How to get current available GPUs in tensorflow?

You can check all device list using following code:

from tensorflow.python.client import device_lib

device_lib.list_local_devices()

There is also a method in the test util. So all that has to be done is:

tf.test.is_gpu_available()

and/or

tf.test.gpu_device_name()

Look up the Tensorflow docs for arguments.

Since TensorFlow 2.1, you can use tf.config.list_physical_devices('GPU'):

import tensorflow as tf

gpus = tf.config.list_physical_devices('GPU')
for gpu in gpus:
    print("Name:", gpu.name, "  Type:", gpu.device_type)

If you have two GPUs installed, it outputs this:

Name: /physical_device:GPU:0   Type: GPU
Name: /physical_device:GPU:1   Type: GPU

In TF 2.0, you must add experimental:

gpus = tf.config.experimental.list_physical_devices('GPU')

See:

• Guide pages
• Current API

There is an undocumented method called device_lib.list_local_devices() that enables you to list the devices available in the local process. (N.B. As an undocumented method, this is subject to backwards incompatible changes.) The function returns a list of DeviceAttributes protocol buffer objects. You can extract a list of string device names for the GPU devices as follows:

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

Note that (at least up to TensorFlow 1.4), calling device_lib.list_local_devices() will run some initialization code that, by default, will allocate all of the GPU memory on all of the devices (GitHub issue). To avoid this, first create a session with an explicitly small per_process_gpu_fraction, or allow_growth=True, to prevent all of the memory being allocated. See this question for more details.