docker – 使用TensorFlow后端的Keras不使用GPU

我用keras版本2.0.0和tensorflow版本0.12.1构建了docker镜像https://github.com/floydhub/dl-docker的gpu版本.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: (60000,28,1)
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.

如果在keras使用GPU之前需要进行一些设置,有人可以告诉我吗？我对所有这些都很新,所以如果我需要提供更多信息,请告诉我.

我已经安装了page中提到的先决条件

>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镜像

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

> GPU版：无论是来自Nvidia安装在计算机上的Nvidia驱动程序直接或按照指示here.请注意,您不必安装CUDA或cuDNN.这些包含在Docker容器中.

我能够完成最后一步

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东西.

我能够执行步骤here

# 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                                         |
+-----------------------------------------------------------------------------+

我也可以运行nvidia-docker命令来启动支持gpu的映像.

我试过了什么

我在下面尝试了以下建议

>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docker镜像中可能完全不同,您必须以某种方式找到它们.

我将建议的行添加到我的bashrc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

>在我的python文件中导入以下命令

进口口
os.environ [“CUDA_DEVICE_ORDER”] =“PCI_BUS_ID”#见问题#152
os.environ [ “CUDA_VISIBLE_DEVICES”] = “0”

不幸的是,这两个步骤单独或一起完成并没有解决问题. Keras仍在以tensorflow的CPU版本作为后端运行.但是,我可能已经找到了可能的问题.我通过以下命令检查了我的tensorflow的版本,并找到了其中两个.

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,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__)

产量

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

我想我现在需要弄清楚如何让keras使用tensorflow的gpu版本.