ariadne-split

Create a Python-based mini-application that leverages the 'ariadne-split' package to enhance the training efficiency of a simple PyTorch model on a dataset. This application will serve as a practical demonstration of dynamic batch splitting techniques during both replay and training phases. The goal is to showcase how these techniques can improve model performance on sequential data, such as time-series predictions or natural language processing tasks.

Steps to follow:
1. Choose a dataset suitable for sequence prediction, such as stock market prices or weather forecasts.
2. Define a basic recurrent neural network (RNN) model using PyTorch that will be trained on the chosen dataset.
3. Integrate the 'ariadne-split' package into your project to enable dynamic batch splitting during training.
4. Implement a function that splits the dataset into batches and applies dynamic batching techniques using 'ariadne-split'.
5. Train the RNN model using the dynamic batching approach and compare its performance against a baseline model trained without dynamic batching.
6. Include a feature to visualize the training process and model performance over time.
7. Write comprehensive documentation explaining each part of the code, the role of 'ariadne-split', and how dynamic batching improves the training process.
8. Ensure the application is user-friendly, allowing users to adjust parameters like learning rate, batch size, and sequence length to observe their impact on training efficiency and model accuracy.

Suggested Features:
- Real-time visualization of loss and accuracy metrics.
- Comparison graphs between the baseline model and the dynamically batched model.
- Interactive interface for parameter tuning.
- Detailed logs of the training process.

By completing this project, you'll gain hands-on experience with advanced training techniques for sequential data and understand how tools like 'ariadne-split' can optimize deep learning workflows.