Tensor flow toggle between CPU/GPU

Question

Having installed tensorflow GPU (running on a measly NVIDIA GeForce 950), I would like to compare performance with the CPU.

I am running the tensorFlow MNIST tutorial code, and have noticed a dramatic increase in speed--estimated anyways (I ran the CPU version 2 days ago on a laptop i7 with a batch size of 100, and this on a desktop GPU, batch size of 10)--between the CPU and the GPU when I switched...but I only noticed the speed increase when I lowered the batch size on the GPU to 10 from 100...

Now I lack an objective measure for what I am gaining.

Is there a way to toggle between the CPU and GPU tensor flows?

Yaroslav Bulatov · Accepted Answer · 2016-04-12 22:40:38Z

40

To make GPU invisible

export CUDA_VISIBLE_DEVICES=""

To return to normal

unset CUDA_VISIBLE_DEVICES

edited Apr 12, 2016 at 22:40

answered Apr 12, 2016 at 22:33

Yaroslav Bulatov

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3 Comments

Chaine Over a year ago

where to put this?

Maksym Ganenko Over a year ago

..or CUDA_VISIBLE_DEVICES="" python myscript.py

Conchylicultor Over a year ago

@Chaine in your terminal, before running your python my_script.py

user18101 · Accepted Answer · 2016-08-24 14:28:54Z

20

try setting tf.device to cpu:0

with tf.Session() as sess:
     with tf.device("/cpu:0"):

answered Aug 24, 2016 at 14:28

user18101

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David Zhan Liu · Accepted Answer · 2016-12-26 10:26:12Z

7

Another option is to install the cpu version and the gpu version of tensorflow in two virtual environments, detailed instructions on how to install tensorflow in virtual environments are listed here https://www.tensorflow.org/get_started/os_setup; in this way, you can have the same code running in two terminals windows one uses CPU and the other uses GPU.

answered Dec 26, 2016 at 10:26

David Zhan Liu

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Sambbhav Garg · Accepted Answer · 2020-09-07 09:33:43Z

6

# Check if the server/ instance is having GPU/ CPU from python code
import sys
import tensorflow as tf
from tensorflow.python.client import device_lib

# device_lib.list_local_devices()     ## this command list all the processing device GPU and CPU


device_name = [x.name for x in device_lib.list_local_devices() if x.device_type == 'GPU']
if device_name[0] == "/device:GPU:0":
    device_name = "/gpu:0"
    #print('GPU')
else:
    #print('CPU')
    device_name = "/cpu:0"

with tf.device(device_name):
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
    c = tf.matmul(a, b)
with tf.Session() as sess:
    print (sess.run(c))

edited Sep 7, 2020 at 9:33

Sambbhav Garg

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answered Jun 4, 2019 at 1:48

DSBLR

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Comments

lurix66 · Accepted Answer · 2020-01-17 12:46:49Z

6

Quite a long time elapsed. Recent versions of Tensorflow (at least from 2.0 on) don't require installing both versions with and without gpu support, so you can launch two separate jupyter-notebook instances. Following @Yaroslav's advice:

$ CUDA_VISIBLE_DEVICES="" jupyter-notebook &
$ jupyter-notebook &

You will then have two separate jupyter clients open in your browser, typically http://localhost:8888/ and http://localhost:8889/, respectively without and with GPU support, where you can run the same .ipynb notebook and measure the performance difference.

answered Jan 17, 2020 at 12:46

lurix66

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The Way · Accepted Answer · 2020-12-13 05:39:26Z

6

To turn off GPU, simply add this at the top of your script.

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

(Comment it out when you want to use the GPU again)

answered Dec 13, 2020 at 5:39

The Way

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