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?
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?
To perform hyperparameter tuning for a PyTorch model, running a hyperparameter tuning job on AI Platform using custom containers on Vertex AI is a suitable approach. This allows you to leverage Vertex AI's built-in support for hyperparameter tuning with custom models, without the need to convert the model to another framework or use an external tool.
B because Vertex AI supports custom models hyperparameter tuning
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.
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.
C seems to correct- https://www.kubeflow.org/docs/components/katib/overview/
Why use a thrid-party tool when Vertex AI already let you tuning hyperparameters in custom containers? I think it's B
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
Went with B
B) Customer containers