Your training uses custom C++ TensorFlow ops (heavy matrix multiplications) inside the main loop; training takes days. You want to reduce time and keep costs low using accelerators on Google Cloud. What should you do?

The correct answer is to stay on CPUs and increase the cluster size. The problem states that the custom C++ TensorFlow ops involve "heavy matrix multiplications." While TPUs and GPUs excel at general matrix multiplications, custom C++ ops are typically CPU-bound unless explicitly written with GPU kernel support (e.g., CUDA for NVIDIA GPUs, or specific TPU-optimized kernels). Without such support, these custom ops would execute on the CPU even if a GPU or TPU is present, negating the accelerator's benefits and potentially introducing overhead for data transfer between the CPU and accelerator. Increasing the CPU cluster size allows for distributed training, leveraging more CPU cores to parallelize the workload and reduce overall training time, which is a cost-effective solution given the constraint of keeping costs low and the nature of the custom ops.