我已经构建了基于keras版本2.0.0和tensorflow版本0.12.1的gpu版docker镜像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，但发现keras没有使用GPU。以下是我的输出结果。
我还可以运行nvidia-docker命令以启动支持GPU的镜像。 我尝试过的 我尝试了以下建议：
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.
请问在使用Keras前是否需要进行一些设置以使用GPU？我对这些都很陌生，如果需要提供更多信息，请告诉我。
我已按照页面上所述安装了先决条件
- 按照您的平台安装Docker：https://docs.docker.com/engine/installation/
我可以启动Docker镜像
docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder floydhub/dl-docker:cpu bash
- 仅适用于GPU版本：在您的计算机上安装Nvidia驱动程序，可以直接从Nvidia安装，或者按照此处的说明进行操作。请注意，您不必安装CUDA或cuDNN，因为它们已经包含在Docker容器中。
我能够运行最后一步。
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)
- 仅适用于GPU版本：安装nvidia-docker: https://github.com/NVIDIA/nvidia-docker，请按照此处的说明进行操作。这将安装docker CLI的替代品。它会在Docker容器内设置Nvidia主机驱动程序环境并处理其他一些事情。
我能够运行here步骤。
# 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 |
+-----------------------------------------------------------------------------+
我还可以运行nvidia-docker命令以启动支持GPU的镜像。 我尝试过的 我尝试了以下建议：
- 检查您是否完成了此教程（https://github.com/ignaciorlando/skinner/wiki/Keras-and-TensorFlow-installation）的第9步。注意：该docker映像内部的文件路径可能完全不同，您必须以某种方式定位它们。
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
在我的Python文件中导入以下命令
import os os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # 查看问题＃152 os.environ["CUDA_VISIBLE_DEVICES"]="0"
这两个步骤，无论是分开还是一起完成，都无法解决问题。Keras仍然使用CPU版本的tensorflow作为其后端。但是，我可能已经找到了可能的问题。通过以下命令检查了我的tensorflow版本，并发现了其中的两个版本。
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正在使用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
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。