我使用keras版本2.0.0和tensorflow版本0.12.1构建了docker
镜像的gpu版本https://github.com/floydhub/dl-
docker。然后，我运行了mnist教程https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py，但意识到keras没有使用GPU。以下是我的输出

root@b79b8a57fb1f:~/sharedfolder# python test.py
Using TensorFlow backend.
Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz
x_train shape: (60000, 28, 28, 1)
60000 train samples
10000 test samples
Train on 60000 samples, validate on 10000 samples
Epoch 1/12
2017-09-06 16:26:54.866833: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-06 16:26:54.866855: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-06 16:26:54.866863: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-06 16:26:54.866870: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-06 16:26:54.866876: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.

有人可以让我知道在keras使用GPU之前是否需要进行一些设置吗？我对所有这些都是新手，所以如果需要提供更多信息，请告诉我。

我已经安装了 页面上提到的先决条件

• 按照适用于您平台的安装指南安装Docker：https : //docs.docker.com/engine/installation/

我能够启动Docker映像

docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder floydhub/dl-docker:cpu bash

• 仅限GPU版本：直接从Nvidia或按照此处的说明在计算机上安装Nvidia驱动程序。请注意，您不必安装CUDA或cuDNN。这些都包含在Docker容器中。

我能够执行最后一步

cv@cv-P15SM:~$ cat /proc/driver/nvidia/version
NVRM version: NVIDIA UNIX x86_64 Kernel Module  375.66  Mon May  1 15:29:16 PDT 2017
GCC version:  gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.4)

• 仅限GPU版本：按照此处的说明安装nvidia-docker：https : //github.com/NVIDIA/nvidia-docker。这将安装docker CLI的替代品。它负责在Docker容器中设置Nvidia主机驱动程序环境以及其他一些事项。

我可以在这里跑一步

# Test nvidia-smi
cv@cv-P15SM:~$ nvidia-docker run --rm nvidia/cuda nvidia-smi

Thu Sep  7 00:33:06 2017       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.66                 Driver Version: 375.66                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 780M    Off  | 0000:01:00.0     N/A |                  N/A |
| N/A   55C    P0    N/A /  N/A |    310MiB /  4036MiB |     N/A      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0                  Not Supported                                         |
+-----------------------------------------------------------------------------+

我还能够运行nvidia-docker命令来启动gpu支持的映像。

我尝试过的

我在下面尝试了以下建议

1. 检查您是否已完成本教程的第9步（https://github.com/ignaciorlando/skinner/wiki/Keras-and-TensorFlow-installation）。注意：您的文件路径在该docker映像中可能完全不同，您必须以某种方式找到它们。

我在我的bashrc中附加了建议的行，并验证了bashrc文件已更新。

echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64' >> ~/.bashrc
echo 'export CUDA_HOME=/usr/local/cuda-8.0' >> ~/.bashrc

1. 在我的python文件中导入以下命令

import os os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"]="0"

不幸的是，单独或一起执行的两个步骤都无法解决问题。Keras仍以CPU版本的tensorflow作为后端运行。但是，我可能已经发现了可能的问题。我通过以下命令检查了我的tensorflow的版本，发现其中两个。

这是CPU版本

root@08b5fff06800:~# pip show tensorflow
Name: tensorflow
Version: 1.3.0
Summary: TensorFlow helps the tensors flow
Home-page: http://tensorflow.org/
Author: Google Inc.
Author-email: opensource@google.com
License: Apache 2.0
Location: /usr/local/lib/python2.7/dist-packages
Requires: tensorflow-tensorboard, six, protobuf, mock, numpy, backports.weakref, wheel

这是GPU版本

root@08b5fff06800:~# pip show tensorflow-gpu
Name: tensorflow-gpu
Version: 0.12.1
Summary: TensorFlow helps the tensors flow
Home-page: http://tensorflow.org/
Author: Google Inc.
Author-email: opensource@google.com
License: Apache 2.0
Location: /usr/local/lib/python2.7/dist-packages
Requires: mock, numpy, protobuf, wheel, six

有趣的是，输出显示keras使用的是Tensorflow版本1.3.0，这是CPU版本而不是0.12.1（GPU版本）

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

import tensorflow as tf
print('Tensorflow: ', tf.__version__)

输出量

root@08b5fff06800:~/sharedfolder# python test.py
Using TensorFlow backend.
Tensorflow:  1.3.0

我想现在我需要弄清楚如何让keras使用tensorflow的gpu版本。