doc: Add xgboost doc on bring your own model by edwardjkim · Pull Request #1727 · aws/sagemaker-python-sdk

edwardjkim · 2020-07-20T18:00:38Z

Description of changes:

This CR adds a section on how to deploy an inference server with an XGBoost model that was trained outside SageMaker.

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edwardjkim · 2020-07-21T18:48:12Z

@metrizable Can you please review?

laurenyu · 2020-07-22T18:06:31Z

doc/frameworks/xgboost/using_xgboost.rst

+For a sample notebook that shows to use the XGBoost built-in algorith mode to load a pre-existing XGBoost model,
+see `Amazon SageMaker XGBoost Bring Your Own Model <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb>`_.


typo with "algorithm". also, would it make more sense to put this in the "Use XGBoost as a Built-in Algorithm" section?

Suggested change

For a sample notebook that shows to use the XGBoost built-in algorith mode to load a pre-existing XGBoost model,

see `Amazon SageMaker XGBoost Bring Your Own Model <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb>`_.

For a sample notebook that shows to use the XGBoost built-in algorithm mode to load a pre-existing XGBoost model,

see `Amazon SageMaker XGBoost Bring Your Own Model <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb>`_.

Thanks for the suggestion. There will also be a section on AWS documentation, and considering your suggestion, I think it makes more sense to move this to AWS documentation since the notebook is about the built-in mode. Python SDK doc can focus on script mode.

doc/frameworks/xgboost/using_xgboost.rst

Co-authored-by: Lauren Yu <6631887+laurenyu@users.noreply.github.com>

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laurenyu · 2020-07-22T20:21:31Z

doc/frameworks/xgboost/using_xgboost.rst

+    )
+
+    # If payload is a string in LIBSVM format, we need to change serializer.
+    predictor.serializer = str


what is str supposed to be here?

We've been using serializer = str in our XGBoost notebook examples because the most natural format for XGBoost is libsvm and there is no default libsvm serializer. My understanding is predictor expects a byte sequence like a numpy array by default, so I've been using serializer = str when I have a payload in libsvm string format. Is there a better way (other than writing a custom serializer)?

ah, gotcha. no, the only other way would be to write a custom serializer.

The predictor is an XGBoost model - couldn't we override the default serializer to str for it?

Yes, we could override it here: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/xgboost/model.py#L45. But I guess the question is if it's going to break any customers who were assuming a different default.

if it makes sense to change it, you can make another PR against the "zwei" branch - that's where our v2 work is happening right now

Cool, thanks for the pointer! And also for reminding me that our team needs to revisit the xgboost and sklearn estimator before v2 for issues like this.

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eslesar-aws

lgtm

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doc: Add xgboost doc on bring your own model

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edwardjkim force-pushed the doc-xgboost-byom branch from da03d7f to d6005a8 Compare July 20, 2020 22:54

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Move reference to BYOM notebook to AWS doc

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		For a sample notebook that shows to use the XGBoost built-in algorith mode to load a pre-existing XGBoost model,
		see `Amazon SageMaker XGBoost Bring Your Own Model <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/xgboost_bring_your_own_model/xgboost_bring_your_own_model.ipynb>`_.

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edwardjkim Jul 29, 2020 •

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