha256:ac03e5d6d9aa83125a41059e7ae9766a0ad6688fb7bd2c1bb1159d96a21a17b0"
    ],
    "Parent": "",
    "Comment": "buildkit.dockerfile.v0",
    "Created": "2025-03-15T17:52:32.12642889Z",
    "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:/opt/conda/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=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",
            "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",
            "PYTORCH_VERSION=2.5.1",
            "CUDA_HOME=/usr/local/cuda"
        ],
        "Cmd": null,
        "Image": "",
        "Volumes": null,
        "WorkingDir": "/workspace",
        "Entrypoint": [
            "tail",
            "-f",
            "/dev/null"
        ],
        "OnBuild": null,
        "Labels": {
            "com.nvidia.volumes.needed": "nvidia_driver",
            "maintainer": "NVIDIA CORPORATION \u003ccudatools@nvidia.com\u003e",
            "org.opencontainers.image.ref.name": "ubuntu",
            "org.opencontainers.image.version": "22.04"
        }
    },
    "Architecture": "amd64",
    "Os": "linux",
    "Size": 14137835464,
    "GraphDriver": {
        "Data": {
            "LowerDir": "/var/lib/docker/overlay2/8d519f550713db0e6e12ae4a008835a9165d688076d89864c5bf698d198ed296/diff:/var/lib/docker/overlay2/9fb3ab1ef19dc5c84fc5e67c95f55392ea586a16ca2c26c8b5336fc4b71d578c/diff:/var/lib/docker/overlay2/7484c13991c72be80455a759c66622bccf7e2377865b07b6c015c7e3442d30a7/diff:/var/lib/docker/overlay2/69eb983ef8348704b4c2590013481d1f6f20e767630ffb7b0920d0a443185e6f/diff:/var/lib/docker/overlay2/b75f8db6800c41816fcc777b58c5683efa7c9ca31d28a41516fb6822a9ac2058/diff:/var/lib/docker/overlay2/27dcd45c5eb0aeab03065a66b8bc1f7cf99ff3bcbda740040a52f5b56b95f4a9/diff:/var/lib/docker/overlay2/16656859196f01a90926db06ab7a9d9cbce4f628fa7aff0b448a6c308d385112/diff:/var/lib/docker/overlay2/5210552f0ec5a2d65f6917e13da344f714a21d438a783bc7d1c9a16af914a184/diff:/var/lib/docker/overlay2/be84fd046e39347d46e4b19d9ade2d51d1d70a40309bf0d63332c77ffc1f2679/diff:/var/lib/docker/overlay2/1ef02b89f822d9d60aa56a95101e13f21f422b3558bf567b4df864493d31a4da/diff:/var/lib/docker/overlay2/f2b8a1741c2536d6b61852d9568bf817fd9a63ac9fb8df11b7a19b0311f91954/diff:/var/lib/docker/overlay2/d20e9b5612f25be51b5fb44e91e45f61eb67c9d70660e670db49ee1998fb1c2a/diff:/var/lib/docker/overlay2/2c7cec8897aabb0388b4470488bf4951e8d7ad022433c73c763425f5d5813956/diff:/var/lib/docker/overlay2/d5234d942bac7845ea7dd140d4747fdec66c2c50467795afa6f2e577139027fd/diff:/var/lib/docker/overlay2/65edf56aea7268f113e7a860fa1cd5ba2b0ee61494b5b888524b08f97f620da5/diff:/var/lib/docker/overlay2/88cc313884619d0e223457ae9dff69f41729fa11bbd330fa57b65d5955229f89/diff:/var/lib/docker/overlay2/f2905627b4505cda033dd62b5a5dc1676edda5a6e1bda7cd6e6e2048fcf5aee0/diff",
            "MergedDir": "/var/lib/docker/overlay2/a9c6fcbcf5964bdeca37d0f1ee647fca95f9162358156a1d494325d2b0e0ca8a/merged",
            "UpperDir": "/var/lib/docker/overlay2/a9c6fcbcf5964bdeca37d0f1ee647fca95f9162358156a1d494325d2b0e0ca8a/diff",
            "WorkDir": "/var/lib/docker/overlay2/a9c6fcbcf5964bdeca37d0f1ee647fca95f9162358156a1d494325d2b0e0ca8a/work"
        },
        "Name": "overlay2"
    },
    "RootFS": {
        "Type": "layers",
        "Layers": [
            "sha256:256d88da41857db513b95b50ba9a9b28491b58c954e25477d5dad8abb465430b",
            "sha256:566cd9dd29d693cf0360da8a73391b843bb6ac8f11b4148acf69c4dc79fa87c5",
            "sha256:6ec2b659c9ab00e2b0fc0acd056577e609cc28649650ec7068b81686f6d1a996",
            "sha256:8afeff4e91d72f3de9232ffc0803f70236e316c27b23ee003e6d47fbfcb6e1c4",
            "sha256:bea30ebbe84377ed36503599c2087cd6bda6f4c96cb59525d238d4a00cf902d3",
            "sha256:b15b1df4adac82b2b46124c743a32d5e982cb6b5ee8a3c04949f809abf8962c9",
            "sha256:83ecbf43a888c43f43b0cd9ec7cf551770790c7aeab17f9536b8820db2c5d45d",
            "sha256:83687aeafbbf74a164a51590ffa36c46e9c41ce4ba3eae9daba1d381c64e5f4b",
            "sha256:3416903c2cc4c9f83472b397741f30365f53543862b03ff5727b42b1a2f938cb",
            "sha256:24e1e08aaa60ea10f478c1b68d9444b8ea74bff76e2547712984b5136e79018e",
            "sha256:7aee75a70a2ff35d4990fab501a025afa498f416cb726ace747ccd7fad6500d4",
            "sha256:c787acbf722215596c749d6bcca5f1895643a86d035321997d234f3706221e95",
            "sha256:2c170fff705fc5cb91740815d5279ea5f8235ca7cf2ec03d93b03456b6b2fcc5",
            "sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef",
            "sha256:ffba0182d4cd71765a8941f2f8ba0a5c029d22dc959b030dbc65b3f27c398562",
            "sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef",
            "sha256:16bbf00760939d45b9eca612da9a4764eb5f331c4b44bb3e4260bb32b98635c8",
            "sha256:72778eab9e44cdb0393c24731b610af41f3f0a37ecbf8183d8e9008b73ef5177"
        ]
    },
    "Metadata": {
        "LastTagTime": "2025-03-19T15:25:44.54440981+08:00"
    }
}

更多版本

docker.io/approachingai/ktransformers:0.2.1

linux/amd64 docker.io18.50GB2025-02-19 00:37
560

docker.io/approachingai/ktransformers:v0.2.2rc1-AVX512

linux/amd64 docker.io18.37GB2025-02-26 16:11
221

docker.io/approachingai/ktransformers:v0.2.3post1-AVX2

linux/amd64 docker.io14.14GB2025-03-09 08:52
212

docker.io/approachingai/ktransformers:v0.2.3post2-AVX512

linux/amd64 docker.io14.14GB2025-03-19 15:27
188

docker.io/approachingai/ktransformers:v0.2.4post1-AVX512

linux/amd64 docker.io15.50GB2025-04-20 00:21
168

docker.io/approachingai/ktransformers:v0.3.1-AVX2

linux/amd64 docker.io15.32GB2025-05-31 01:44
8

docker.io/approachingai/ktransformers:v0.3.1-AVX512

linux/amd64 docker.io15.32GB2025-05-31 01:49
8