tensorrt 安裝

首先要先安裝cuda和cudnn,這個在這就不寫了，網上可以搜到很多相關的文章。

https://blog.csdn.net/qq_42393859/article/details/85294126

這裏要注意的是，cuda和cudnn的版本，根據你tensorrt版本需求安裝。

nvidia官網上有相關文件，不過要先登錄帳號，我cuda是10.0.130版本，cudnn是配套的７.5版本，加起來２個多Ｇ。

tensort各個版本的下載位置：https://developer.nvidia.com/nvidia-tensorrt-download，我這裏下載的是５.1.5GA tar包版本，

Tar File Install Packages For Linux x86　TensorRT 5.1.5.0 GA for Ubuntu 16.04 and CUDA 10.0 tar package

這裏根據你的環境選擇自己對應的版本。

各個版本的安裝文檔鏈接：https://docs.nvidia.com/deeplearning/sdk/tensorrt-archived/index.html

例５.1.x.x版本  tar包文件的安裝

 Tar File Installation
Note: Before issuing the following commands, you'll need to replace 5.1.x.x with your specific TensorRT version. The following commands are examples.

    Install the following dependencies, if not already present:
        Install the CUDA Toolkit 9.0, 10.0 or 10.1
        cuDNN 7.5.0
        Python 2 or Python 3 (Optional)
    Download the TensorRT tar file that matches the Linux distribution you are using.
    Choose where you want to install TensorRT. This tar file will install everything into a subdirectory called TensorRT-5.1.x.x.
    Unpack the tar file.

    $ tar xzvf TensorRT-5.1.x.x.<os>.<arch>-gnu.cuda-x.x.cudnn7.x.tar.gz

    Where:
        5.1.x.x is your TensorRT version
        <os> is Ubuntu-14.04.5, Ubuntu-16.04.5, Ubuntu-18.04.2, Red-Hat, or CentOS-Linux
        <arch> is x86_64 or ppc64le
        cuda-x.x is CUDA version 9.0, 10.0, or 10.1
        cudnn7.x is cuDNN version 7.5
    This directory will have sub-directories like lib, include, data, etc…

    $ ls TensorRT-5.1.x.x
    bin  data  doc  graphsurgeon  include  lib  python  samples  targets  TensorRT-Release-Notes.pdf  uff

    Add the absolute path to the TensorRTlib directory to the environment variable LD_LIBRARY_PATH:

    $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<eg:TensorRT-5.1.x.x/lib>

    Install the Python TensorRT wheel file.

    $ cd TensorRT-5.1.x.x/python

    If using Python 2.7:

    $ sudo pip2 install tensorrt-5.1.x.x-cp27-none-linux_x86_64.whl

    If using Python 3.x:

    $ sudo pip3 install tensorrt-5.1.x.x-cp3x-none-linux_x86_64.whl

    Install the Python UFF wheel file. This is only required if you plan to use TensorRT with TensorFlow.

    $ cd TensorRT-5.1.x.x/uff

    If using Python 2.7:

    $ sudo pip2 install uff-0.6.3-py2.py3-none-any.whl

    If using Python 3.x:

    $ sudo pip3 install uff-0.6.3-py2.py3-none-any.whl

    In either case:

    $ which convert-to-uff
    /usr/local/bin/convert-to-uff

    Install the Python graphsurgeon wheel file.

    $ cd TensorRT-5.1.x.x/graphsurgeon

    If using Python 2.7:

    $ sudo pip2 install graphsurgeon-0.4.1-py2.py3-none-any.whl

    If using Python 3.x:

    $ sudo pip3 install graphsurgeon-0.4.1-py2.py3-none-any.whl

    Verify the installation:
        Ensure that the installed files are located in the correct directories. For example, run the tree -d command to check whether all supported installed files are in place in the lib, include, data, etc… directories.
        Build and run one of the shipped samples, for example, sampleMNIST in the installed directory. You should be able to compile and execute the sample without additional settings. For more information about sampleMNSIT, see the "Hello World" For TensorRT sample.
        The Python samples are in the samples/python directory.