ImportError：libcublas.so.10.0：无法打开共享库文件：没有这样的文件或目录

时间：2019-03-18 14:46:32

标签： tensorflow cuda ubuntu-18.04

我已经在Ubuntu 18.04上安装了Cuda 10.1和cudnn，并且似乎已正确安装为nvcc和nvidia-smi类型，我得到了正确的响应：

    user:~$ nvcc -V
    nvcc: NVIDIA (R) Cuda compiler driver
    Copyright (c) 2005-2019 NVIDIA Corporation
    Built on Fri_Feb__8_19:08:17_PST_2019
    Cuda compilation tools, release 10.1, V10.1.105
    user:~$ nvidia-smi 
    Mon Mar 18 14:36:47 2019       
    +-----------------------------------------------------------------------------+
    | NVIDIA-SMI 418.43       Driver Version: 418.43       CUDA Version: 10.1     |
    |-------------------------------+----------------------+----------------------+
    | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
    | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
    |===============================+======================+======================|
    |   0  Quadro K5200        Off  | 00000000:03:00.0  On |                  Off |
    | 26%   39C    P8    14W / 150W |    225MiB /  8118MiB |      0%      Default |
    +-------------------------------+----------------------+----------------------+

    +-----------------------------------------------------------------------------+
    | Processes:                                                       GPU Memory |
    |  GPU       PID   Type   Process name                             Usage      |
    |=============================================================================|
    |    0      1538      G   /usr/lib/xorg/Xorg                            32MiB |
    |    0      1583      G   /usr/bin/gnome-shell                           5MiB |
    |    0      3008      G   /usr/lib/xorg/Xorg                           100MiB |
    |    0      3120      G   /usr/bin/gnome-shell                          82MiB |
    +-----------------------------------------------------------------------------+

我已经使用以下命令安装了tensorflow： user:~$ sudo pip3 install --upgrade tensorflow-gpu 

The directory '/home/amin/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
The directory '/home/amin/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.
Requirement already up-to-date: tensorflow-gpu in /usr/local/lib/python3.6/dist-packages (1.13.1)
Requirement already satisfied, skipping upgrade: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.0.7)
Requirement already satisfied, skipping upgrade: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (3.6.1)
Requirement already satisfied, skipping upgrade: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.32.3)
Requirement already satisfied, skipping upgrade: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.7.0)
Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.0.9)
Requirement already satisfied, skipping upgrade: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.2.2)
Requirement already satisfied, skipping upgrade: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.1.0)
Requirement already satisfied, skipping upgrade: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.18.0)
Requirement already satisfied, skipping upgrade: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.13.0)
Requirement already satisfied, skipping upgrade: six>=1.10.0 in /usr/lib/python3/dist-packages (from tensorflow-gpu) (1.11.0)
Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/lib/python3/dist-packages (from tensorflow-gpu) (1.13.3)
Requirement already satisfied, skipping upgrade: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (0.7.1)
Requirement already satisfied, skipping upgrade: tensorboard<1.14.0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu) (1.13.1)
Requirement already satisfied, skipping upgrade: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu) (2.9.0)
Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu) (40.6.3)
Requirement already satisfied, skipping upgrade: mock>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow-gpu) (2.0.0)
Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu) (0.14.1)
Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow-gpu) (3.0.1)
Requirement already satisfied, skipping upgrade: pbr>=0.11 in /usr/local/lib/python3.6/dist-packages (from mock>=2.0.0->tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow-gpu) (5.1.1)

但是，当我尝试导入tensorflow时，出现关于libcublas.so.10.0的错误：

    user:~$ python3
    Python 3.6.7 (default, Oct 22 2018, 11:32:17) 
    [GCC 8.2.0] on linux
    Type "help", "copyright", "credits" or "license" for more information.
    >>> import tensorflow as tf
    Traceback (most recent call last):
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in <module>
        from tensorflow.python.pywrap_tensorflow_internal import *
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
        _pywrap_tensorflow_internal = swig_import_helper()
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
        _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
      File "/usr/lib/python3.6/imp.py", line 243, in load_module
        return load_dynamic(name, filename, file)
      File "/usr/lib/python3.6/imp.py", line 343, in load_dynamic
        return _load(spec)
    ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory

    During handling of the above exception, another exception occurred:

    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/__init__.py", line 24, in <module>
        from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/__init__.py", line 49, in <module>
        from tensorflow.python import pywrap_tensorflow
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 74, in <module>
        raise ImportError(msg)
    ImportError: Traceback (most recent call last):
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in <module>
        from tensorflow.python.pywrap_tensorflow_internal import *
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
        _pywrap_tensorflow_internal = swig_import_helper()
      File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
        _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
      File "/usr/lib/python3.6/imp.py", line 243, in load_module
        return load_dynamic(name, filename, file)
      File "/usr/lib/python3.6/imp.py", line 343, in load_dynamic
        return _load(spec)
    ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory


    Failed to load the native TensorFlow runtime.

