I'm building TensorFlow from source code. The build appears to succeed; however, when my TensorFlow program invokes import tensorflow, one or both of the following errors appear:
ImportError: libcudart.so.8.0: cannot open shared object file: No such file or directoryImportError: libcudnn.5: cannot open shared object file: No such file or directory
First, for the following error:
ImportError: libcudart.so.8.0: cannot open shared object file: No such file or directory
make sure your LD_LIBRARY_PATH includes your lib64 directory in whichever path you installed your cuda package in. You can do this by adding an export line in your .bashrc. For Omar, it looked like the following:
I fixed this just adding the cuda path to my .bashrc
export LD_LIBRARY_PATH=/usr/local/cuda/lib64/
For me, I had to do Omar's line and also:
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64/
because I have two directories involving cuda (probably not the best).
Second, are you sure you installed cuDNN? Note that this is different from the regular cuda package. You will need to register, then download and install the package from the following page: https://developer.nvidia.com/cudnn
Third, I had this same problem:
ImportError: libcudnn.5: cannot open shared object file: No such file or directory
It turns out there is no libcudnn.5 in my /usr/local/cuda/lib64 or /usr/local/cuda-8.0/lib64 directories. However, I do have a libcudnn.so.6.* file. To solve the problem, I created a soft link:
ln -s libcudnn.so.6.* libcudnn.so.5
in my /usr/local/cuda/lib64 directory. Now everything works for me. Your directory might be different if you already had cuDNN, and your libcudnn.so.6.* might be a different version, so check that.
I came across the same issue
In [1]: import tensorflow
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
/usr/local/lib/python3.5/site-packages/tensorflow/python/pywrap_tensorflow.py in <module>()
40 sys.setdlopenflags(_default_dlopen_flags | ctypes.RTLD_GLOBAL)
---> 41 from tensorflow.python.pywrap_tensorflow_internal import *
42 from tensorflow.python.pywrap_tensorflow_internal import __version__
/usr/local/lib/python3.5/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
/usr/local/lib/python3.5/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:
/usr/local/lib/python3.5/imp.py in load_module(name, file, filename, details)
241 else:
--> 242 return load_dynamic(name, filename, file)
243 elif type_ == PKG_DIRECTORY:
/usr/local/lib/python3.5/imp.py in load_dynamic(name, path, file)
341 name=name, loader=loader, origin=path)
--> 342 return _load(spec)
343
ImportError: libcudnn.so.5: 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-1-a649b509054f> in <module>()
----> 1 import tensorflow
/usr/local/lib/python3.5/site-packages/tensorflow/__init__.py in <module>()
22
23 # pylint: disable=wildcard-import
---> 24 from tensorflow.python import *
25 # pylint: enable=wildcard-import
26
/usr/local/lib/python3.5/site-packages/tensorflow/python/__init__.py in <module>()
49 import numpy as np
50
---> 51 from tensorflow.python import pywrap_tensorflow
52
53 # Protocol buffers
/usr/local/lib/python3.5/site-packages/tensorflow/python/pywrap_tensorflow.py in <module>()
50 for some common reasons and solutions. Include the entire stack trace
51 above this error message when asking for help.""" % traceback.format_exc()
---> 52 raise ImportError(msg)
53
54 # pylint: enable=wildcard-import,g-import-not-at-top,unused-import,line-too-long
ImportError: Traceback (most recent call last):
File "/usr/local/lib/python3.5/site-packages/tensorflow/python/pywrap_tensorflow.py", line 41, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "/usr/local/lib/python3.5/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "/usr/local/lib/python3.5/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 "/usr/local/lib/python3.5/imp.py", line 242, in load_module
return load_dynamic(name, filename, file)
File "/usr/local/lib/python3.5/imp.py", line 342, in load_dynamic
return _load(spec)
ImportError: libcudnn.so.5: cannot open shared object file: No such file or directory
