MLA-C01 ML Model Development Practice Question
A team is training a PyTorch model using SageMaker. They have a custom training script that requires specific Python packages not included in the SageMaker default PyTorch container. Which approach 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
✓
Use the built-in PyTorch estimator and specify a requirements.txt in the source directory
Using a SageMaker PyTorch estimator with a requirements.txt file allows installing additional packages on top of the official container. This is simpler than building a custom container.
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 the built-in PyTorch estimator and specify a requirements.txt in the source directory
Why this is correct
SageMaker automatically installs packages listed in requirements.txt in the source directory.
- ✗
Build a custom Docker container from scratch and push it to Amazon ECR
Why it's wrong here
Building a custom Docker container from scratch is unnecessary overhead because SageMaker provides a mechanism to extend its pre-built containers by installing additional packages via a `requirements.txt` file or a Dockerfile that inherits from the SageMaker PyTorch base image. This option is tempting because custom containers are the correct approach when the training script requires a fundamentally different runtime environment—such as a different operating system, a non-Python dependency, or a package that conflicts with the SageMaker container’s base libraries—where extending the existing image is insufficient.
- ✗
Use the SageMaker XGBoost estimator and modify the script to use PyTorch
Why it's wrong here
XGBoost estimator is not designed for PyTorch; it's for XGBoost models.
- ✗
Use SageMaker Autopilot to automatically handle dependencies
Why it's wrong here
Autopilot is for AutoML, not for running custom PyTorch scripts.
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