使用tensorflow-gpu后端导入Keras时出错（找不到libcublas.so.10.0）

Question

I'm trying to run a library included in Keras, given that it's very power-consuming I'd like to use tensorflow-gpu as a backend. During import, I get this ImportError

Using TensorFlow backend.

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
~/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py in <module>
     57 
---> 58   from tensorflow.python.pywrap_tensorflow_internal import *
     59   from tensorflow.python.pywrap_tensorflow_internal import __version__

~/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py in <module>
     27             return _mod
---> 28     _pywrap_tensorflow_internal = swig_import_helper()
     29     del swig_import_helper

~/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py in swig_import_helper()
     23             try:
---> 24                 _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
     25             finally:

~/.conda/envs/tensorflow/lib/python3.7/imp.py in load_module(name, file, filename, details)
    241         else:
--> 242             return load_dynamic(name, filename, file)
    243     elif type_ == PKG_DIRECTORY:

~/.conda/envs/tensorflow/lib/python3.7/imp.py in load_dynamic(name, path, file)
    341             name=name, loader=loader, origin=path)
--> 342         return _load(spec)
    343 

ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory

During handling of the above exception, another exception occurred:

ImportError                               Traceback (most recent call last)
<ipython-input-11-bbde2f34164a> in <module>
      2 from torch.optim import Adam
      3 from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
----> 4 from keras.preprocessing.sequence import pad_sequences
      5 from sklearn.model_selection import train_test_split
      6 from pytorch_pretrained_bert import BertTokenizer, BertConfig

~/.conda/envs/tensorflow/lib/python3.7/site-packages/keras/__init__.py in <module>
      1 from __future__ import absolute_import
      2 
----> 3 from . import utils
      4 from . import activations
      5 from . import applications

~/.conda/envs/tensorflow/lib/python3.7/site-packages/keras/utils/__init__.py in <module>
      4 from . import data_utils
      5 from . import io_utils
----> 6 from . import conv_utils
      7 
      8 # Globally-importable utils.

~/.conda/envs/tensorflow/lib/python3.7/site-packages/keras/utils/conv_utils.py in <module>
      7 from six.moves import range
      8 import numpy as np
----> 9 from .. import backend as K
     10 
     11 

~/.conda/envs/tensorflow/lib/python3.7/site-packages/keras/backend/__init__.py in <module>
     87 elif _BACKEND == 'tensorflow':
     88     sys.stderr.write('Using TensorFlow backend.\n')
---> 89     from .tensorflow_backend import *
     90 else:
     91     # Try and load external backend.

~/.conda/envs/tensorflow/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py in <module>
      3 from __future__ import print_function
      4 
----> 5 import tensorflow as tf
      6 from tensorflow.python.framework import ops as tf_ops
      7 from tensorflow.python.training import moving_averages

~/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/__init__.py in <module>
     22 
     23 # pylint: disable=g-bad-import-order
---> 24 from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
     25 
     26 from tensorflow._api.v1 import app

~/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/__init__.py in <module>
     47 import numpy as np
     48 
---> 49 from tensorflow.python import pywrap_tensorflow
     50 
     51 # Protocol buffers

~/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py in <module>
     72 for some common reasons and solutions.  Include the entire stack trace
     73 above this error message when asking for help.""" % traceback.format_exc()
---> 74   raise ImportError(msg)
     75 
     76 # pylint: enable=wildcard-import,g-import-not-at-top,unused-import,line-too-long

ImportError: Traceback (most recent call last):
  File "/home/canniz/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "/home/canniz/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "/home/canniz/.conda/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
    _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
  File "/home/canniz/.conda/envs/tensorflow/lib/python3.7/imp.py", line 242, in load_module
    return load_dynamic(name, filename, file)
  File "/home/canniz/.conda/envs/tensorflow/lib/python3.7/imp.py", line 342, 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.

I see here --> ImportError: libcublas.so.10.0: cannot open shared object file: No such file or director that the problem could be an incompatible version of CUDA with tensorflow GPU.

So now my problem is the following:

• Tensorflow GPU version is 1.13

• I have installed CUDA 10.0 (following the instructions of compatibility) and the relative Cudnn in fact what I get from nvcc --version is `nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2018 NVIDIA Corporation Built on Sat_Aug_25_21:08:01_CDT_2018 Cuda compilation tools, release 10.0, V10.0.130

I installed Nvidia drivers running sudo apt-get install nvidia-driver-430 (which should be the right version for my Nvidia-GeForce-930mX)

As you can see, the CUDA version is 10.2. How is it possible? Is it possible that installing Nvidia drivers, it automatically set CUDA driver to 10.2 and then I manually installed CUDA toolkit 10.0 so now Tensorflow (or more specifically Keras, using tf backend) looks at the CUDA DRIVER version?

What could I do? Downgrade Nvidia drivers? Is it safe? Is it possible to downgrade only CUDA DRIVERS?

Answer 1

You can try to uninstall tensorflow with :

pip uninstall tensorflow-gpu

and install an older version of it :

pip install tensorflow-gpu==1.12.0