How to get current available GPUs in tensorflow?

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_libdef 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.

You can check all device list using following code:

from tensorflow.python.client import device_libdevice_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.