MLS-C01 Modeling Practice Question
A data scientist is training a deep learning model on Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single GPU instance. The model is a large transformer with millions of parameters. Which change would most likely reduce training time?
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 SageMaker's distributed data parallelism with multiple GPU instances
Using data parallelism with multiple GPU instances can significantly reduce training time for large models by distributing the workload across multiple GPUs. Model parallelism is also possible but data parallelism is more common and easier to implement.
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 to fit in memory
Why it's wrong here
Reducing batch size may not necessarily reduce total training time; it could increase the number of steps.
- ✗
Use a smaller instance type
Why it's wrong here
Smaller instance types have less compute power, likely increasing training time.
- ✗
Switch to a CPU instance
Why it's wrong here
CPU instances are slower for deep learning than GPU instances.
- ✓
Use SageMaker's distributed data parallelism with multiple GPU instances
Why this is correct
Data parallelism splits the mini-batch across GPUs, reducing training time.
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