Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. 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 Kuberflow 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.
This is a question sourced from google blog
pre-trained BERT model
https://cloud.google.com/blog/topics/developers-practitioners/pytorch-google-cloud-how-train-and-tune-pytorch-models-vertex-ai
https://cloud.google.com/blog/topics/developers-practitioners/pytorch-google-cloud-how-deploy-pytorch-models-vertex-ai
C:
Don't wast your time to convert to other framework, you can use it on custom container absolutely.
https://cloud.google.com/blog/topics/developers-practitioners/pytorch-google-cloud-how-train-and-tune-pytorch-models-vertex-ai
I insist on B, At the present, it seem like we can use prebuilt container instead of custom container, but none of the 4 choice, so B is the most likely way out of this question.
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