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Vanilla SageMaker "Models" (as opposed to versioned ModelPackages) are immutable in the API with no "UpdateModel" action... But I think you should be able to create a new Model copying the settings of the current one.
I'd suggest to:
- Use DescribeModel (via boto3.client("sagemaker").describe_model(), assuming you're using Python) to fetch all the parameters of the existing JumpStart model such as the S3 artifact location and other settings
- Use CreateModel (create_model()) to create a new model with same configuration but network isolation disabled
- Use your new model to try and deploy an async endpoint
Probably you'd find the low-level boto3 SDK more intuitive for this task than the high-level sagemaker
SDK's Model class - because the latter does some magic that makes typical build/train/deploy workflows easier but can be less natural for hacking around with existing model definitions. For example, creating an SMSDK Model
object doesn't actually create a Model in the SageMaker API, because deployment instance type affects choice of container image so that gets deferred until a .deploy()
call or similar later.
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- 已提問 6 個月前
- AWS 官方已更新 1 年前
- AWS 官方已更新 2 年前
Thank You very much!