Introduction

Keras, a popular high-level neural networks API, is often used with TensorFlow as its backend due to TensorFlow's extensive capabilities for deep learning. One of the fantastic features of this combination is the ability to leverage computational resources effectively—whether it be a powerful GPU or a CPU. Sometimes, developers may explicitly want to control which computational device Keras should use, based on various constraints or optimization strategies. This article provides a detailed exploration of how one can force Keras to use either a CPU or a GPU.

Why Control CPU/GPU Usage?

Before diving into how you can control the device usage, it is essential to understand why you may need to:

1. Resource Availability: Not all hardware configurations have a dedicated GPU. Forcing operations to run on a CPU is necessary for such environments.
2. Debugging: Debugging on CPU might be easier due to more predictable execution compared to parallel operations on GPUs.
3. Comparison: Running models on both CPU and GPU can help compare performance metrics and manage resource allocation in a shared environment.
4. Cost Management: In cloud environments, GPUs can be more expensive than CPUs. Efficiently managing resources can significantly reduce computing costs.

Enabling and Disabling GPU Usage

Controlling Devices with Environment Variables

You can control hardware usage by setting environment variables before importing any TensorFlow or Keras modules.

• Force CPU: Set the CUDA_VISIBLE_DEVICES environment variable to an empty string.
  Example (in Python):

1  import os
2  os.environ['CUDA_VISIBLE_DEVICES'] = ''
3  import tensorflow as tf
4  from tensorflow import keras

• Force Specific GPU: If you have multiple GPUs and want to use a specific one, set the variable to the GPU's device ID.
  Example:

  os.environ['CUDA_VISIBLE_DEVICES'] = '0'  # Forces TensorFlow to use only the first GPU

TensorFlow Device Context

For more granular control within the code, use TensorFlow's tf.device context manager to specify CPU or GPU for specific operations.

Example:

1import tensorflow as tf
2
3with tf.device('/CPU:0'):
4    # Code here will run on CPU
5    model = create_model()
6    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
7
8with tf.device('/GPU:0'):
9    # Code here will run on the first GPU
10    model.fit(training_data, training_labels)

Configuring Keras Policy

Keras and TensorFlow also provide global settings that affect device placement.

• Keras Policy: With TensorFlow 2.x and above, you can set global mixed precision policies that determine whether computations are performed with float32 or mixed precision, which can affect device selection implicitly, as GPUs often provide better hardware acceleration for mixed precision.

1  from tensorflow.keras import mixed_precision
2
3  # Configure to use mixed precision
4  mixed_precision.set_global_policy('mixed_float16')

Key Points Summary

Additional Considerations

Installation Dependencies

• Ensure the compatible CUDA and cuDNN versions are installed. TensorFlow requires these for GPU acceleration.
• Verify that your TensorFlow installation includes GPU support. You can check this with:

  tf.test.is_built_with_cuda()

Monitoring and Debugging

Monitor usage to ensure the execution is happening on the desired device:

• TensorFlow Profiler can give insights into which device is being used for which operation.
• The nvidia-smi tool helps monitor GPU usage externally.

nvidia-smi

Conclusion

Forcing Keras with TensorFlow backend to use CPU or GPU gives you precise control over your model's execution environment. This can lead to more efficient resource utilization, effective debugging, and better performance optimization. Understanding how to manipulate device placement is an essential skill for any deep learning practitioner looking to harness the full power of their available computational resources.