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[ML] Use disk storage for forecasting large models #36

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[ML] Use disk storage for forecasting large models #36

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hendrikmuhs
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@hendrikmuhs hendrikmuhs commented Apr 5, 2018

This implements the C++ side of forecast persistence. An additional parameter allows the forecast runner to persist models on disk for temporary purposes. Models are loaded back into memory one by one.

For models smaller than the current limit of 20MB nothing changes.

X-Pack part: elastic/x-pack-elasticsearch#4134

replaces #22

Only formating changes after #15, no logical changes compared to #22

Hendrik Muhs added 8 commits April 5, 2018 11:24
Re-factor minimumSeasonalVarianceScale to make it available as method.

This is required in order to restore models from a file stream, more precisely it is required as a parameter for maths::CModelParams which is required for maths::SModelRestoreParams
@droberts195 droberts195 changed the title [ML] Use off-heap storage for forecasting large models [ML] Use disk storage for forecasting large models Apr 5, 2018
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I was LGTM on #22, so this is still LGTM.

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Moving into a feature branch, replaced by #59

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