摘要

基本按照官网来就行，但是也可以一看。

前情提要

Ubuntu下Python3.8.2安装虚拟环境virtualenv的出错与处理。Python3.8.2 install virtualenv wrong and solve.

一、安装CUDA 10.1

我的配置是GTX 1060MQ，仔细对比过，应该是属于1060吧。

1.1 源命令

按照官网GPU 支持的 “ 使用 apt 安装 CUDA” 一节的源命令：
Ubuntu 18.04 (CUDA 10.1)：

    # Add NVIDIA package repositories
    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
    sudo dpkg -i cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
    sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
    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-418
    # 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-10-1 \
        libcudnn7=7.6.4.38-1+cuda10.1  \
        libcudnn7-dev=7.6.4.38-1+cuda10.1
    

    # Install TensorRT. Requires that libcudnn7 is installed above.
    sudo apt-get install -y --no-install-recommends libnvinfer6=6.0.1-1+cuda10.1 \
        libnvinfer-dev=6.0.1-1+cuda10.1 \
        libnvinfer-plugin6=6.0.1-1+cuda10.1

Ubuntu 16.04 (CUDA 10.1)：

    # 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-repo-ubuntu1604_10.1.243-1_amd64.deb
    sudo dpkg -i cuda-repo-ubuntu1604_10.1.243-1_amd64.deb
    sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
    sudo apt-get update
    wget http://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

    # 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-418
    # 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-10-1 \
        libcudnn7=7.6.4.38-1+cuda10.1  \
        libcudnn7-dev=7.6.4.38-1+cuda10.1
    

    # Install TensorRT. Requires that libcudnn7 is installed above.
    sudo apt-get install -y --no-install-recommends \
        libnvinfer6=6.0.1-1+cuda10.1 \
        libnvinfer-dev=6.0.1-1+cuda10.1 \
        libnvinfer-plugin6=6.0.1-1+cuda10.1

Ubuntu 16.04（CUDA 9.0，TensorFlow 1.13.0 以下版本）：

    # Add NVIDIA package repository
    sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
    wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
    sudo apt install ./cuda-repo-ubuntu1604_9.1.85-1_amd64.deb
    wget http://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 update

    # Install the NVIDIA driver
    # Issue with driver install requires creating /usr/lib/nvidia
    sudo mkdir /usr/lib/nvidia
    sudo apt-get install --no-install-recommends nvidia-410
    # Reboot. Check that GPUs are visible using the command: nvidia-smi

    # Install CUDA and tools. Include optional NCCL 2.x
    sudo apt install cuda9.0 cuda-cublas-9-0 cuda-cufft-9-0 cuda-curand-9-0 \
        cuda-cusolver-9-0 cuda-cusparse-9-0 libcudnn7=7.2.1.38-1+cuda9.0 \
        libnccl2=2.2.13-1+cuda9.0 cuda-command-line-tools-9-0

    # Optional: Install the TensorRT runtime (must be after CUDA install)
    sudo apt update
    sudo apt install libnvinfer4=4.1.2-1+cuda9.0

1.2 安装

直接根据自己的系统和需要安装的源代码建立一个.sh文件进行安装，我是在虚拟环境当中进行安装的：

#激活虚拟环境，不是完全必要
source ./virPy/bin/activate 

#新建一个文件夹及文件存放上面选择的命令，以及方便管理下载的包
mkdir cuda_install
subl install.sh     #建立sh文件，并将1.1中复制的命令粘贴进去

#安装
sudo sh intall.sh

然后等待执行完以后reboot，使用nvidia-smi检查安装情况：
输入：

nvidia-smi

输出如下则安装成功：

Sun Apr 19 05:50:40 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.87.01    Driver Version: 418.87.01    CUDA Version: N/A      |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 106...  Off  | 00000000:01:00.0 Off |                  N/A |
| N/A   60C    P0    25W /  N/A |    206MiB /  6078MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1185      G   /usr/lib/xorg/Xorg                           162MiB |
|    0      2060      G   compiz                                        42MiB |
+-----------------------------------------------------------------------------+

二、安装tensorflow

直接执行：

pip install tensorflow

全文完