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, 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上提到的先决条件
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
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)
我能够运行步骤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
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
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #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: [email protected]
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: [email protected]
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__)
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。
将tensorflow和tensorflow-gpu软件包并排安装并不是一个好主意（有一次它偶然发生在我身上，Keras正在使用CPU版本）。
我想我现在需要弄清楚如何让keras使用tensorflow的gpu版本。
您只需从系统中删除这两个软件包，然后重新安装tensorflow-gpu [评论后更新]：
pip uninstall tensorflow tensorflow-gpu
pip install tensorflow-gpu
此外，令人费解的是你为什么似乎使用floydhub/dl-docker:cpu容器，而根据说明你应该使用floydhub/dl-docker:gpu一个...
将来，您可以尝试使用虚拟环境来分离张量流CPU和GPU，例如：
conda create --name tensorflow python=3.5
activate tensorflow
pip install tensorflow
和
conda create --name tensorflow-gpu python=3.5
activate tensorflow-gpu
pip install tensorflow-gpu
我有类似的问题 - keras没有使用我的GPU。我根据conda的指令安装了tensorflow-gpu，但是在安装keras之后它根本没有将GPU列为可用设备。我意识到keras的安装增加了tensorflow包！所以我有tensorflow和tensorflow-gpu包。我发现有keras-gpu包可用。完成卸载keras，tensorflow，tensorflow-gpu和tensorflow-gpu的安装后，keras-gpu解决了问题。