amd-torch-device-gfx1152

Your task is to develop a machine learning model training application using the 'amd-torch-device-gfx1152' package, which is designed to optimize PyTorch operations on AMD GPUs with GFX1152 architecture. This application will allow users to train custom neural network models using their AMD GPU, focusing on image classification tasks. The application should include the following functionalities:

1. User Interface: A simple, intuitive interface that allows users to upload datasets (preferably in CSV or image folder format), select pre-defined neural network architectures (e.g., ResNet, VGG, etc.), and specify training parameters such as batch size, number of epochs, and learning rate.

2. Model Training: Implement functionality to train selected models on uploaded datasets. Ensure that the application leverages the 'amd-torch-device-gfx1152' package to utilize the full potential of the AMD GPU for accelerating training processes.

3. Performance Monitoring: Include real-time performance metrics during training, such as loss values, accuracy, and time taken per epoch. Additionally, provide a summary report at the end of the training session detailing overall performance statistics.

4. Model Evaluation & Saving: After training, evaluate the model's performance on a validation set and display the results. Allow users to save the trained model locally or to cloud storage for future use.

5. Integration with Cloud Storage: Enable integration with cloud storage services like AWS S3 or Google Cloud Storage for uploading datasets and saving trained models.

The application should be built using Python and utilize Flask for the web framework, ensuring it's accessible via a browser. Make sure to document all steps clearly, including how to install dependencies and run the application locally. Emphasize the importance of utilizing the 'amd-torch-device-gfx1152' package to showcase its capabilities in enhancing GPU performance for machine learning tasks.