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.pyUsing 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/versionNVRM 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-smicv@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
- 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' >> ~/.bashrcecho 'export CUDA_HOME=/usr/local/cuda-8.0' >> ~/.bashrc
- 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 tensorflowName: 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-gpuName: 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 kerasfrom 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.pyUsing 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-gpupip 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