TensorFlow后端的Keras不使用GPU

我使用keras版本2.0.0和tensorflow版本0.12.1构建了docker

镜像的gpu版本https://github.com/floydhub/dl-

docker。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.

有人可以让我知道在keras使用GPU之前是否需要进行一些设置吗?我对所有这些都是新手,所以如果需要提供更多信息,请告诉我。

  • 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映像

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

  • 仅限GPU版本:直接从Nvidia或按照此处的说明在计算机上安装Nvidia驱动程序。请注意,您不必安装CUDA或cuDNN。这些都包含在Docker容器中。

我能够执行最后一步

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事项。

我可以在这里跑一步

# 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 |

+-----------------------------------------------------------------------------+

我还能够运行nvidia-docker命令来启动gpu支持的映像。

我在下面尝试了以下建议

  1. 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:您的文件路径在该docker映像中可能完全不同,您必须以某种方式找到它们。

我在我的bashrc中附加了建议的行,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

  1. 在我的python文件中导入以下命令

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

os.environ["CUDA_VISIBLE_DEVICES"]="0"

不幸的是,单独或一起执行的两个步骤都无法解决问题。Keras仍以CPU版本的tensorflow作为后端运行。但是,我可能已经发现了可能的问题。我通过以下命令检查了我的tensorflow的版本,发现其中两个。

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, 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__)

输出量

root@08b5fff06800:~/sharedfolder# python test.py

Using TensorFlow backend.

Tensorflow: 1.3.0

我想现在我需要弄清楚如何让keras使用tensorflow的gpu版本。

回答:

这是 有两个是个好主意tensorflow,并tensorflow-

gpu包并排安装(在一个单独的一次发生在我身上不小心,Keras使用的CPU版本)。

我想现在我需要弄清楚如何让keras使用tensorflow的gpu版本。

您应该只从系统中删除这两个软件包,然后重新安装tensorflow-gpu[注释后更新]:

pip uninstall tensorflow tensorflow-gpu

pip install tensorflow-gpu

此外,令人困惑的是为什么您似乎要使用该floydhub/dl-docker:cpu容器,而根据说明,您应该使用该容器floydhub/dl-

docker:gpu

以上是 TensorFlow后端的Keras不使用GPU 的全部内容, 来源链接: utcz.com/qa/424196.html

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