Added SageMaker batch transform support. #317
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Codecov Report
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## master #317 +/- ##
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+ Coverage 40.32% 80.22% +39.89%
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Files 142 144 +2
Lines 8024 8183 +159
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+ Hits 3236 6565 +3329
+ Misses 4788 1618 -3170
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src/gluonts/model/forecast.py
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| output_types: Set[OutputType] | ||
| quantiles: List[str] # FIXME: validate list elements | ||
| num_eval_samples: int = pydantic.Schema(100, alias="num_samples") | ||
| output_types: Set[OutputType] = {"qunatiles", "mean"} |
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Fix spelling of qunatiles to quantiles.
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| # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. | |||
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Can we maybe put this in another file and just import the stuff we want to expose here?
This has been our convention so far regarding __init__.py.
| import numpy as np | ||
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| def jsonify_floats(json_object): |
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We could subclass json.JSONEncoder -- but this is fine too.
| forecaster_type: Optional[Type[Union[Estimator, Predictor]]], | ||
| settings: Settings, | ||
| ) -> Application: | ||
| check_gpu_support() |
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I guess for supporting gpu's properly, we need a bit more logic for handling the number of proceses.
Also, the server should probably be started with the following environment variables to ensure that each server has one cpu and mxnet does not try to parallelize a single request.
OMP_NUM_THREADS=1
MXNET_ENGINE_TYPE=NaiveEngine
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Thanks. These changes were just moved around, but we should still do what you’ve suggested.
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TODO:
To use batch inference, one has to set the environment variable:
INFERENCE_CONFIGto the right json string.