docker.io/approachingai/ktransformers:v0.3.1-AVX512 linux/amd64

docker.io/approachingai/ktransformers:v0.3.1-AVX512 - 国内下载镜像源浏览次数:18

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:v0.3.1-AVX512
国内镜像	swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.3.1-AVX512
镜像ID	sha256:83d2ded2afdae3ba98deff8051697e000d0db0f52e68eb66ba0f3da066932d47
镜像TAG	v0.3.1-AVX512
大小	15.32GB
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/ktransformers
OS/平台	linux/amd64
浏览量	18 次
贡献者
镜像创建	2025-05-17T10:15:33.506320223Z
同步时间	2025-05-31 01:49
更新时间	2025-06-01 11:06

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

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无权限下载?点我修复生成Docker Run命令

Docker拉取命令

docker pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.3.1-AVX512
docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.3.1-AVX512  docker.io/approachingai/ktransformers:v0.3.1-AVX512

Containerd拉取命令

ctr images pull swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.3.1-AVX512
ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.3.1-AVX512  docker.io/approachingai/ktransformers:v0.3.1-AVX512

Shell快速替换命令

sed -i 's#approachingai/ktransformers:v0.3.1-AVX512#swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.3.1-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:v0.3.1-AVX512 && docker tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.3.1-AVX512  docker.io/approachingai/ktransformers:v0.3.1-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:v0.3.1-AVX512 && ctr images tag  swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/approachingai/ktransformers:v0.3.1-AVX512  docker.io/approachingai/ktransformers:v0.3.1-AVX512'

镜像构建历史


# 2025-05-17 18:15:33  0.00B 配置容器启动时运行的命令
ENTRYPOINT ["tail" "-f" "/dev/null"]
                        
# 2025-05-17 18:15:33  2.26MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c cp /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /opt/conda/lib/ # buildkit
                        
# 2025-05-17 18:15:33  0.00B 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c pip cache purge # buildkit
                        
# 2025-05-17 18:15:32  42.34MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c pip install third_party/custom_flashinfer/ # buildkit
                        
# 2025-05-17 18:15:29  586.74MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c CPU_INSTRUCT=${CPU_INSTRUCT}     USE_BALANCE_SERVE=1     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 # buildkit
                        
# 2025-05-17 16:51:03  800.19MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c pip install flash-attn # buildkit
                        
# 2025-05-17 16:50:50  44.66MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c pip install ninja pyproject numpy cpufeature aiohttp zmq openai # buildkit
                        
# 2025-05-17 16:50:44  13.35MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c pip install --upgrade pip # buildkit
                        
# 2025-05-17 16:50:43  733.22MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c git submodule update --init --recursive # buildkit
                        
# 2025-05-17 16:50:14  0.00B 设置工作目录为/workspace/ktransformers
WORKDIR /workspace/ktransformers
                        
# 2025-05-17 16:50:14  0.00B 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c rm -rf /var/lib/apt/lists/* # buildkit
                        
# 2025-05-17 16:50:14  26.49MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c git clone https://github.com/kvcache-ai/ktransformers.git # buildkit
                        
# 2025-05-17 16:50:13  167.56MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c apt install -y --no-install-recommends     libtbb-dev     libssl-dev     libcurl4-openssl-dev     libaio1     libaio-dev     libfmt-dev     libgflags-dev     zlib1g-dev     patchelf     git     wget     vim     gcc     g++     cmake # buildkit
                        
# 2025-05-17 16:50:06  65.49MB 执行命令并创建新的镜像层
RUN |1 CPU_INSTRUCT=AVX512 /bin/sh -c apt update -y # buildkit
                        
# 2025-05-17 16:50:02  0.00B 设置环境变量 CUDA_HOME
ENV CUDA_HOME=/usr/local/cuda
                        
# 2025-05-17 16:50:02  0.00B 设置工作目录为/workspace
WORKDIR /workspace
                        
# 2025-05-17 16:50:02  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

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    "Created": "2025-05-17T10:15:33.506320223Z",
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            "NV_LIBNCCL_PACKAGE_VERSION=2.17.1-1",
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            "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",
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        "Cmd": null,
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        "Volumes": null,
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更多版本

docker.io/approachingai/ktransformers:0.2.1

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