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我的系统是 EC2 上的 Ubuntu 14.04：

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。

我在 /usr/local/local/lib64 中有正确的文件并包含文件夹。

Tensorflow 线什么也没给出：

    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:)）。

4

1 回答 1

2

你是如何构建张量流的？

如果您使用 bazel 执行此操作，您是否正确添加了 --config=cuda？

如果您使用 pip 安装它，您是否正确使用了启用 gpu 的那个？

编辑：

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

您需要使用与 gpu 兼容的二进制文件：

# 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

然后安装张量流：

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

# Python 3
$ sudo pip3 install --upgrade $TF_BINARY_URL
于 2016-10-03T03:27:59.150 回答