How do I check if keras is using gpu version of tensorflow?

To verify whether Keras is using the GPU version of TensorFlow, follow these steps:

1. Check TensorFlow Version

   First, confirm that the installed TensorFlow version supports GPU. Use the following code to check the TensorFlow version:

   python
   import tensorflow as tf
   print(tf.__version__)

   Ensure the version is TensorFlow 1.x (1.4 or higher) or TensorFlow 2.x, as these versions automatically support GPU when CUDA and cuDNN are correctly installed.

2. Check GPU Availability

   Next, use TensorFlow's methods to verify if GPU is detected. You can use the following code snippet:

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

   Alternatively, use a simpler approach:

   python
   print(tf.config.list_physical_devices('GPU'))

   If the output includes GPU-related information (e.g., devices with 'GPU' in their name), it confirms TensorFlow is utilizing the GPU.

3. Run a Simple TensorFlow Operation to Observe GPU Utilization

   Execute a basic TensorFlow computation and monitor GPU utilization using the system Task Manager (on Windows) or commands (e.g., nvidia-smi on Linux). Here is a simple TensorFlow computation example:

   python
   tf.random.normal([1000, 1000]).gpu()

   After running this code, observe GPU utilization. A significant increase typically indicates TensorFlow is using the GPU for computation.

4. Check Keras Backend

   Although Keras is a high-level neural network API, it typically uses TensorFlow as its computational backend. Check the current backend library with the following code:

   python
   from keras import backend as K
   print(K.backend())

   If the output is 'tensorflow', Keras is using TensorFlow as the backend. Combined with the previous steps, this confirms Keras is also leveraging the GPU.

By following these steps, you can systematically verify whether Keras is using the GPU version of TensorFlow. These steps ensure your model training process effectively utilizes GPU resources, thereby enhancing training speed and efficiency.