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SagemakerEstimator in Spark ML Pipeline issue #98
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Reference: MLFW-2726 Hello @hdamani09, Apologies for the late reply. Thank you for bringing this to our attention. I have created an internal backlog ticket to track this, as it seems that our SageMaker estimators don't have an implemented write function, which enables saving.
There doesn't seem to be a possible way to do this. I'll make note of this to investigate and provide a solution in the internal ticket. I apologize for the experience. |
Reference: MLFW-2726
System Information
Describe the problem
Hi,
I tried to add a sagemakerEstimator within a Spark ML Pipeline and fit the training dataset on the pipeline which worked without any issues. When I tried to save the pipeline itself, it threw an exception stating the pipeline contains a stage that is not writable.
Is it intended to be that way since when fit runs on the sagemakerEstimator, it automatically persists the model to trainingOutputS3DataPath ?
If I wish to have a pipeline persisted which contains other transformer stages along with the sagemakerEstimator instance how would I do it?
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