带有 TensorFlow 后端的 Keras 不使用 GPU [英] Keras with TensorFlow backend not using GPU
问题描述
我构建了 docker 镜像的 gpu 版本 https://github.com/floydhub/dl-docker 使用 keras 2.0.0 版和 tensorflow 0.12.1 版.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.下面是我的输出
I built the gpu version of the docker image https://github.com/floydhub/dl-docker with keras version 2.0.0 and tensorflow version 0.12.1. I then ran the mnist tutorial https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py but realized that keras is not using GPU. Below is the output 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.
谁能告诉我在 keras 使用 GPU 之前是否需要进行一些设置?我对所有这些都很陌生，所以如果我需要提供更多信息，请告诉我.
Can anyone let me know if there are some settings that need to be made before keras uses GPU ? I am very new to all these so do let me know if I need to provide more information.
我已经安装了页面上提到的先决条件/strong>
I have installed the pre-requisites as mentioned on the page
- 按照适用于您平台的安装指南安装 Docker:https://docs.docker.com/engine/安装/
我可以启动docker镜像
I am able to launch the docker image
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 或按照说明在您的计算机上安装 Nvidia 驱动程序这里.请注意，您不必安装 CUDA 或 cuDNN.这些都包含在 Docker 容器中.
- GPU Version Only: Install Nvidia drivers on your machine either from Nvidia directly or follow the instructions here. Note that you don't have to install CUDA or cuDNN. These are included in the Docker container.
我能够运行最后一步
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 GPU 版本:安装 nvidia-docker:https://github.com/NVIDIA/nvidia-docker，按照此处的说明进行操作.这将安装 docker CLI 的替代品.它负责在 Docker 容器内设置 Nvidia 主机驱动程序环境以及其他一些事情.
- GPU Version Only: Install nvidia-docker: https://github.com/NVIDIA/nvidia-docker, following the instructions here. This will install a replacement for the docker CLI. It takes care of setting up the Nvidia host driver environment inside the Docker containers and a few other things.
我可以在此处
# 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 |
+-----------------------------------------------------------------------------+
我还可以运行 nvidia-docker 命令来启动支持 GPU 的映像.
I am also able to run the nvidia-docker command to launch a gpu supported image.
我的尝试
我已经尝试了以下建议
- 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.
- Check if you have completed step 9 of this tutorial ( https://github.com/ignaciorlando/skinner/wiki/Keras-and-TensorFlow-installation ). Note: Your file paths may be completely different inside that docker image, you'll have to locate them somehow.
我将建议的行附加到我的 bashrc 中，并已验证 bashrc 文件已更新.
I appended the suggested lines to my bashrc and have verified that the bashrc file is updated.
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
To import the following commands in my python file
导入操作系统os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # 见问题 #152os.environ["CUDA_VISIBLE_DEVICES"]="0"
不幸的是，单独或一起完成的两个步骤都没有解决问题.Keras 仍然以 tensorflow 的 CPU 版本作为其后端运行.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.
Both steps, done separately or together unfortunately did not solve the issue. Keras is still running with the CPU version of tensorflow as its backend. However, I might have found the possible issue. I checked the version of my tensorflow via the following commands and found two of them.
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，它是 CPU 版本，而不是 0.12.1，GPU 版本
Interestingly, the output shows that keras is using tensorflow version 1.3.0 which is the CPU version and not 0.12.1, the GPU version
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
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 gpu 版本的 tensorflow.
I guess now I need to figure out how to have keras use the gpu version of tensorflow.
推荐答案
同时拥有 tensorflow 和 tensorflow-gpu 永远不是一个好主意代码> 并排安装的包(有一次偶然发生在我身上，Keras 使用的是 CPU 版本).
It is never a good idea to have both tensorflow and tensorflow-gpu packages installed side by side (the one single time it happened to me accidentally, Keras was using the CPU version).
我想现在我需要弄清楚如何让 keras 使用 gpu 版本的 tensorflow.
I guess now I need to figure out how to have keras use the gpu version of tensorflow.
您应该简单地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]:
You should simply remove both packages from your system, and then re-install tensorflow-gpu [UPDATED after comment]:
pip uninstall tensorflow tensorflow-gpu
pip install tensorflow-gpu
此外，令人费解的是为什么您似乎使用 floydhub/dl-docker:cpu 容器，而根据说明您应该使用 floydhub/dl-docker:gpu 一...
Moreover, it is puzzling why you seem to use the floydhub/dl-docker:cpu container, while according to the instructions you should be using the floydhub/dl-docker:gpu one...
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