To achieve real-time data processing, software usually undergoes the complex endeavour of being optimized for specific hardware accelerators, like with the use of CUDA . This is a lengthy process that also requires very specific knowledge and expertise. With the advent of modern machine learning (ML) frameworks like PyTorch and TensorFlow , the landscape of software development is witnessing a transformation. Designed for modularity and efficiency, these frameworks empower a wider audience of researchers to rapidly translate intricate ideas, particularly those centred on tensorial operations, into functional solutions and with a minimal code footprint. This marriage of advanced ML methodologies with scientific computing is what made TORO possible. To draw a comparison, the indexing algorithm XGandalf, primarily crafted in C++, involves an extensive codebase (several thousands of lines) and is limited to CPU processing. In contrast, TORO, developed using PyTorch, is encapsulated within less than 500 lines of high-level Python code. Moreover, the C++ back-end of PyTorch allows for seamless deployment in both C++ and Python projects. Remarkably, despite its concise codebase, TORO delivers indexing performance on par with XGandalf, and yet is versatile enough to operate on GPUs, TPUs, and CPUs alike. With the change of a single line of code, TORO is optimized to run on modern GPUs indexing in parallel large batches of frames, leading to unparalleled indexing performance surpassing the 2kHz regime requirement of modern detectors like JUNGFRAU (4M) . As with most indexing algorithms, TORO offers a trade-off between speed and quality by choosing different hyperparameters. However, even in the fastest setting we studied, the drop in reconstruction quality is barely noticeable.