Cost effective - among the choices, it is cheaper to have a temporary accelerator instead of increasing our VM cost for an indefinite amount of time D -> TPU accelerator cannot support custom operations

key pjrse is "run partially on a CPU" from https://cloud.google.com/tpu/docs/intro-to-tpu#when_to_use_tpus refers to GPU

The best way to reduce the TensorFlow training time in a cost-effective manner is to use a VM with a GPU hardware accelerator. TensorFlow can take advantage of GPUs to significantly speed up training time for many models. Specifically, option C is the best choice. Changing the VM to another standard type like n2-highmem-32 or e2-standard-32 (options A and B) may provide some improvement, but likely not a significant speedup. Using a TPU (option D) could speed up training, but TPUs are more costly than GPUs. For a cost-effective solution, GPU acceleration provides the best performance per dollar. Since the model must run partially on CPUs, a VM instance with GPUs added will allow TensorFlow to offload appropriate operations to the GPUs while keeping CPU-specific operations on the CPU. This can provide a significant reduction in training time for many common TensorFlow models while keeping costs reasonable

https://cloud.google.com/tpu/docs/intro-to-tpu#when_to_use_tpus

C. Train the model using a VM with a GPU hardware accelerator.

C https://cloud.google.com/tpu/docs/tpus#when_to_use_tpus:~:text=Models%20with%20a%20significant%20number%20of%20custom%20TensorFlow%20operations%20that%20must%20run%20at%20least%20partially%20on%20CPUs

C is the answer. https://cloud.google.com/tpu/docs/tpus#when_to_use_tpus GPUs - Models with a significant number of custom TensorFlow operations that must run at least partially on CPUs

I agree with C, for choosing a GPU one of the cases says: "Models with a significant number of custom TensorFlow operations that must run at least partially on CPUs" https://cloud.google.com/tpu/docs/tpus#when_to_use_tpus