我已经在 Ubuntu 18.04 上安装了 Cuda 10.1 和 cudnn，它似乎正确安装为类型 nvcc 和 nvidia-smi，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 |
+-----------------------------------------------------------------------------+
user:~$ sudo pip3 install --upgrade tensorflow-gpuThe 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)
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.
谢谢
最佳答案
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
然后我通过链接为 CUDA 10.0 安装了 cudnn v7.5.0
CUDNN download并且您需要使用帐户登录。
选择正确的版本后，我通过链接下载 CUDNN power link
之后，我为 cudnn 添加了包含和 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*
修改了.bashrc的lib和cuda 10.0的路径后，如果没有，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}}
经过所有这些步骤，我成功地在python3中导入了tensorflow。
关于tensorflow - 导入错误 : libcublas. so.10.0:无法打开共享对象文件:没有这样的文件或目录，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/55224016/