Docker Image Layering: Stop Re Downloading Heavy Dependencies (pytorch, Rocm, CUDA)

This project focuses on improving the efficiency of Docker images by implementing a layering strategy that minimizes the redundant downloading of heavy dependencies. It addresses common challenges faced by developers working with machine learning frameworks and GPU computing environments, specifically targeting the integration of PyTorch, ROCm, and CUDA. By leveraging Docker's layered architecture, it reduces build times and storage requirements, making it easier for developers to manage their environments.

Key technologies utilized in this project include Docker, along with the specific libraries and frameworks mentioned. The approach taken allows for faster iterations during development and can significantly enhance productivity for data scientists and machine learning engineers. This project is particularly useful for those who frequently switch between different machine learning projects and need to maintain efficient and consistent environments without incurring the overhead of downloading large files repeatedly.