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Hi there,
it would seem to me that there maybe an discrepancy in when one of your outputs is returned, they should only be returned after an if else statement and defined within the if else statement. Here is a resource that seems to address a problem similar to yours, perhaps applying a similar strategy will produce significant results.
Hopefully this will provide some insight into your problem.
Regards NN
Hi Ntiyiso, where do you suggest to add such logic ?
Hi there exorcismus, I would suggest applying the logic to your predictions in the model itself if applicable.
Hi Ntiyiso, the model and the endpoint works fine and am able to use them for prediction, am not sure how/why implementing that logic affects being invoked from spark, what do you think ?
Hi exorcismus, it's possible that pyspark version and pyarrow settings aren't compatible with your os and/vm configurations similarly what sudopip was experiencing.
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Can you share more details on the container that you are using here? Is it one of the SageMaker supported DLCs - https://github.com/aws/deep-learning-containers/blob/master/available_images.md Have you tested the inference on local?