Topic 1 Question 66
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?
Convert the model to a Keras model, and run a Keras Tuner job.
Run a hyperparameter tuning job on AI Platform using custom containers.
Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.
Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.
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コメント(6)
- 正解だと思う選択肢: B
B because Vertex AI supports custom models hyperparameter tuning
👍 7OzoneReloaded2022/12/09 - 正解だと思う選択肢: B
ans: B
A, D => too much work. C => not sure why you would complicate so much when Vertex AI has this feature in custom containers.
👍 3wish00352022/12/15 - 正解だと思う選択肢: B
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
👍 2John_Pongthorn2023/01/25
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