MLS-C01 Modeling Practice Question
A machine learning team is using Amazon SageMaker to train a large language model. The training script uses PyTorch and the model requires significant memory. The team wants to use model parallelism across multiple GPUs. Which SageMaker feature should they use?
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
✓
SageMaker model parallelism library
SageMaker's model parallelism library (SMP) is designed for distributed training of large models across GPUs. Horovod is for data parallelism, not model parallelism. SageMaker Debugger is for monitoring training. Distributed Training is a generic term; the specific library is SMP.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Distributed Training
Why it's wrong here
While correct in concept, the specific feature is the model parallelism library.
- ✓
SageMaker model parallelism library
Why this is correct
SMP is specifically designed for model parallelism.
- ✗
SageMaker Horovod
Why it's wrong here
Horovod supports data parallelism, not model parallelism.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger monitors training metrics, not for parallelism.
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