Keras與TensorFlow後端不使用GPU

Question

我構建了docker圖像https://github.com/floydhub/dl-docker的gpu版本，keras版本2.0.0和tensorflow版本0.12.1。然後我運行了mnist教程https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py，但意識到keras沒有使用GPU。下面是我的輸出

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. 

任何人都可以讓我知道，如果有一些設置需要在keras使用GPU之前做出？我對這些都很陌生，所以如果我需要提供更多信息，請告訴我。

我已經安裝了先決條件的page

• 提到下面的安裝指南平臺安裝泊塢窗：https://docs.docker.com/engine/installation/

我能夠啓動泊塢窗圖像

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

• 僅限GPU版本：直接在Nvidia上安裝Nvidia驅動程序或按照說明書here安裝。請注意，您不必安裝CUDA或cuDNN。這些包含在Docker容器中。

我能夠運行的最後一步

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) 

• GPU版：安裝NVIDIA-泊塢窗：https://github.com/NVIDIA/nvidia-docker，這裏的操作說明。這將安裝Docker CLI的替代品。它負責在Docker容器內部設置Nvidia主機驅動程序環境以及其他一些內容。

我能夠運行步驟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           | 
+-----------------------------------------------------------------------------+ 

我也可以運行NVIDIA-泊塢窗命令來啓動一個GPU支持的圖像。

我已經試過

我曾嘗試下面

1. 檢查下列建議，如果你已經完成了本教程（https://github.com/ignaciorlando/skinner/wiki/Keras-and-TensorFlow-installation）的第9步。注意：您的文件路徑在該Docker鏡像中可能完全不同，您必須以某種方式找到它們。

我將建議的行附加到我的bashrc並驗證了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. 要在我的Python文件

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

兩個步驟，完成單獨或一起可惜沒能解決問題導入下面的命令。 Keras仍然使用tensorflow的CPU版本作爲其後端。但是，我可能已經找到了可能的問題。我通過以下命令檢查了我的tensorflow版本，發現其中兩個。

這是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 

而這是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，輸出顯示keras使用tensorflow版本1.3.0這是CPU版本，而不是0.12.1中，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__) 

輸出

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

我想現在我需要弄清楚如何讓keras使用tensorflow的gpu版本。

Answer 1

這是從未一個好主意，有兩個tensorflow和tensorflow-gpu包並排安裝（在一個單獨的一次發生在我身上不小心，Keras使用的CPU版本）。

我想現在我需要弄清楚如何讓keras使用tensorflow的gpu版本。此外，根據同時

pip uninstall tensorflow tensorflow-gpu 
pip install tensorflow-gpu 

，令人費解，爲什麼你似乎使用floydhub/dl-docker:cpu容器：

您應該簡單地完全去除tensorflow包，只留下tensorflow-gpu在您的系統[評論之後更新]到你應該使用的指令floydhub/dl-docker:gpu ...