Google Professional Machine Learning Engineer Question 281
Single answerGoogle Cloud PlatformYou are training a large-scale deep learning model on Google Cloud. The model involves complex matrix multiplications and requires accelerating the training process. Additionally, the dataset size exceeds the memory of a single machine. Which hardware configuration would be the most appropriate for this scenario?
- A
Use a Google Compute Engine instance with standard CPUs and local SSDs.
- B
Use a Google Kubernetes Engine (GKE) cluster with preemptible VMs and standard CPUs.
- C
Use a Google Cloud TPU Pod with sufficient resources to handle your dataset and model.
- D
Use a Google Compute Engine instance with GPUs and high-memory machine types.
Show answer and explanation
Correct answer: C
Explanation
Training large-scale deep learning models requires hardware optimized for high computational throughput and memory distribution. Google Cloud TPU Pods are specifically designed to handle such workloads, offering state-of-the-art performance for complex matrix operations and the ability to scale across multiple nodes. While GPUs can also accelerate training, TPUs are more efficient for these scenarios due to their architecture and integration with TensorFlow.
- A. Incorrect.
CPUs with local SSDs are not optimized for training large-scale deep learning models that require high computational power and memory. They are more suited for general-purpose workloads.
- B. Incorrect.
Standard CPUs in a GKE cluster might provide scalability but are not designed for the computational requirements of large-scale deep learning models. Preemptible VMs can also cause interruptions, which may not be desirable for long training jobs.
- C. Correct.
Cloud TPU Pods are specifically designed for large-scale deep learning tasks, offering significant acceleration for matrix computations and seamless scalability for large datasets.
- D. Incorrect.
GPUs with high-memory machine types can accelerate training for many machine learning models. However, for extremely large-scale deep learning tasks requiring massive computation and memory distribution (e.g., a dataset exceeding the memory of a single machine), TPUs are a better fit.