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Answer: Run a hyperparameter tuning job on AI Platform using custom containers.
The correct answer is B. Vertex AI supports hyperparameter tuning for custom models using custom containers. This allows you to use your PyTorch model without needing to convert it to another framework. Options A and D would require additional work to convert the model to Keras or TensorFlow, respectively. Option C involves setting up a Kubeflow Pipelines instance and using Katib, which is unnecessary when Vertex AI already offers a streamlined solution for hyperparameter tuning with custom containers.
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Your data science team is training a PyTorch model for image classification based on a pre-trained ResNet model. As part of the model optimization process, you are required to perform hyperparameter tuning to enhance the model's performance. Considering the existing tools and frameworks available on Google Cloud Platform, what should you do?
A
Convert the model to a Keras model, and run a Keras Tuner job.
B
Run a hyperparameter tuning job on AI Platform using custom containers.
C
Create a Kubeflow Pipelines instance, and run a hyperparameter tuning job on Katib.
D
Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.
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