Google Professional Machine Learning Engineer Question 283
Select 2Google Cloud PlatformYou are tasked with training a deep learning model on a large dataset containing millions of high-resolution images. The model architecture includes multiple convolutional layers and requires significant computational power. Which of the following considerations should guide your choice of hardware for training this model?
- A
Use GPUs with high memory capacity to support the processing of large batches of high-resolution images.
- B
Choose CPUs with multiple cores, as they are optimized for parallel training of deep learning models.
- C
Leverage TPUs if the deep learning framework you are using supports them, as they are specifically optimized for matrix computations and large-scale model training.
- D
Prioritize hardware with high disk I/O speed, as this will significantly accelerate training.
- E
Select GPUs with lower memory to minimize costs, as memory size does not impact the ability to train large models.
Show answer and explanation
Correct answers: A, C
Explanation
When training deep learning models on large datasets with high computational requirements, it is critical to select hardware optimized for the task. GPUs with high memory capacity and TPUs are designed for such workloads, as they can handle parallelized matrix computations efficiently and support large-scale deep learning training. CPUs and disk I/O speed are less critical, and prioritizing low-memory GPUs can hinder training performance despite cost savings.
- A. Correct.
GPUs with high memory capacity are essential for handling large batches of high-resolution images and for efficiently processing the computations required by convolutional layers.
- B. Incorrect.
While CPUs with multiple cores are useful for certain types of workloads, they are not optimized for the parallelized matrix computations typical in deep learning, making them less suitable for training deep learning models.
- C. Correct.
TPUs are specifically designed for deep learning workloads, offering significant acceleration for matrix computations and large-scale training, provided the framework being used supports them.
- D. Incorrect.
While disk I/O speed is important for data loading, it does not directly impact the computational training of the model. Instead, caching and data preprocessing pipelines can mitigate I/O bottlenecks.
- E. Incorrect.
GPUs with lower memory may reduce costs but could lead to out-of-memory errors or require smaller batch sizes, which can negatively impact training efficiency and performance.