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/install_sources#common_installation_problems
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
I have installed cudnn 6.0 while it needs libcudnn.so.5, apparently it couldn't find libcudnn.so.5. It seems that your tensorflow needs cudnn 5.x, so install cudnn 5.x
Make sure you have already installed cuda 8.0 and exported the PATH and LD_LIBRARY_PATH
To install cudnn 5.x, try the following commands
$ tar -zxvf cudnn-8.0-linux-x64-v5.1.tgz
$ cd cuda/lib64/
$ ls -l
total 150908
lrwxrwxrwx 1 doom doom 13 Nov 7 2016 libcudnn.so -> libcudnn.so.5
lrwxrwxrwx 1 doom doom 18 Nov 7 2016 libcudnn.so.5 -> libcudnn.so.5.1.10
-rwxr-xr-x 1 doom doom 84163560 Nov 7 2016 libcudnn.so.5.1.10
-rw-r--r-- 1 doom doom 70364814 Nov 7 2016 libcudnn_static.a
Here you will see 2 symbolic link files, and just copy libcudnn.so.5.1.10 and libcudnn_static.a to /usr/local/cuda/lib64
$ cd /usr/local/cuda/lib64/
$ sudo ln -s libcudnn.so.5.1.10 libcudnn.so.5
$ sudo ln -s libcudnn.so.5 libcudnn.so
$ ls -l libcudnn*
lrwxrwxrwx 1 root root 13 May 24 09:24 libcudnn.so -> libcudnn.so.5
lrwxrwxrwx 1 root root 18 May 24 09:24 libcudnn.so.5 -> libcudnn.so.5.1.10
-rwxr-xr-x 1 root root 84163560 May 24 09:23 libcudnn.so.5.1.10
-rw-r--r-- 1 root root 70364814 May 24 09:23 libcudnn_static.a
cudnn.h in include directory to /usr/local/cuda/include$ sudo cp cudnn.h /usr/local/cuda/include/
Hope it will help you!
I fixed this just adding the cuda path to my .bashrc
export LD_LIBRARY_PATH=/usr/local/cuda/lib64/
Just have in mind that first you need to go to nvidia Deep Learning page, register and download cuDNN, extract and copy the files from include and lib64 folders into your cuda installation.
I have seen a similar error (bottom of this post), but complaining about libcudnn.so.6 instead of libcudart.so.8.0 (see a note below).
Solution:
Note:
the Tensorflow installation instructions (as of 20/Aug/2017) require installing cuDNN v5.1, but my Tensorflow installation (following the instructions for installing in a virtualenv) required cuDNN v6.x (as indicated by the error). I don't know if it is a mistake on my side or a Tensorflow documentation one. Nevertheless, above solution worked for me.
Encountered error:
In [1]: import tensorflow as tf
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-1-41389fad42b5> in <module>()
----> 1 import tensorflow as tf
/home/haseeb/.virtualenvs/attention_transformer/local/lib/python2.7/site-packages/tensorflow/__init__.py in <module>()
22
23 # pylint: disable=wildcard-import
---> 24 from tensorflow.python import *
25 # pylint: enable=wildcard-import
26
/home/haseeb/.virtualenvs/attention_transformer/local/lib/python2.7/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
/home/haseeb/.virtualenvs/attention_transformer/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py in <module>()
50 for some common reasons and solutions. Include the entire stack trace
51 above this error message when asking for help.""" % traceback.format_exc()
---> 52 raise ImportError(msg)
53
54 # pylint: enable=wildcard-import,g-import-not-at-top,unused-import,line-too-long
ImportError: Traceback (most recent call last):
File "/home/haseeb/.virtualenvs/attention_transformer/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 41, in <module>
from tensorflow.python.pywrap_tensorflow_internal import *
File "/home/haseeb/.virtualenvs/attention_transformer/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
_pywrap_tensorflow_internal = swig_import_helper()
File "/home/haseeb/.virtualenvs/attention_transformer/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
_mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
ImportError: libcudnn.so.6: cannot open shared object file: No such file or directory
Failed to load the native TensorFlow runtime.
See https://www.tensorflow.org/install/install_sources#common_installation_problems
for some common reasons and solutions. Include the entire stack trace
above this error message when asking for help.
