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_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.

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 

In TensorFlow Core v2.3.0, the following code should work.

import tensorflow as tf visible_devices = tf.config.get_visible_devices() for devices in visible_devices: print(devices) 

Depending on your environment, this code will produce flowing results.

PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU') PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')

latest version recommended by tensorflow:

tf.config.list_physical_devices('GPU') 

I am working on TF-2.1 and torch, so I don't want to specific this automacit choosing in any ML frame. I just use original nvidia-smi and os.environ to get a vacant gpu.

def auto_gpu_selection(usage_max=0.01, mem_max=0.05): """Auto set CUDA_VISIBLE_DEVICES for gpu :param mem_max: max percentage of GPU utility :param usage_max: max percentage of GPU memory :return: """ os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' log = str(subprocess.check_output("nvidia-smi", shell=True)).split(r"\n")[6:-1] gpu = 0 # Maximum of GPUS, 8 is enough for most for i in range(8): idx = i*3 + 2 if idx > log.__len__()-1: break inf = log[idx].split("|") if inf.__len__() < 3: break usage = int(inf[3].split("%")[0].strip()) mem_now = int(str(inf[2].split("/")[0]).strip()[:-3]) mem_all = int(str(inf[2].split("/")[1]).strip()[:-3]) # print("GPU-%d : Usage:[%d%%]" % (gpu, usage)) if usage < 100*usage_max and mem_now < mem_max*mem_all: os.environ["CUDA_VISIBLE_EVICES"] = str(gpu) print("\nAuto choosing vacant GPU-%d : Memory:[%dMiB/%dMiB] , GPU-Util:[%d%%]\n" % (gpu, mem_now, mem_all, usage)) return print("GPU-%d is busy: Memory:[%dMiB/%dMiB] , GPU-Util:[%d%%]" % (gpu, mem_now, mem_all, usage)) gpu += 1 print("\nNo vacant GPU, use CPU instead\n") os.environ["CUDA_VISIBLE_EVICES"] = "-1" 

If I can get any GPU, it will set CUDA_VISIBLE_EVICES to BUSID of that gpu :

GPU-0 is busy: Memory:[5738MiB/11019MiB] , GPU-Util:[60%] GPU-1 is busy: Memory:[9688MiB/11019MiB] , GPU-Util:[78%] Auto choosing vacant GPU-2 : Memory:[1MiB/11019MiB] , GPU-Util:[0%] 

else, set to -1 to use CPU:

GPU-0 is busy: Memory:[8900MiB/11019MiB] , GPU-Util:[95%] GPU-1 is busy: Memory:[4674MiB/11019MiB] , GPU-Util:[35%] GPU-2 is busy: Memory:[9784MiB/11016MiB] , GPU-Util:[74%] No vacant GPU, use CPU instead 

Note: Use this function before you import any ML frame that require a GPU, then it can automatically choose a gpu. Besides, it's easy for you to set multiple tasks.

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

Run the following in any shell

python -c "import tensorflow as tf; print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))" 

You can use the following code fields to show device name, type, memory and locality.

from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) 

The accepted answer gives you the device description like:

['/device:GPU:0'] 

If you want more details you can use tf.config.experimental.get_device_details()

import tensorflow as tf def get_available_gpus(): physical_gpus = tf.config.list_physical_devices(device_type="GPU") return [(x, tf.config.experimental.get_device_details(x)) for x in physical_gpus] 

This will give you details on device_name and compute_capability, e.g.:

[(PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), {'device_name': 'NVIDIA T500', 'compute_capability': (7, 5)})]