CODEWITHSUNDEEP
pythontensorflowdeep-learninggpupython-3.7
sineman
sineman

Reputation: 109

Tensorflow identifying GPUs, but not recognizing them under the list of devices

I have installed tensorflow-gpu version 1.15 on my profile on a cluster, which has access to 2 GPUs. I was able to verify this by running

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

The above statements yield the list of local devices as:

[name: "/device:CPU:0"
 device_type: "CPU"
 memory_limit: 268435456
 locality {
 }
 incarnation: 17161457237421390575,
 name: "/device:XLA_CPU:0"
 device_type: "XLA_CPU"
 memory_limit: 17179869184
 locality {
 }
 incarnation: 2136131381156225295
 physical_device_desc: "device: XLA_CPU device",
 name: "/device:XLA_GPU:0"
 device_type: "XLA_GPU"
 memory_limit: 17179869184
 locality {
 }
 incarnation: 5626920946153973344
 physical_device_desc: "device: XLA_GPU device",
 name: "/device:XLA_GPU:1"
 device_type: "XLA_GPU"
 memory_limit: 17179869184
 locality {
 }
 incarnation: 1069390960246559975
 physical_device_desc: "device: XLA_GPU device"]

which clearly shows the GPU devices listed. On further search, I learned that XLA_GPU correlates to a GPU capable of supporting tensorflow linear algebra routines. However, when I run the GPU test function

tf.test.is_gpu_available()

the output is False. I'm confused as to whether the GPU is not being detected here, or there is an issue with the tensorflow-gpu installation (which was through pip). Any inputs on this would be appreciated.

Upvotes: 2

Views: 4085

Answers (1)

user11530462
user11530462

Reputation:

The recommended way is to check if TensorFlow is using GPU is the following:

tf.config.list_physical_devices('GPU')

Output:

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

The following will also return the name of your GPU devices.

import tensorflow as tf
tf.test.gpu_device_name()

If a non-GPU version of the package is installed, the function would also return False. Use tf.test.is_built_with_cuda to validate if TensorFlow was build with CUDA support.

Note: tf.test.is_gpu_available is deprecated. Please refer here

Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.config.list_physical_devices('GPU') instead.

Best way to test is to run code and check that GPU is using with nvidia-smi as mentioned by Matias Valdenegro or run simple code as below

import tensorflow as tf
with tf.device('/GPU:0'):
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
    c = tf.matmul(a, b)

with tf.compat.v1.Session() as sess:
    print (sess.run(c))

Output:

[[22. 28.]
 [49. 64.]]

Upvotes: 3

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