Google Professional Machine Learning Engineer Question 271
Select 3Google Cloud PlatformYou are training a deep learning model on Google Cloud AI Platform (Vertex AI) and encounter a training failure. The error logs indicate 'Out of memory error' during the training process. What actions could you take to resolve the issue?
- A
Reduce the batch size used during training.
- B
Increase the memory resources of the training machine by selecting a larger machine type.
- C
Enable TPU training to offload memory-intensive computations.
- D
Use mixed precision training to reduce memory consumption.
- E
Disable data augmentation in your training pipeline.
Show answer and explanation
Correct answers: A, B, D
Explanation
Out-of-memory errors during model training are typically caused by excessive memory requirements. The most effective solutions include reducing the batch size, increasing the training machine's memory capacity, or using techniques like mixed precision training to optimize memory usage. While enabling TPUs or disabling data augmentation may have other benefits, they do not directly solve memory-related issues in the training process.
- A. Correct.
Reducing the batch size decreases the memory required for processing each batch, which can help alleviate out-of-memory errors.
- B. Correct.
Increasing the memory resources by selecting a larger machine type (e.g., switching to a machine with more RAM or more GPUs) can provide sufficient memory for the training process.
- C. Incorrect.
While TPUs can speed up training, they do not necessarily solve memory-related issues unless paired with proper memory optimizations.
- D. Correct.
Mixed precision training reduces memory consumption by using lower precision (e.g., FP16 instead of FP32), which can help mitigate out-of-memory errors.
- E. Incorrect.
Disabling data augmentation does not directly address memory issues, as the primary cause is related to model and batch size rather than preprocessing steps.