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.
I guess now I need to figure out how to have keras use the gpu version of tensorflow.
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,令人费解的是你似乎使用了floydhub / dl-docker：cpu容器,而根据说明你应该使用floydhub / dl-docker：gpu one …
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