Developed hardware accelerator availability detection and pre-launch validation for the nerfstudio-project/nerfstudio repository, focusing on improving reliability and user experience in training workflows. Leveraged Python for backend development to implement a get_available_devices function that detects CUDA, MPS, and CPU support, enabling dynamic device management. Integrated robust error handling and pre-launch checks to verify device compatibility before training begins, reducing runtime errors caused by misconfiguration. Enhanced the default device selection logic to better align with available hardware, streamlining onboarding and minimizing friction for users. The work emphasized backend engineering, device management, and error prevention, contributing to safer and faster training starts.