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Keras with TensorFlow support without GPU

I built a version of docker gpu image https://github.com/floydhub/dl-docker with keras version 2.0.0 and version tenorflow 0.12.1. Then I ran the mnist tutorial https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py , but realized that keras does not use the GPU. Below is the result that I have

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.

Can someone tell me if there are any settings that need to be done before kera uses the GPU? I am very new to this, so let me know if I need to provide more information.

I set the prerequisites as indicated on the page

docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder floydhub/dl-docker:cpu bash
  • GPU: Nvidia Nvidia , . , CUDA cuDNN. Docker.

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)


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

nvidia-docker, gpu .

bashrc , bashrc .

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

  • python

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

, , , . Keras - tensorflow . . .

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


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

, , keras tensorflow 1.3.0, CPU, 0.12.1,

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__)


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

, , keras gpu.

+4
2

tensorflow tensorflow-gpu, ( , , Keras CPU).

, , keras gpu.

, tensorflow-gpu [ ]:

pip uninstall tensorflow tensorflow-gpu
pip install tensorflow-gpu

, , , , floydhub/dl-docker:cpu, floydhub/dl-docker:gpu ...

+9

tenorflow, :

conda create --name tensorflow python=3.5
activate tensorflow
pip install tensorflow


conda create --name tensorflow-gpu python=3.5
activate tensorflow-gpu
pip install tensorflow-gpu
0

Source: https://habr.com/ru/post/1685178/

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