MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model for image classification on Amazon SageMaker. The dataset consists of 10,000 images of size 224x224 pixels. The training job uses a single ml.p3.2xlarge instance. The data scientist notices that the GPU utilization is very low (~20%) and the training is slow. Which change would most likely improve GPU utilization?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of data loader workers to load data in parallel
Low GPU utilization often indicates that the data loading pipeline is bottlenecked. Increasing the number of data loader workers can improve data throughput to the GPU, keeping it busy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use gradient accumulation
Why it's wrong here
Gradient accumulation simulates larger batch sizes but does not directly improve GPU utilization.
- ✗
Use a larger instance type with more GPUs
Why it's wrong here
More GPUs may not help if the data pipeline is the bottleneck.
- ✗
Increase the batch size
Why it's wrong here
Increasing batch size may increase memory usage but not necessarily utilization if data loading is slow.
- ✓
Increase the number of data loader workers to load data in parallel
Why this is correct
More workers can load data faster, reducing idle GPU time.
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