As of now tensor-flow supports cuda-9.0
Do following things. Hope it helps :
$ sudo apt-get install cuda-9.0
$ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-9.0/lib64
Download cuDNN for 9.0 (You need to register before downloading) https://developer.nvidia.com/rdp/form/cudnn-download-survey
$ sudo dpkg -i libcudnn7_7.1.2.21-1+cuda9.0_amd64.deb
Close all terminal and open new
$ source activate tensorflow
$ python
>> import tensorflow as tf
You should not get any error after this.
Mysteriously, my libcudnn.so.5 was installed at ~/cuda/lib64. For people like me, you need to change
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:~/cuda/lib64"
to
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/home/yourusername/cuda/lib64"
The preceding errors are typically caused by not specifying a version number for the Cuda SDK or cuDNN when you run the configure script. In other words, when running the configure script, always specify a version number in response to the following two questions:
Please specify the Cuda SDK version you want to use, e.g. 7.0.Please specify the cuDNN version you want to use.Don't accept the system defaults.
On MacOS, this issue is often caused by bazel running in a sandbox environment, thus not respecting the LD_LIBRARY_PATH set in your local shell. I wouldn't bother going into the merit of deep integration of sandboxing in a build tool.
The simple workaround is to symlink the libraries into /usr/local/lib.
cd /usr/local/lib && ln -s ../cuda/lib/libcudart.8.0.dylib
First, Install CUDA library (version 7.5) from here
Installation Instructions:
1- sudo dpkg -i cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb
2- sudo apt-get update
3- sudo apt-get install cuda
Second, install the cuDNN from here
Third, export cuDNN path:
export LD_LIBRARY_PATH=/usr/local/cuda/lib64/
In case you have an error like "The package libcudnnX needs to be reinstalled", follow those steps here
Check the NVIDIA requirements to run TensorFlow with GPU support (link):
CUDA® Toolkit 8.0
The NVIDIA drivers associated with CUDA Toolkit 8.0
cuDNN v6.0
GPU card with CUDA Compute Capability 3.0 or higher
The libcupti-dev library, which is the NVIDIA CUDA Profile Tools Interface
I installed the cuda v5.1 and the message below still remains:
ImportError: libcudart.so.8.0: cannot open shared object file:
No such file or directory
I so I got pissed off because everything looks fine, so I decide to check my GPU with the command (on Linux):
glxinfo | grep GeForce
And I noticed that my NVIDIA GPU is not supported:
OpenGL renderer string: **GeForce GTX 560M**/PCIe/SSE2
In this link you can find a list, like that:
So my solution was use tensor flow without GPU support. So I do:
pip uninstall tensorflow-gpu
I install whithout support:
pip install tensorflow
TensorFlow 1.2.1 is compatible with cuDNN 5.1, but not yet with 6.0. So just install cuDNN 5.1. Besides that you seem to be missing CUDA 8.0.
Common workaround general problem related to GPU, CUDA, and Docker:
A. If you are dealing with machine learning/ deep learning related deployment, use nvidia-docker and not native-docker. To install nvidia-docker follow these simple steps.
Docker containers are platform-agnostic, but also hardware-agnostic. This presents a problem when using specialized hardware such as NVIDIA GPUs which require kernel modules and user-level libraries to operate. As a result, Docker does not natively support NVIDIA GPUs within containers.
B. If you want to access GPU with docker, never build a container from scratch, you will be fried with a number of errors. Instead just use any container from Nvidia-Docker hub. Choose any specific image, copy its Dockerfile and run sudo nvidia-docker build -t happyapp .. [happyapp is your new app name]. In 5 min you will get your container ready (depends on network speed :p).
C. Never download a nvidia-docker having any cuda/cudnn version If you want to install and run Tensorflow on it. If you do so you will get libcudnn.so.6 or libcudnn.so.9 or libcusolver.so.8.0 related errors and you will hardly get around this errors.
Instead just use pre-built Tensorflow Docker image: sudo nvidia-docker run -it tensorflow/tensorflow:latest-gpu /bin/bash
