MLA-C01 Question 164
Select 3You are training a deep learning model on AWS SageMaker, and the training process is taking longer than anticipated. You want to reduce the model training time while maintaining an acceptable level of model performance. Which of the following methods could help you achieve this goal?
- A
Enable early stopping to terminate training when the model performance stops improving on the validation set.
- B
Use SageMaker's managed Spot Training to leverage lower-cost, interruptible instances.
- C
Distribute the training workload across multiple GPUs using SageMaker's distributed training capabilities.
- D
Increase the dataset size to ensure the model generalizes better and reduces overfitting.
- E
Optimize the model architecture by reducing the number of layers or parameters.
Show answer and explanation
Correct answers: A, C, E
Explanation
To reduce model training time, techniques like early stopping, distributed training, and optimizing the model architecture are effective. Early stopping avoids unnecessary epochs, distributed training leverages parallel computing resources, and simplifying the model reduces computational complexity. While Spot Training reduces costs, it does not directly reduce training time, and increasing the dataset size may improve generalization but increases training time.
- A. Correct.
Enabling early stopping is a common technique to reduce training time by terminating the training process when the model performance on the validation set stops improving. This avoids unnecessary epochs and saves compute time.
- B. Incorrect.
Using managed Spot Training can reduce costs but does not directly reduce training time. Spot instances can also be interrupted, which could potentially increase total wall clock time if the training job is paused and resumed.
- C. Correct.
Distributing the training workload across multiple GPUs or instances can significantly reduce the model training time by parallelizing computations.
- D. Incorrect.
Increasing the dataset size can improve model generalization but will likely increase training time as the model has more data to process.
- E. Correct.
Optimizing the model architecture by reducing the number of layers or parameters can reduce training time as there are fewer computations required during both forward and backward passes.