10 examples of 'how to check if tensorflow is using gpu' in Python

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def gpu_available_in_session():
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    sess = tfv1.get_default_session()
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    for dev in sess.list_devices():
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        if dev.device_type.lower() == 'gpu':
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            return True
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    return False

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def is_gpu_available():
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    from tensorflow.python.client import device_lib
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    local_device_protos = device_lib.list_local_devices()
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    gpu_list = [x.name for x in local_device_protos if x.device_type == 'GPU']
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    if len(gpu_list) > 0:
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        print("Tensorflow GPU:", gpu_list)
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        return True
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    else:
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        return False

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def check_cuda_support():
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    """ Check if tensorflow was build with CUDA """
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    return tf.test.is_built_with_cuda()

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def linux_with_gpu():
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    """Returns if machine is running an Linux OS and has a GPU"""
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    has_gpu = is_available()
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    return is_linux() and has_gpu

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def gpu_no_of_var(var):
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    """
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    Function that returns the GPU number or whether the tensor is on GPU or not
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    Args:
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        var: torch tensor
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    Returns:
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        The CUDA device that the torch tensor is on, or whether the tensor is on GPU
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    """
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    try:
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        is_cuda = next(var.parameters()).is_cuda
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    except:
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        is_cuda = var.is_cuda
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    if is_cuda:
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        try:
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            return next(var.parameters()).get_device()
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        except:
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            return var.get_device()
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    else:
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        return False

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def test_tf_support_gpu_instances(sagemaker_session, tf_version):
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    tf = _build_tf(sagemaker_session, tf_version, train_instance_type="ml.g2.2xlarge")
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    assert tf.train_image() == _get_full_gpu_image_uri(tf_version)
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    tf = _build_tf(sagemaker_session, tf_version, train_instance_type="ml.p2.2xlarge")
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    assert tf.train_image() == _get_full_gpu_image_uri(tf_version)

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def mxnet_prefer_gpu():
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    """If gpu available return gpu, else cpu
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    Returns
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    -------
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    context : Context
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        The preferable GPU context.
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    """
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    gpu = int(os.environ.get('MXNET_GPU', default=0))
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    if gpu in mx.test_utils.list_gpus():
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        return mx.gpu(gpu)
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    return mx.cpu()

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@classmethod
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def supports_device(cls, device):
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  return common_supports_device(device)

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

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def gpu_count():
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    return len(get_available_gpus())