amd-torch-device-gfx1033

Create a mini-application that leverages the 'amd-torch-device-gfx1033' Python package to optimize and run PyTorch models specifically on AMD GPUs with the GFX1033 architecture. This application will serve as a proof-of-concept to showcase the benefits of using specialized hardware and software configurations for deep learning tasks. Here are the steps and features your project should include:

1. **Setup Environment**: Ensure that the environment is properly set up with the necessary dependencies, including 'amd-torch-device-gfx1033', PyTorch, and any other required libraries.
2. **Model Selection**: Choose a pre-trained PyTorch model (e.g., ResNet, VGG) that can be used for image classification tasks.
3. **Optimization Workflow**: Implement an optimization workflow that utilizes 'amd-torch-device-gfx1033' to fine-tune the selected model for the specific GPU architecture. This could involve adjusting parameters such as memory allocation, parallel processing capabilities, etc.
4. **Performance Benchmarking**: Develop a feature that allows users to benchmark the performance of the optimized model against the original, non-optimized version. Metrics like inference time and accuracy should be collected and displayed.
5. **User Interface**: Create a simple user interface where users can upload images, select between the optimized and non-optimized models, and see real-time results and performance metrics.
6. **Documentation**: Provide comprehensive documentation explaining each step of the process, from setup to usage, highlighting how 'amd-torch-device-gfx1033' enhances the performance of deep learning models on AMD GPUs.