MLS-C01 Modeling Practice Question
A machine learning team is using SageMaker to train a deep learning model. The training job is failing due to insufficient GPU memory. Which approach should the team take to resolve this issue without changing the model architecture?
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
✓
Use gradient accumulation to reduce the effective batch size per step.
Gradient accumulation allows the model to simulate a larger batch size without increasing memory usage per step, as gradients are accumulated over several smaller batches before updating weights. Option A is wrong because increasing the batch size would increase memory consumption per step, exacerbating the issue. Option C (adding more GPUs) is wrong because simply adding more GPUs to the instance does not reduce the memory usage per GPU; each GPU would still process the same batch size and run out of memory. Option D is wrong because decreasing the learning rate does not affect memory usage; it only changes the step size during optimization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size.
Why it's wrong here
Larger batch size increases memory consumption.
- ✓
Use gradient accumulation to reduce the effective batch size per step.
Why this is correct
Gradient accumulation allows training with larger effective batches while keeping per-step memory low.
- ✗
Add more GPUs to the training instance.
Why it's wrong here
Adding GPUs does not reduce per-GPU memory; the model still needs to fit on each GPU.
- ✗
Decrease the learning rate.
Why it's wrong here
Learning rate does not affect memory usage.
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