python - How to run Tensorflow on CPU

Question

I have installed the GPU version of tensorflow on an Ubuntu 14.04.

I am on a GPU server where tensorflow can access the available GPUs.

I want to run tensorflow on the CPUs.

Normally I can use env CUDA_VISIBLE_DEVICES=0 to run on GPU no. 0.

How can I pick between the CPUs instead?

I am not intersted in rewritting my code with with tf.device("/cpu:0"):

Answer 1

You can apply device_count parameter per tf.Session:

config = tf.ConfigProto(
        device_count = {'GPU': 0}
    )
sess = tf.Session(config=config)

See also protobuf config file:

tensorflow/core/framework/config.proto

Answer 2

You can also set the environment variable to

CUDA_VISIBLE_DEVICES=""

without having to modify the source code.

Answer 3

If the above answers don't work, try:

os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

Answer 4

For me, only setting CUDA_VISIBLE_DEVICES to precisely -1 works:

Works:

import os
import tensorflow as tf

os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

if tf.test.gpu_device_name():
    print('GPU found')
else:
    print("No GPU found")

# No GPU found

Does not work:

import os
import tensorflow as tf

os.environ['CUDA_VISIBLE_DEVICES'] = ''    

if tf.test.gpu_device_name():
    print('GPU found')
else:
    print("No GPU found")

# GPU found

Answer 5

The environment variable solution doesn't work for me running tensorflow 2.3.1. I assume by the comments in the github thread that the below solution works for versions >=2.1.0.

From tensorflow github:

import tensorflow as tf

# Hide GPU from visible devices
tf.config.set_visible_devices([], 'GPU')

Make sure to do this right after the import with fresh tensorflow instance (if you're running jupyter notebook, restart the kernel).

And to check that you're indeed running on the CPU:

# To find out which devices your operations and tensors are assigned to
tf.debugging.set_log_device_placement(True)

# Create some tensors and perform an operation
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)

print(c)

Expected output:

2.3.1
Executing op MatMul in device /job:localhost/replica:0/task:0/device:CPU:0
tf.Tensor(
[[22. 28.]
 [49. 64.]], shape=(2, 2), dtype=float32)

Answer 6

Just using the code below.

import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'

Answer 7

In my case, for tensorflow 2.4.0, none of the previous answer works unless you install tensorflow-cpu

pip install tensorflow-cpu

Answer 8

As recommended by the Tensorflow GPU guide.

# Place tensors on the CPU
with tf.device('/CPU:0'):
  a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
  b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
  # Any additional tf code placed in this block will be executed on the CPU

Answer 9

Another possible solution on installation level would be to look for the CPU only variant: https://www.tensorflow.org/install/pip#package-location

In my case, this gives right now:

pip3 install https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.2.0-cp38-cp38-win_amd64.whl

Just select the correct version (in this case, cp38 hints python 3.8). Bonus points for using a venv like explained eg in this answer.

Answer 10

In some systems one have to specify:

import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=""  # or even "-1"

BEFORE importing tensorflow.

Answer 11

You could use tf.config.set_visible_devices. One possible function that allows you to set if and which GPUs to use is:

import tensorflow as tf

def set_gpu(gpu_ids_list):
    gpus = tf.config.list_physical_devices('GPU')
    if gpus:
        try:
            gpus_used = [gpus[i] for i in gpu_ids_list]
            tf.config.set_visible_devices(gpus_used, 'GPU')
            logical_gpus = tf.config.experimental.list_logical_devices('GPU')
            print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
        except RuntimeError as e:
            # Visible devices must be set before GPUs have been initialized
            print(e)

Suppose you are on a system with 4 GPUs and you want to use only two GPUs, the one with id = 0 and the one with id = 2, then the first command of your code, immediately after importing the libraries, would be:

set_gpu([0, 2])

In your case, to use only the CPU, you can invoke the function with an empty list:

set_gpu([])

For completeness, if you want to avoid that the runtime initialization will allocate all memory on the device, you can use tf.config.experimental.set_memory_growth. Finally, the function to manage which devices to use, occupying the GPUs memory dynamically, becomes:

import tensorflow as tf

def set_gpu(gpu_ids_list):
    gpus = tf.config.list_physical_devices('GPU')
    if gpus:
        try:
            gpus_used = [gpus[i] for i in gpu_ids_list]
            tf.config.set_visible_devices(gpus_used, 'GPU')
            for gpu in gpus_used:
                tf.config.experimental.set_memory_growth(gpu, True)
            logical_gpus = tf.config.experimental.list_logical_devices('GPU')
            print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPU")
        except RuntimeError as e:
            # Visible devices must be set before GPUs have been initialized
            print(e)