I just want to make sure I understand the context: Are you referring to pre and postprocessing for an inference request or for training a model? I'm asking because SageMaker offers both:
- Processing jobs which are used to process data before training a model: https://docs.aws.amazon.com/sagemaker/latest/dg/processing-job.html
- Processing data before sending it to an endpoint for inference: https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#write-an-inference-script
Could you help clarify the question?
I am talking about inference pipeline only.
Don't we have pre and post-processing in-built feature for NER in SM PyTorch or HuggingFace model servers?
We thought that pre and post-processing taken care by SM and we just need to bring our fine-tuned model on our dataset.
Hi Vinayak - apologies for the late response, I was OOTO.
Yes, you are correct, pre- and post-processing for Huggingface inference is "built in" via the SageMaker Hugging Face Inference Toolkit. The documentation on the Github repo shows how this is being done.
Hope that helps!
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