Google Professional Machine Learning Engineer Question 108
Select 4Google Cloud PlatformYou are training a deep learning model on Google Cloud for a large dataset. The model requires significant computational resources, and you aim to optimize training efficiency while minimizing costs. Which of the following approaches should you implement?
- A
Use a preemptible VM instance for training.
- B
Leverage TPU Pods and adjust the batch size to maximize utilization.
- C
Implement learning rate scheduling to dynamically adjust the learning rate during training.
- D
Use a custom-built local server instead of Google Cloud resources to reduce costs.
- E
Enable mixed-precision training to reduce GPU memory usage and speed up computation.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Efficient training on Google Cloud involves leveraging cost-effective and high-performance tools such as preemptible VMs, TPUs, and mixed-precision training. Additionally, techniques like learning rate scheduling enhance model convergence. Avoiding cloud resources, such as by using a local server, may not leverage the scalability and efficiency benefits of Google Cloud.
- A. Correct.
Preemptible VMs are cost-effective for training tasks that can handle interruptions, making them a good choice for reducing overall costs.
- B. Correct.
TPU Pods can significantly speed up training for large datasets when properly utilized, and adjusting the batch size ensures optimal resource usage.
- C. Correct.
Learning rate scheduling improves training efficiency by adapting the learning rate based on the current training phase, leading to faster convergence.
- D. Incorrect.
Using a custom-built local server might reduce costs, but it would likely sacrifice scalability, efficiency, and access to optimized hardware available on Google Cloud.
- E. Correct.
Mixed-precision training reduces memory usage and speeds up computation by using lower precision for certain operations without significantly impacting accuracy.