["python","gpu","tensorflow"]
How to get current available GPUs in tensorflow?

I have a plan to use distributed TensorFlow, and I saw TensorFlow can use GPUs for training and testing. In a cluster environment, each machine could have 0 or 1 or more GPUs, and I want to run my TensorFlow graph into GPUs on as many machines as possible. I found that when running tf.Session() TensorFlow gives information about GPU in the log messages like below: I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0) My question is how do I get information about current available GPU from TensorFlow? I can get loaded GPU information from the log, but I want to do it in a more sophisticated, programmatic way. I also could restrict GPUs intentionally using the CUDA_VISIBLE_DEVICES environment variable, so I don't want to know a way of getting GPU information from OS kernel. In short, I want a function like tf.get_available_gpus() that will return ['/gpu:0', '/gpu:1'] if there are two GPUs available in the machine. How can I implement this?
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