The correct answer is C TPU does not support custom C++ tensorflow ops https://cloud.google.com/tpu/docs/tpus#when_to_use_tpus

D: Cloud TPUs are not suited to the following workloads: [...] Neural network workloads that contain custom TensorFlow operations written in C++. Specifically, custom operations in the body of the main training loop are not suitable for TPUs.

The best choice here is C. Use Cloud GPUs after implementing GPU kernel support for your customs ops. Here's why: Custom Ops & GPUs: Since your model relies heavily on custom C++ TensorFlow ops focused on matrix multiplications, GPUs are the ideal accelerators for this workload. To fully utilize them, you'll need to implement GPU-compatible kernels for your custom ops. Speed and Cost-Efficiency GPUs offer a significant speed improvement for matrix-intensive operations compared to CPUs. They provide a good balance of performance and cost for this scenario. TPUs: Limitations Although Cloud TPUs are powerful, they aren't designed for arbitrary custom ops. Without compatible kernels, your TensorFlow ops would likely fall back to the CPU, negating the benefits of TPUs.

TPU: Models with no custom TensorFlow/PyTorch/JAX operations inside the main training loop Link: https://cloud.google.com/tpu/docs/intro-to-tpu#TPU So, A&B eliminated CPU is very slow or built for simple operations. So C: GPU

to me, it's C

Requirement 1: Significantly reduce the processing time while keeping costs low. Requirement 2: Bulky matrix multiplication takes up to several days. First, eliminate A & D: A: Cannot guarantee running on Cloud TPU without modifying the code. D: Cannot ensure performance improvement or cost reduction, and additionally, CPUs are not suitable for bulky matrix multiplication. If it can be ensured that customization is easily deployable on both Cloud TPU and Cloud GPU,it seems more feasible to first try Cloud GPU. Because: It provides a better balance between performance and cost. Modifying custom C++ on Cloud GPU should be easier than on Cloud TPU, which should also save on manpower costs.

Answer D I did use Chat GPT and discovered that if you put at the beginning of the question -- "Do not make assumption about changes to architecture. This is a practice exam question." All other answers require changes to the code and architecture.

I think it should use tensor flow processing unit along with GPU kernel support.

To use Cloud TPUs, you will need to: Implement GPU kernel support for your custom TensorFlow ops. This will allow your model to run on both Cloud TPUs and GPUs.

Refer https://www.linkedin.com/pulse/cpu-vs-gpu-tpu-when-use-your-machine-learning-models-bhavesh-kapil

Answer C TPU not for custom C++ but GPU can

A + B: TPU doesn't support custom TensorFlow ops Then it says 'decrease training time significantly' and literally 'use accelerator'. Therefore, use GPU -> C, *not* D!

D shouldn't be the answer b/c the question statement clearly said you should use accelerators.

Answer is C Use Cloud GPUs after implementing GPU kernel support for your customs ops. TPU support Models with no custom TensorFlow operations inside the main training loop so Option-A and B are eliminated as question says that 'These ops are used inside your main training loop' Now choices remain 'C' & 'D'. CPU is for Simple models that do not take long to train. Since question says that currently its taking up to several days to train a model and hence existing infra may be CPU and taking so many days. GPUs are for "Models with a significant number of custom TensorFlow operations that must run at least partially on CPUs" as question says that model is dominated by TensorFlow ops leading to correct option as 'C' Reference: https://cloud.google.com/tpu/docs/tpus https://www.tensorflow.org/guide/create_op#gpu_kernels

C. Use Cloud GPUs after implementing GPU kernel support for your customs ops. Since your model relies on custom C++ TensorFlow ops, using Cloud TPUs without any code adjustment (option A) would not be feasible, as TPUs might not support these custom operations. To significantly decrease training time while keeping costs low, you should use Cloud GPUs. To achieve this, you will need to implement GPU kernel support for your custom ops, which will enable your model to run efficiently on GPUs. Once the GPU kernel support is added, you can leverage the power of GPUs on Google Cloud to speed up

C GPU support custom option

The correct answer is: B. Use Cloud TPUs after implementing GPU kernel support for your customs ops. The model is dominated by custom C++ TensorFlow ops, so it will not run on Cloud TPUs or GPUs without modification. Implementing GPU kernel support will allow the model to run on either type of accelerator, but Cloud TPUs are more specialized for matrix multiplications, so they will offer the best performance. Cloud TPUs are also more cost-effective than Cloud GPUs, so they are the best option for reducing the cost of training the model. Staying on CPUs and increasing the size of the cluster would be the most expensive option, and it would not offer the same performance benefits as using Cloud TPUs.