Accelerating Deep Learning Model Training on Cloud Tensor Processing Unit

Deep learning techniques have grown rapidly in recent years due to their success in image classification, speech recognition, and natural language understanding. These techniques have the potential to solve complex problems and are being applied in various fields, such as agriculture, medicine, and administration. However, training large and complex models requires high-performance computational platforms, making accelerator hardware an essential tool and driving up its cost. An alternative solution is to use cloud computing, where users only pay for usage and have access to a wide range of computing resources and services. In this paper, we adapt a Diabetic Retinopathy neural network model for TPU-based training in the cloud and observe promising results, including reduced training time without code optimization. This demonstrates the potential of cloud computing in reducing the burden on local systems that are often overwhelmed by multiple running applications. This allows for training larger and more advanced models at a lower cost than local computational centers.