无法加载动态库“libnvere.so.6”

Question

我试图正常导入TensorFlow python包，但出现以下错误：

以下是上述终端图像中的文本：

2020-02-23 19:01:06.163940: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory
2020-02-23 19:01:06.164019: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory
2020-02-23 19:01:06.164030: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
<module 'tensorflow_core._api.v2.version' from '/home/saman/miniconda3/envs/testconda/lib/python3.7/site-packages/tensorflow_core/_api/v2/version/__init__.py'

Answer 1

我得到这个警告是因为（意外）更新了libvnifer6包。它被更新为6.0.1-1+cuda10.2，而原始安装使用了6.0.1-1+cuda10.1

在我卸载了引用cuda10.2的包并重新运行之后

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

这一警告消失了

Answer 2

这些消息中的大多数是警告，而不是错误。它们只是意味着没有安装使用Nvidia GPU的库，但使用Tensorflow不需要任何Nvidia GPU，因此不需要这些库。jakub的评论告诉我们如何关闭警告：

export TF_CPP_MIN_LOG_LEVEL="2"

但是，我也在没有Nvidia的情况下运行Tensorflow，还有一条消息是错误，而不是警告：

2020-04-10 10:04:13.365696: E tensorflow/stream_executor/cuda/cuda_driver.cc:351] failed call to cuInit: UNKNOWN ERROR (303)

它应该是无关的，因为它也指cuda，这是Nvidia。但这似乎不是一个致命的错误

Answer 3

这是一个警告，不是错误。您仍然可以使用TensorFlow。共享库libnvinfer和libnvinfer_plugin是可选的，只有在使用nvidia的TensorRT功能时才需要

TensorFlow's installation instructions列出GPU依赖项：

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

• NVIDIA® GPU drivers —CUDA 10.1 requires 418.x or higher.
• CUDA® Toolkit —TensorFlow supports CUDA 10.1 (TensorFlow >= 2.1.0)
• CUPTI ships with the CUDA Toolkit.
• cuDNN SDK (>= 7.6)
• (Optional) TensorRT 6.0 to improve latency and throughput for inference on some models.

您可以使用以下命令（取自TensorFlow documentation）在Ubuntu18.04上安装它们：

# 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-430
# 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