• cuda apt install


    Hardware requirements

    The following GPU-enabled devices are supported:

    • NVIDIA® GPU card with CUDA® architectures 3.5, 5.0, 6.0, 7.0, 7.5, 8.0 and higher than 8.0. See the list of CUDA®-enabled GPU cards.
    • For GPUs with unsupported CUDA® architectures, or to avoid JIT compilation from PTX, or to use different versions of the NVIDIA® libraries, see the Linux build from source guide.
    • Packages do not contain PTX code except for the latest supported CUDA® architecture; therefore, TensorFlow fails to load on older GPUs when CUDA_FORCE_PTX_JIT=1 is set. (See Application Compatibility for details.)

    Note: The error message "Status: device kernel image is invalid" indicates that the TensorFlow package does not contain PTX for your architecture. You can enable compute capabilities by building TensorFlow from source.

    Software requirements

    The following NVIDIA® software must be installed on your system:

    Linux setup

    The apt instructions below are the easiest way to install the required NVIDIA software on Ubuntu. However, if building TensorFlow from source, manually install the software requirements listed above, and consider using a -devel TensorFlow Docker image as a base.

    Install CUPTI which ships with the CUDA® Toolkit. Append its installation directory to the $LD_LIBRARY_PATH environmental variable:

     
    export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/extras/CUPTI/lib64
    

    Install CUDA with apt

    This section shows how to install CUDA® 10 (TensorFlow >= 1.13.0) on Ubuntu 16.04 and 18.04. These instructions may work for other Debian-based distros.

    Caution: Secure Boot complicates installation of the NVIDIA driver and is beyond the scope of these instructions.

    Ubuntu 18.04 (CUDA 11.0)

     
    # Add NVIDIA package repositories
    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
    sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
    sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
    sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
    sudo apt-get update

    wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb


    sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
    sudo apt-get update

    # Install NVIDIA driver
    sudo apt-get install --no-install-recommends nvidia-driver-450
    # Reboot. Check that GPUs are visible using the command: nvidia-smi

    wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
    sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
    sudo apt-get update

    # Install development and runtime libraries (~4GB)
    sudo apt-get install --no-install-recommends
        cuda-11-0
        libcudnn8=8.0.4.30-1+cuda11.0  
        libcudnn8-dev=8.0.4.30-1+cuda11.0


    # Install TensorRT. Requires that libcudnn8 is installed above.
    sudo apt-get install -y --no-install-recommends libnvinfer7=7.1.3-1+cuda11.0
        libnvinfer-dev=7.1.3-1+cuda11.0
        libnvinfer-plugin7=7.1.3-1+cuda11.0

    Ubuntu 16.04 (CUDA 11.0)

     
    # Add NVIDIA package repositories
    # Add HTTPS support for apt-key
    sudo apt-get install gnupg-curl
    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-ubuntu1604.pin
    sudo mv cuda-ubuntu1604.pin /etc/apt/preferences.d/cuda-repository-pin-600
    sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
    sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/ /"
    sudo apt-get update
    wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
    sudo apt install ./nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64.deb
    sudo apt-get update
    wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1604/x86_64/libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
    sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
    sudo apt-get update

    # Install NVIDIA driver
    # Issue with driver install requires creating /usr/lib/nvidia
    sudo mkdir /usr/lib/nvidia
    sudo apt-get install --no-install-recommends nvidia-driver-450
    # Reboot. Check that GPUs are visible using the command: nvidia-smi

    # Install development and runtime libraries (~4GB)
    sudo apt-get install --no-install-recommends
        cuda-11-0
        libcudnn8=8.0.4.30-1+cuda11.0  
        libcudnn8-dev=8.0.4.30-1+cuda11.0


    # Install TensorRT. Requires that libcudnn7 is installed above.
    sudo apt-get install -y --no-install-recommends
        libnvinfer7=7.1.3-1+cuda11.0
        libnvinfer-dev=7.1.3-1+cuda11.0
        libnvinfer-plugin7=7.1.3-1+cuda11.0
        libnvinfer-plugin-dev=7.1.3-1+cuda11.0
  • 相关阅读:
    html固定宽度下拉框内容显示不全问题解决方法
    ORA-00918: column ambiguously defined
    LigerUI可编辑表格左下角出现白色小方块遮罩层问题解决办法
    Lombok简化Java代码的好工具
    PLSQL Developer连接不上64位Oracle 10g的解决办法
    Windows 2008安装Oracle10g提示操作系统版本检查未通过
    Windows2008安装WebSphere 6.1提示此安装程序不能在图形方式中运行
    LigerUI树节点选中之后节点背景太短
    javapms部署之后首页不能正常显示问题
    Oracle触发器给表自身的字段重新赋值出现ORA-04091异常
  • 原文地址:https://www.cnblogs.com/klausage/p/14435274.html
Copyright © 2020-2023  润新知