    See https://www.tensorflow.org/install/errors

    for some common reasons and solutions.  Include the entire stack trace
    above this error message when asking for help.

我缺少什么？以及我该如何解决？

谢谢

13 个答案:

答案 0 :(得分：12)

CUDA 10.1（根据tensorflow文档安装）引发can't find libcublas.so.10.0错误。这些库存在于/usr/local/cuda-10.1/targets/x86_64-linux/lib/中，但名称错误。

有另一篇（丢失的）stackoverflow帖子说这是该软件包的固定依赖项问题，可以通过附加一个cli标志来解决。这似乎并不能解决我的问题。

经过测试的解决方法是修改说明以降级到CUDA 10.0 

# Uninstall packages from tensorflow installation instructions 
sudo apt-get remove cuda-10-1 \
    libcudnn7 \
    libcudnn7-dev \
    libnvinfer6 \
    libnvinfer-dev \
    libnvinfer-plugin6

# WORKS: Downgrade to CUDA-10.0
sudo apt-get install -y --no-install-recommends \
    cuda-10-0 \
    libcudnn7=7.6.4.38-1+cuda10.0  \
    libcudnn7-dev=7.6.4.38-1+cuda10.0;
sudo apt-get install -y --no-install-recommends \
    libnvinfer6=6.0.1-1+cuda10.0 \
    libnvinfer-dev=6.0.1-1+cuda10.0 \
    libnvinfer-plugin6=6.0.1-1+cuda10.0;

升级到CUDA-10.2似乎也遇到同样的问题

# BROKEN: Upgrade to CUDA-10.2 
# use `apt show -a libcudnn7 libnvinfer7` to find 10.2 compatable version numbers
sudo apt-get install -y --no-install-recommends \
    cuda-10-2 \
    libcudnn7=7.6.5.32-1+cuda10.2  \
    libcudnn7-dev=7.6.5.32-1+cuda10.2;
sudo apt-get install -y --no-install-recommends \
    libnvinfer7=7.0.0-1+cuda10.2 \
    libnvinfer-dev=7.0.0-1+cuda10.2 \
    libnvinfer-plugin7=7.0.0-1+cuda10.2;

在Python中测试GPU可见性

python3
>>> import tensorflow as tf
>>> tf.test.is_gpu_available()

关于tensorflow导入的未来警告

https://github.com/tensorflow/tensorflow/issues/30427

两种解决方案：

  • pip3 install tf-nightly-gpu
  • pip3 install "numpy<1.17"

更新：

您还需要正确的张量流版本以与您的CUDA版本匹配

Tensorflow / CUDA版本组合：

  • Tensorflow v2.x不支持CUDA 9（默认为Ubuntu 18.4）
  • Tensorflow v2.1.0与CUDA 10.1一起使用
  • Tensorflow v2.0.0与CUDA 10.0一起使用

查看完整列表：https://www.tensorflow.org/install/source#tested_build_configurations

您可能可能需要重新安装具有与CUDA匹配的命名版本的tensorflow 

pip uninstall tensorflow tensorflow-gpu
pip install tensorflow==2.1.0 tensorflow-gpu==2.1.0

然后将CUDA添加到〜/ .bashrc中的$ PATH和$ LD_LIBRARY_PATH中

〜/ .bashrc 

# CUDA Environment Setup: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#environment-setup
for CUDA_BIN_DIR in `find /usr/local/cuda-*/bin   -maxdepth 0`; do export PATH="$PATH:$CUDA_BIN_DIR"; done;
for CUDA_LIB_DIR in `find /usr/local/cuda-*/lib64 -maxdepth 0`; do export LD_LIBRARY_PATH="${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}$CUDA_LIB_DIR"; done;

export            PATH=`echo $PATH            | tr ':' '\n' | awk '!x[$0]++' | tr '\n' ':' | sed 's/:$//g'` # Deduplicate $PATH
export LD_LIBRARY_PATH=`echo $LD_LIBRARY_PATH | tr ':' '\n' | awk '!x[$0]++' | tr '\n' ':' | sed 's/:$//g'` # Deduplicate $LD_LIBRARY_PATH

答案 1 :(得分：8)

