How to tell if tensorflow is using gpu acceleration from inside python shell? How to tell if tensorflow is using gpu acceleration from inside python shell? python python

How to tell if tensorflow is using gpu acceleration from inside python shell?


No, I don't think "open CUDA library" is enough to tell, because different nodes of the graph may be on different devices.

When using tensorflow2:

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

For tensorflow1, to find out which device is used, you can enable log device placement like this:

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Check your console for this type of output.


Apart from using sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) which is outlined in other answers as well as in the official TensorFlow documentation, you can try to assign a computation to the gpu and see whether you have an error.

import tensorflow as tfwith 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.Session() as sess:    print (sess.run(c))

Here

  • "/cpu:0": The CPU of your machine.
  • "/gpu:0": The GPU of your machine, if you have one.

If you have a gpu and can use it, you will see the result. Otherwise you will see an error with a long stacktrace. In the end you will have something like this:

Cannot assign a device to node 'MatMul': Could not satisfy explicit device specification '/device:GPU:0' because no devices matching that specification are registered in this process


Recently a few helpful functions appeared in TF:

You can also check for available devices in the session:

with tf.Session() as sess:  devices = sess.list_devices()

devices will return you something like

[_DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:CPU:0, CPU, -1, 4670268618893924978), _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 6127825144471676437), _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 16148453971365832732), _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 10003582050679337480), _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 5678397037036584928)


Following piece of code should give you all devices available to tensorflow.

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

Sample Output

[name: "/cpu:0" device_type: "CPU" memory_limit: 268435456 locality { } incarnation: 4402277519343584096,

name: "/gpu:0" device_type: "GPU" memory_limit: 6772842168 locality { bus_id: 1 } incarnation: 7471795903849088328 physical_device_desc: "device: 0, name: GeForce GTX 1070, pci bus id: 0000:05:00.0" ]


matomo