MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model on SageMaker using a custom container. The training job fails with an 'OutOfMemory' error. Which THREE actions could resolve this issue? (Choose 3.)
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 simulate larger batch sizes.
An OutOfMemory error occurs when the model and data exceed the GPU memory. Reducing batch size (C) directly lowers memory per iteration. Gradient accumulation (A) allows using a larger effective batch size without increasing memory by splitting it into micro-batches. Using an instance with more memory (D) provides additional capacity. Reducing epochs (B) does not affect per-batch memory; it only shortens training. Increasing learning rate (E) can cause instability but does not reduce memory usage.
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 to simulate larger batch sizes.
Why this is correct
Gradient accumulation divides the desired batch into micro-batches, performing a forward pass on each and accumulating gradients, then updating weights once. This simulates a larger batch without increasing memory per step.
- ✗
Reduce the number of training epochs.
Why it's wrong here
Reducing the number of training epochs decreases the total training time but does not change the memory required per batch or per iteration.
- ✓
Reduce the batch size.
Why this is correct
Reducing batch size directly decreases the amount of data processed simultaneously, lowering GPU memory consumption.
- ✓
Use an instance type with more memory, such as ml.p3.16xlarge.
Why this is correct
Using a larger instance type (e.g., ml.p3.16xlarge) increases available GPU and system memory, allowing the existing workload to fit.
- ✗
Increase the learning rate.
Why it's wrong here
Increasing the learning rate does not reduce memory usage; it may cause divergence or require more memory if adaptive optimizers are used.
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