如果使用的是Cuda 10.1（如https://www.tensorflow.org/install/gpu中所述），问题在于libcublas.so.10已从cuda-10.1目录移至cuda-10.2（！）

从以下答案中复制：https://github.com/tensorflow/tensorflow/issues/26182#issuecomment-684993950

... libcublas.so.10位于/usr/local/cuda-10.2/lib64（来自nvidia的惊喜-安装10.1会安装一些10.2的东西），但是只有/ usr / local / cuda位于包含路径中，该路径指向到/usr/local/cuda-10.1。

修复是将其添加到您的包含路径：

export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

注意：已知此修补程序可在Cuda 10.1 V10.1.243中使用（用nvcc -V打印版本）。

答案 2 :(得分：5)

我从以下链接下载了cuda 10.0 CUDA 10.0

然后我使用以下命令安装了它：

sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_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
sudo apt-get install cuda-10-0

然后我通过转到链接为CUDA 10.0安装了cudnn v7.5.0 CUDNN download，您需要使用帐户登录。

，选择正确的版本后，我通过链接CUDNN power link下载了 之后，我为cudnn添加了include和lib文件，如下所示：

sudo cp -P cuda/targets/ppc64le-linux/include/cudnn.h /usr/local/cuda-10.0/include/
sudo cp -P cuda/targets/ppc64le-linux/lib/libcudnn* /usr/local/cuda-10.0/lib64/
sudo chmod a+r /usr/local/cuda-10.0/lib64/libcudnn*

修改.bashrc的lib和cuda 10.0的路径后，如果没有，则需要将它们添加到.bashrc中。

export PATH=/usr/local/cuda-10.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64:${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

在完成所有这些步骤之后，我成功地在python3中导入了tensorflow。

答案 3 :(得分：2)

正如CalderBot所说，您也可以这样做

sudo cp -r /usr/local/cuda-10.2/lib64/libcu* /usr/local/cuda-10.1/lib64 /

答案 4 :(得分：1)

当安装的cuda和tensorflow的版本不兼容时，会发生此错误。我在使用cuda 9运行tensorflow 1.13.0版本时遇到了类似的ImportError。由于我已经在使用pip的虚拟环境中安装了tensorflow，所以我只是卸载了tensorflow 1.13.0并按如下所示安装了tensorflow 1.12.0； 

@Autowire
RestTemplate restTemplate;

现在一切正常。

答案 5 :(得分：1)

问题是由您当前的cuda版本10.1（如我们从图片的右上角看到的）引起的。

从TF官方网站上可以看到，tf和cuda之间的对应关系为：TF website for the chart 

Version                 cuDNN    CUDA
tensorflow-2.1.0         7.6       10.1
tensorflow-2.0.0         7.4       10.0
tensorflow_gpu-1.14.0    7.4       10.0
tensorflow_gpu-1.13.1    7.4       10.0

因此，您可以将tf升级到2.1或通过以下方式降级您的cuda：

conda install cudatoolkit=10.0.130

然后，它也会自动将您的客户满意度降级。

答案 6 :(得分：1)

您的计算机是否支持 CUDA？

在 Linux 中，您可以通过以下方式验证您的系统是否具有支持 CUDA 的 GPU：

$ lspci | grep -i nvidia

如果没有看到任何设置，请在命令行输入 update-pciids（通常在 /sbin 中找到）更新 Linux 维护的 PCI 硬件数据库，然后重新运行之前的 lspci 命令。

在此页面中，您有安装 CUDA 的说明： https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html

如果您的计算机不支持 CUDA，您可以安装另一个 tensorflow 发行版或编译 tensorflow 代码：https://www.tensorflow.org/install/source

答案 7 :(得分：0)

更改我的tensorflow版本解决了我的问题。

检查此问题1 https://github.com/tensorflow/tensorflow/issues/26182）

  

官方tensorflow-gpu二进制文件（通过pip或conda下载的二进制文件）使用cuda 9.0，自TF 1.5起的cudnn 7和cuda 10.0，自TF 1.13起的cudnn 7构建。这些都写在发行说明中。如果使用的是官方二进制文件，则必须使用匹配版本的cuda。

答案 8 :(得分：0)

我有同样的问题。我通过将以下命令添加到“ .bashrc ”文件中来修复该问题。

  

export LD_LIBRARY_PATH = $ LD_LIBRARY_PATH：/usr/local/cuda-10.0/lib64 /

系统配置： 

Ubuntu 16.04 LTS
Tensorflow GPU 2.0beta1
Cuda 10.0
cuDNN 7.6.0 for Cuda 10.0

我使用了conda来配置系统。

答案 9 :(得分：0)

