如何在CPU上运行Tensorflow

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"):

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

You can also set the environment variable to

CUDA_VISIBLE_DEVICES=""

without having to modify the source code.

If the above answers don’t work, try:

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

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

Just using the code below.

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

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.

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)

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. Bonus points for using a venv like explained eg in this answer.