带有 TensorFlow 后端的 Keras 不使用 GPU

Question

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.

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.

I have installed the pre-requisites as mentioned on the page

• Install Docker following the installation guide for your platform: https://docs.docker.com/engine/installation/

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

• 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.

I am able to run the last step

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)

• 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.

I am able to run the step 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                                         |
+-----------------------------------------------------------------------------+

I am also able to run the nvidia-docker command to launch a gpu supported image.

What I have tried

I have tried the following suggestions below

1. 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.

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

1. To import the following commands in my python file

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

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.

This is the CPU version

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

And this is the GPU version

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

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__)

Output

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

I guess now I need to figure out how to have keras use the gpu version of tensorflow.

Answer 1

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).

I guess now I need to figure out how to have keras use the gpu version of tensorflow.

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

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...

Answer 2

I had similar kind of issue - keras didn't use my GPU. I had tensorflow-gpu installed according to instruction into conda, but after installation of keras it simply not listed GPU as available device. I've realized that installation of keras adds tensorflow package! So I had both tensorflow and tensorflow-gpu packages. I've found that there is keras-gpu package available. After complete uninstallation of keras, tensorflow, tensorflow-gpu and installation of tensorflow-gpu, keras-gpu the problem was solved.

Answer 3

In the future, you can try using virtual environments to separate tensorflow CPU and GPU, for example:

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

AND

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

Answer 4

这对我有用：安装 tensorflow v2.2.0 pip install tensorflow==2.2.0 也删除 tensorflow-gpu（如果存在）