阿明

尝试从tensorflow模型包-https://github.com/tensorflow/models/tree/master/tutorials/image/imagenet

运行imagenet教程时遇到相同的错误 
 python3 classify_image.py
 ...
 2019-07-21 22:29:58.367858: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory
 2019-07-21 22:29:58.367982: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory
 2019-07-21 22:29:58.368112: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory
 2019-07-21 22:29:58.368234: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory
 2019-07-21 22:29:58.368369: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory
 2019-07-21 22:29:58.368498: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory
 2019-07-21 22:29:58.374333: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7

我认为某个地方存在版本不兼容性，并且可能是张量流，但仍然依赖于cuda库提供的旧版本的二进制文件。转到存储二进制文件的地方并创建一个名为10.0但以10.1或库的默认版本为目标的链接，似乎可以解决我的问题。

 # cd /usr/lib/x86_64-linux-gnu
 # ln -s libcudart.so.10.1 libcudart.so.10.0
 # ln -s libcublas.so libcublas.so.10.0
 # ln -s libcufft.so libcufft.so.10.0
 # ln -s libcurand.so libcurand.so.10.0
 # ln -s libcusolver.so libcusolver.so.10.0
 # ln -s libcusparse.so libcusparse.so.10.0

现在我可以成功运行教程

 2019-07-24 21:43:21.172908: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
 2019-07-24 21:43:21.174653: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
 2019-07-24 21:43:21.175826: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
 2019-07-24 21:43:21.182305: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
 2019-07-24 21:43:21.183970: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
 2019-07-24 21:43:21.206796: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
 2019-07-24 21:43:21.210685: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
 2019-07-24 21:43:21.212694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
 2019-07-24 21:43:21.213060: I tensorflow/core/platform/cpu_feature_guard.cc:142]      
 Your CPU supports instructions that this TensorFlow binary was not compiled to use: FMA
 2019-07-24 21:43:21.238541: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3214745000 Hz
 2019-07-24 21:43:21.240096: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x557e2b682ce0 executing computations on platform Host. Devices:
 2019-07-24 21:43:21.240162: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
 2019-07-24 21:43:21.355158: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x557e2b652000 executing computations on platform CUDA. Devices:
 2019-07-24 21:43:21.355234: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): GeForce GTX 1060 6GB, Compute Capability 6.1
 2019-07-24 21:43:21.357074: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
 name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.7715
 pciBusID: 0000:01:00.0
 2019-07-24 21:43:21.357151: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
 2019-07-24 21:43:21.357207: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
 2019-07-24 21:43:21.357245: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
 2019-07-24 21:43:21.357283: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
 2019-07-24 21:43:21.357321: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
 2019-07-24 21:43:21.357358: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
 2019-07-24 21:43:21.357395: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
 2019-07-24 21:43:21.360449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
 2019-07-24 21:43:21.380616: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
 2019-07-24 21:43:21.385223: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
 2019-07-24 21:43:21.385272: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
 2019-07-24 21:43:21.385299: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
 2019-07-24 21:43:21.388647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5250 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
 2019-07-24 21:43:32.001598: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
 2019-07-24 21:43:32.532105: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
 W0724 21:43:34.981204 140284114071872 deprecation_wrapper.py:119] From classify_image.py:85: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

答案 10 :(得分：0)

如果有人仍然有此问题，可以存在libcublas.so.10，但名称为libcublas.so.10.0

因此，您可以通过运行以下命令对其进行修复：

sudo ln libcublas.so.10.0.130 libcublas.so.10

在/usr/local/cuda-10.0/lib64

中

答案 11 :(得分：0)

我在conda环境中安装了正确版本的CUDA和tensorflow-gpu==1.14.0，但是不知何故我仍然收到此错误消息。 This post帮助我终于解决了这个问题。

我以前通过tensorflow-gpu安装了pip-在创建新环境并通过tensorflow-gpu安装conda之后解决了我的问题。

conda install -c anaconda tensorflow-gpu=1.14.0

答案 12 :(得分：-1)

尝试安装spconv时遇到类似的问题。

File "/home/kmario23/anaconda3/envs/py38/lib/python3.8/site-packages/torch/_ops.py", line 105, in load_library
    ctypes.CDLL(path)
  File "/home/kmario23/anaconda3/envs/py38/lib/python3.8/ctypes/__init__.py", line 373, in __init__
    self._handle = _dlopen(self._name, mode)
OSError: libcublas.so.10: cannot open shared object file: No such file or directory

在特定环境中安装cuda工具包版本10.1解决了该问题：

$ conda install -c anaconda cudatoolkit=10.1
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