如何在张量流中获取当前可用的GPU？

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?

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.

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.

In TensorFlow 2.0, you can use tf.config.experimental.list_physical_devices('GPU'):

import tensorflow as tf
gpus = tf.config.experimental.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

From 2.1, you can drop experimental:

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

See:

• Guide pages
• Current API

The accepted answer gives you the number of GPUs but it also allocates all the memory on those GPUs. You can avoid this by creating a session with fixed lower memory before calling device_lib.list_local_devices() which may be unwanted for some applications.

I ended up using nvidia-smi to get the number of GPUs without allocating any memory on them.

import subprocess

n = str(subprocess.check_output(["nvidia-smi", "-L"])).count('UUID')

Apart from the excellent explanation by Mrry, where he suggested to use device_lib.list_local_devices() I can show you how you can check for GPU related information from the command line.

Because currently only Nvidia’s gpus work for NN frameworks, the answer covers only them. Nvidia has a page where they document how you can use the /proc filesystem interface to obtain run-time information about the driver, any installed NVIDIA graphics cards, and the AGP status.

/proc/driver/nvidia/gpus/0..N/information

Provide information about each of the installed NVIDIA graphics adapters (model name, IRQ, BIOS version, Bus Type). Note that the BIOS version is only available while X is running.

So you can run this from command line cat /proc/driver/nvidia/gpus/0/information and see information about your first GPU. It is easy to run this from python and also you can check second, third, fourth GPU till it will fail.

Definitely Mrry’s answer is more robust and I am not sure whether my answer will work on non-linux machine, but that Nvidia’s page provide other interesting information, which not many people know about.

The following works in tensorflow 2:

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

From 2.1, you can drop experimental:

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

https://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices

I got a GPU called NVIDIA GTX GeForce 1650 Ti in my machine with tensorflow-gpu==2.2.0

Run the following two lines of code:

import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

Output:

Num GPUs Available:  1

Use this way and check all parts :

from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds


version = tf.__version__
executing_eagerly = tf.executing_eagerly()
hub_version = hub.__version__
available = tf.config.experimental.list_physical_devices("GPU")

print("Version: ", version)
print("Eager mode: ", executing_eagerly)
print("Hub Version: ", h_version)
print("GPU is", "available" if avai else "NOT AVAILABLE")

Ensure you have the latest TensorFlow 2.x GPU installed in your GPU supporting machine, Execute the following code in python,

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf 

print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

Will get an output looks like,

2020-02-07 10:45:37.587838: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-02-07 10:45:37.588896: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0, 1, 2, 3, 4, 5, 6, 7 Num GPUs Available: 8