accel-hydra

Create a fully-functional mini-application that leverages the capabilities of the 'accel-hydra' package to train a simple neural network model on a dataset of your choice. This application should demonstrate the flexibility and power of the AccelHydra framework by showcasing its modular and configurable nature. Here are the steps and features to include:

1. **Dataset Preparation**: Select a public dataset that contains numerical data suitable for regression or classification tasks. Preprocess the data as necessary (e.g., normalization, splitting into training and validation sets).
2. **Model Definition**: Define a basic neural network architecture using PyTorch. The architecture should be simple enough to focus on demonstrating the usage of AccelHydra but complex enough to showcase its capabilities.
3. **Configuration Setup**: Utilize Hydra to define a configuration file for the experiment. This file should allow for easy adjustment of hyperparameters such as learning rate, batch size, number of epochs, and optimizer type without modifying the codebase.
4. **Training Loop Implementation**: Implement the training loop using the Accelerate component of AccelHydra. Ensure that the training process is efficient and takes advantage of any available hardware accelerators like GPUs.
5. **Evaluation and Logging**: After each epoch, evaluate the model's performance on the validation set and log the results. Use Hydra's logging capabilities to track these metrics over time.
6. **Experiment Reproducibility**: Demonstrate how different configurations can lead to different outcomes by running multiple experiments with varied settings defined in the configuration file.
7. **Visualization**: Optionally, implement a feature that visualizes the training progress, such as plotting loss curves over time.
8. **Documentation**: Provide clear documentation on how to run the application and modify configurations for different scenarios.

This project aims to serve as a practical example of how AccelHydra can simplify and enhance the process of developing machine learning models, making it easier for developers to experiment and iterate quickly.