MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to train a deep learning model using TensorFlow. The training job is failing with an 'OutOfMemory' error. The instance type is ml.p3.2xlarge with 16 GB GPU memory. The model has 10 million parameters. Which THREE actions should be taken to resolve the memory issue? (Choose THREE.)
⚠ Common exam trap
A common mix-up: candidates confuse 'increasing epochs' with reducing memory load, or think that increasing batch size helps convergence, when in fact it exacerbates the memory issue.
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
✓
Reduce the batch size
Reducing the batch size directly decreases the memory footprint per training step because fewer samples are loaded into GPU memory simultaneously. With 10 million parameters and 16 GB GPU memory, the default batch size may exceed available memory for activations and gradients. This is the most straightforward fix for an OutOfMemory error in TensorFlow on SageMaker.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce the batch size
Why this is correct
Smaller batch size directly reduces memory usage.
- ✗
Increase the number of epochs
Why it's wrong here
Number of epochs does not affect per-step memory.
- ✓
Enable mixed precision training
Why this is correct
Mixed precision reduces memory usage by storing tensors in half-precision.
- ✗
Increase the batch size
Why it's wrong here
Larger batch size increases memory consumption.
- ✓
Use gradient accumulation
Why this is correct
Gradient accumulation allows effective larger batch size without increasing memory per step.
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