PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #335
You need to train an XGBoost model on a small dataset. Your training code requires custom dependencies. You need to set up a Vertex AI custom training job. You want to minimize the startup time of the
The correct answer is B. Store the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run. Using the prebuilt XGBoost container avoids the overhead of building and maintaining a custom container, minimizing startup time. Storing data in Cloud Storage and installing custom dependencies at runtime via a Python source distribution follows Google best practices for flexibi
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Options
- ACreate a custom container that includes the data and the custom dependencies. In your training
- BStore the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run
- CUse the XGBoost prebuilt custom container. Create a Python source distribution that includes the
- DStore the data in a Cloud Storage bucket, and create a custom container with your training
How the community answered
(39 responses)- A13% (5)
- B56% (22)
- C26% (10)
- D5% (2)
Explanation
Using the prebuilt XGBoost container avoids the overhead of building and maintaining a custom container, minimizing startup time. Storing data in Cloud Storage and installing custom dependencies at runtime via a Python source distribution follows Google best practices for flexibility and efficiency in custom training jobs.
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