TensorFlow Distributed Training Example with Beans Dataset

Overview

This code provides a short example for testing and benchmarking the TensorFlow framework's capabilities in distributed training using the beans dataset.

• Loads the "beans" dataset using TensorFlow Datasets (TFDS).
• Implements a basic convolutional neural network (CNN) model for image classification.
• Employs distributed training with MirroredStrategy (potentially using multiple GPUs).
• Demonstrates data wrangling and augmentation techniques for training.

Note

This is a simplified example and might not be suitable for production use cases. It's intended to showcase core functionalities for getting started with distributed training in TensorFlow.

Running the script

1. Ensure you have TensorFlow and TensorFlow Datasets installed (`pip install tensorflow tensorflow-datasets`).
2. Download the "beans" dataset using TFDS (instructions might be needed depending on the dataset).
3. Adjust hyperparameters (e.g., epochs, batch size) if needed.
4. Run the script: `python your_script_name.py`

Further considerations

• This example uses a basic CNN architecture. Explore more advanced architectures and parameter tuning for better performance on your specific task.
• Consider using more robust data augmentation techniques for improved model generalization.
• This script focuses on demonstrating distributed training. Explore additional functionalities like model saving, loading, and evaluation for complete training workflows.