Exam Professional Data Engineer topic 1 question 127 discussion

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.

C. Use Cloud GPUs after implementing GPU kernel support for your customs ops. Here's why: Your current bottleneck is in the custom C++ TensorFlow ops performing matrix multiplications, which are computationally intensive operations. GPUs are specifically designed to efficiently handle matrix operations and would significantly speed up your training time compared to CPUs. Since you've developed custom C++ TensorFlow ops, you'll need to implement GPU kernel support for these operations to take advantage of GPU acceleration.

According to the official documentation. Models that contain many custom TensorFlow operations written in C++ should keep using CPUs.

I think this is D. I recently did the ML professional exam and they ask that there, and it's always "c++ custom ops = CPU", it's in fact the only scenario for non-small models on CPU. It's written in black and white here: https://cloud.google.com/tpu/docs/intro-to-tpu#when_to_use_tpus, check out the CPU/GPU/TPU "when to use" section.

Why Not Other Options? A. Use Cloud TPUs without any additional adjustment to your code: TPUs are optimized for standard TensorFlow operations and require custom TensorFlow ops to be adapted to TPU-compatible kernels, which is not trivial. Without modifications, your custom C++ ops will not run efficiently on TPUs. B. Use Cloud TPUs after implementing GPU kernel support for your customs ops: Implementing GPU kernel support alone is not sufficient for running on TPUs. TPUs require specific optimizations and adaptations beyond GPU kernels. D. Stay on CPUs, and increase the size of the cluster you're training your model on: While increasing the CPU cluster size might reduce training time, it is not as efficient or cost-effective as using GPUs, especially for matrix multiplication tasks.

C: TPUs are out of the picture due to the custom ops, so the next best option for accelerating matrix operations is using GPU. Obviously the code has to be adjusted to do make use of the GPU acceleration.

CPU : Simple models GPU: Custom TensorFlow/PyTorch/JAX operations

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