AWS JumpStart adding Shared Model requires me to enable both training and deployment, but shouldn't it be optional?

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Hello, I am trying to add a Shared Model on AWS Jumpstart but only want to enable deployment, as the model was already trained on custom data outside of AWS JumpStart environment. However, after I fill up the form in AWS JumpStart from SageMaker Studio UI for Basic Information and Enable Depolyment (Container, Inference Script, S3 Model Artifact bucket) and click "Add model" an error occurs: "There are errors in the form. Enable training: container, scriptLocation, modelArtifactLocation, instanceType". As stated here https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-content-sharing.html the choice to enable training or deployment is optional. Is there a bug in the application where it requires both training and deployment information in order to create a Shared Model?

1 Answer
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Hi,

Thank you for using AWS Sagemaker.

I understand that you have query that while sharing model using "Shared models" section of the Studio UI if it is necessary to fill both training and deployment details or if it is optional.

I would like to inform you that I also replicated this behaviour at my end and I could confirm that it is required to provide details for both "Enable Training" and "Enable deployment"

With respect to documentation https://docs.aws.amazon.com/sagemaker/latest/dg/jumpstart-content-sharing.html , it mentions below line .


When adding a model to share, you can optionally provide a training environment and allow collaborators in your organization to train the shared model.

The word "optionally" here conveys that you have various options with respect to selecting the training environment. These options are :

1)select a container used for an existing training job 2)bring your own container in Amazon ECR 3)use an Amazon SageMaker Deep Learning Container

Thus , you would need to select one of the above options according to your use case and would need to provide further details regarding training.

In case you have further queries or concern regarding the same, I'd recommend you to reachout to AWS Support by creating a support case[+] so that an engineer can help you in addressing your questions/use case.

Reference: ——————

[+]Open a support case with AWS using the link: https://console.aws.amazon.com/support/home?#/case/create [ +]https://aws.amazon.com/premiumsupport/faqs/

AWS
Radhika
answered 5 months ago

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