When working with deep learning models in Keras and TensorFlow, understanding whether your backend is using a GPU or CPU is critical for optimizing performance and resource utilization. This becomes especially crucial in the case of computationally heavy model training and inference. This guide provides an approach to determine whether your Keras with TensorFlow backend is operating on GPU or CPU and offers additional insights into utilizing these resources effectively.

Checking the Device Type in TensorFlow

TensorFlow provides utilities to identify the type of device being used (CPU or GPU). Here is a step-by-step method to check whether TensorFlow is using a GPU or CPU.

Step 1: Import Necessary Libraries

Start by ensuring that the necessary libraries (tensorflow for backend operations and keras for high-level API) are imported as follows:

import tensorflow as tf
from tensorflow import keras

Step 2: List Physical Devices

You can use the list_physical_devices method provided by tf.config to list available hardware devices:

1gpus = tf.config.list_physical_devices('GPU')
2cpus = tf.config.list_physical_devices('CPU')
3
4print("GPUs: ", gpus)
5print("CPUs: ", cpus)

This script prints a list of available GPUs and CPUs. If the GPU list is empty, it indicates that TensorFlow is running on a CPU.

Step 3: Check for GPU Availability

Alternatively, the tf.test.is_gpu_available() function checks for GPU availability:

gpu_available = tf.test.is_gpu_available()
print("Is GPU available: ", gpu_available)

Note: As of TensorFlow 2.1, the use of tf.test.is_gpu_available() is deprecated in favor of tf.config.list_physical_devices('GPU').

Step 4: Device Context Management

To ensure that certain operations run on a specific device, you can use the with tf.device(...): context manager:

1with tf.device('/GPU:0'):
2    a = tf.constant([[1.0, 2.0], [3.0, 4.0]])
3    b = tf.constant([[1.0, 1.0], [1.0, 1.0]])
4    c = a @ b

This forces the operation to run on the designated GPU or CPU.

Technical Notes

1. GPU Utilization: TensorFlow, by default, attempts to use all visible GPUs. However, in some cases, especially in multi-GPU setups, specifying a specific GPU may be necessary.
2. TensorFlow Environment Variables: TensorFlow behavior can be influenced by environment variables such as CUDA_VISIBLE_DEVICES, which restricts device visibility to TensorFlow.

Common Issues and Troubleshooting

• Compatibility and Drivers: Ensure that your GPU drivers and CUDA, cuDNN libraries are correctly installed and compatible with your TensorFlow version.
• TensorFlow Version: Confirm that your installed TensorFlow version supports GPU. TensorFlow provides separate packages for GPU support such as tensorflow-gpu. From TensorFlow 2.0 onwards, the base package includes both CPU and GPU support, provided the appropriate libraries are available.

Summary Table

Conclusion

Determining whether your Keras with TensorFlow backend is using a GPU or CPU can be done efficiently using TensorFlow's built-in functions. This insight not only helps in ensuring the efficient utilization of resources but also aids in debugging issues related to model performance. Always keep in mind the compatibility requirements and best practices to facilitate hassle-free machine learning workflows.