Cuda，CuDNN已安装，但Tensorflow无法使用GPU [英] Cuda, CuDNN installed But Tensorflow can't use the GPU

问题描述

我的系统是EC2上的Ubuntu 14.04:

My system is Ubuntu 14.04 on EC2.:

nvidia-smi
Sun Oct  2 13:35:28 2016       
+------------------------------------------------------+                       
| NVIDIA-SMI 352.63     Driver Version: 352.63         |                       
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GRID K520           Off  | 0000:00:03.0     Off |                  N/A |
| N/A   37C    P0    35W / 125W |     11MiB /  4095MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
ubuntu@ip-XXX-XX-XX-990:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2015 NVIDIA Corporation
Built on Tue_Aug_11_14:27:32_CDT_2015
Cuda compilation tools, release 7.5, V7.5.17

我安装了CUDA 7.5和CuDNN 5.1.

I Installed CUDA 7.5 and CuDNN 5.1.

我在/usr/local/local/lib64中有适当的文件，并且包含文件夹.

I have the proper files in /usr/local/local/lib64 and include folders.

Tensorflow行什么都没有:

Tensorflow line gives nothing:

    sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
Device mapping: no known devices.
I tensorflow/core/common_runtime/direct_session.cc:252] Device mapping:

>>> 

请帮助(非常感谢:)).

Please help (Thanks a lot :)).

推荐答案

您如何构建tensorflow?

How did you build tensorflow?

如果用bazel做到了，是否正确添加了--config = cuda?

If you did it with bazel did you add correctly --config=cuda?

如果您通过pip安装了它，是否正确选择了具有gpu enable的产品?

If you installed it with pip did you took correctly the one with gpu enable?

您可以在此处查看如何使用pip进行安装: https://www.tensorflow.org/versions/r0 .11/get_started/os_setup.html#pip-installation

You can see here how to install with pip: https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html#pip-installation

您需要选择一个与gpu兼容的二进制文件:

You need to take the one with binary compatible with gpu:

# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp27-none-linux_x86_64.whl

# Mac OS X, GPU enabled, Python 2.7:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.11.0rc0-py2-none-any.whl

# Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp34-cp34m-linux_x86_64.whl

# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 7.5 and CuDNN v5. For other versions, see "Install from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc0-cp35-cp35m-linux_x86_64.whl

# Mac OS X, GPU enabled, Python 3.4 or 3.5:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow-0.11.0rc0-py3-none-any.whl

然后安装tensorflow:

then install tensorflow:

# Python 2
$ sudo pip install --upgrade $TF_BINARY_URL

# Python 3
$ sudo pip3 install --upgrade $TF_BINARY_URL

这篇关于Cuda，CuDNN已安装，但Tensorflow无法使用GPU的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！