How do I know if Tensorflow GPU is installed?

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To check if Tensorflow is using a GPU, you can use the config.list_physical_devices('GPU') in Tensorflow. This method returns True if a GPU is available and False if not. Here is an example of how to use it:

import tensorflow as tf
#Check if Tensorflow is using a GPU 
if tf.config.list_physical_devices('GPU'):    
    print("Tensorflow is using a GPU")
else: 
     print("Tensorflow is not using a GPU")

Following will be the output if you have GPU:

Tensorflow is using a GPU
If you want to check which GPUs are available, you can use the GPUOptions class and the list_physical_devices() method from the DeviceManager class. Here is an example:

import tensorflow as tf 

# Get the list of available GPUs 
gpus = tf.config.experimental.list_physical_devices('GPU') 

# Print the list of available GPUs 
print(gpus)
The output of the above program will be as follow:
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
This will print a list of available GPUs, if any. If no GPUs are available, it will print an empty list.

You could also use the following example: 
import tensorflow as tf
tf.config.list_physical_devices('GPU')
Output:
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

You could run the following command to find the information about the GPU that TensorFlow is using:

! nvidia-smi

Output:

You will get output similar to the following:

Sun Dec 11 10:56:52 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   44C    P8    11W /  70W |      3MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

The above table provides the information about the GPU currently in use.
To check all devices available to the TensorFlow, you could use the following:

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

Output:

[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 6268366153253263738
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 14415560704
locality {
  bus_id: 1
  links {
  }
}
incarnation: 17524716795020899435
physical_device_desc: "device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5"
xla_global_id: 416903419
]

In the above output the all devices are listed. You may notice different output based on the available devices on your machine.

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