machine learning - GPU is not used for calculations despite tensorflow-gpu installed

Question

My computer has the following software installed: Anaconda (3), TensorFlow (GPU), and Keras. There are two Anaconda virtual environments - one with TensorFlow for Python 2.7 and one for 3.5, both GPU version, installed according to the TF instructions. (I had a CPU version of TensorFlow installed previously in a separate environment, but I've deleted it.)

When I run the following:

source activate tensorflow-gpu-3.5
python code.py

and check nvidia-smi it shows only 3MiB GPU Memory Usage by Python, so it looks like GPU is not used for calculations. (code.py is a simple deep Q-learning algorithm implemented with Keras)

Any ideas what can be going wrong?

Answer 1

A good way to debug these problems is to check which operations have been allocated to which devices.

You can check this by passing a configuration parameter to the session:

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

When you run your app, you will see some output indicating which devices are being used.

You can find more information here: https://www.tensorflow.org/tutorials/using_gpu