amd-torch-device-gfx1031

Create a Python-based mini-application that leverages the 'amd-torch-device-gfx1031' package to optimize deep learning model training on AMD GPUs. Your application should be able to automatically detect if an AMD GPU with the GFX1031 architecture is available, and if so, configure PyTorch to use it for accelerating the training of neural networks. Here are the key steps and features your application should include:

1. **Project Setup**: Initialize a new Python project and install necessary dependencies including 'torch', 'amd-torch-device-gfx1031', and any other required libraries.
2. **Device Detection**: Implement a function to check for the presence of an AMD GPU with GFX1031 architecture. If found, set up PyTorch to utilize this specific device for computations.
3. **Model Training**: Design a simple neural network using PyTorch and train it on a dataset of your choice. Ensure that the training process takes advantage of the detected AMD GPU if available.
4. **Performance Metrics**: Integrate functionality to measure and display the performance gains achieved by using the AMD GPU over CPU-only training.
5. **User Interface**: Develop a basic command-line interface (CLI) through which users can interact with your application. This should allow them to start the training process, view current status, and see training results.
6. **Documentation**: Write comprehensive documentation explaining how to install and run the application, along with a brief overview of how 'amd-torch-device-gfx1031' enhances PyTorch's capabilities on AMD GPUs.

The goal is to showcase the efficiency and ease of integrating specialized hardware acceleration into deep learning workflows using Python and PyTorch, specifically highlighting the benefits of the 'amd-torch-device-gfx1031' package.