Google Professional Machine Learning Engineer Question 105
Select 4Google Cloud PlatformYou are training a large deep learning model on Google Cloud. The training dataset is stored in Google Cloud Storage (GCS), and the model requires significant computational resources to converge. To minimize cost and improve training efficiency, what steps should you take?
- A
Use preemptible VMs for training to reduce compute costs.
- B
Enable mixed-precision training to leverage Tensor Cores on GPUs.
- C
Increase the batch size as much as possible without exceeding the memory limit.
- D
Use a custom training loop to bypass TensorFlow's built-in optimizations.
- E
Cache the dataset in memory to avoid repeatedly fetching it from GCS.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Efficient training on Google Cloud involves optimizing compute costs, leveraging hardware accelerators like GPUs, and minimizing data I/O bottlenecks. Using preemptible VMs, mixed-precision training, appropriate batch sizes, and caching the dataset in memory are all effective strategies. Custom training loops, while useful in some advanced cases, can reduce efficiency if TensorFlow's built-in optimizations are bypassed.
- A. Correct.
Using preemptible VMs can significantly reduce compute costs while still providing the necessary resources for training. However, you need to handle potential interruptions in your training pipeline.
- B. Correct.
Mixed-precision training can improve training speed by taking advantage of hardware accelerators like NVIDIA GPUs with Tensor Cores, which are optimized for 16-bit floating-point operations.
- C. Correct.
Increasing the batch size (while staying within memory limits) can reduce the number of iterations required for convergence, making training more efficient.
- D. Incorrect.
Using a custom training loop is unnecessary in this scenario and may bypass critical TensorFlow optimizations, leading to inefficient training.
- E. Correct.
Caching the dataset in memory avoids repeated data fetches from GCS, reducing latency and improving data throughput during training.