[ML] Feature/forecast scale#89
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hendrikmuhs merged 7 commits intoelastic:masterfrom May 14, 2018
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April 17, 2018 18:28
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.
droberts195
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May 4, 2018
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| // Note: This value measures the size in memory, not the size of the persistence, | ||
| // which is likely higher and would be hard to calculate upfront | ||
| //! max memory allowed to use for forecast models persisting to disk |
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Only the last line of this begins with //!. All the lines should be consistent.
| // if you change this value also change the limit on X-pack side. | ||
| // The purpose of this value is to guard the rest of the system regarding | ||
| // an out of disk space | ||
| //! minimum disk space required for disk persistence |
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Only the last line of this begins with //!. All the lines should be consistent.
include/model/CModelFactory.h
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| //! component. | ||
| std::size_t componentSize() const; | ||
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| // Get the minimum seasonal variance scale, specific to the model |
lib/api/CForecastRunner.cc
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| series.s_ToForecast.emplace_back( | ||
| feature, std::move(model), byFieldValue); | ||
| } else // restorer exhausted, no need for further restoring | ||
| { |
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nit: could move { after else but before comment
hendrikmuhs
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May 14, 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.
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This implements forecast model cloning overflowing to disk to allow forecasting of large jobs. The tmp storage location has to be given as parameter (X-Pack change: elastic/elasticsearch#30399). If the parameter is not given, this feature is disabled. For models smaller than the existing limit (20MB) nothing changes.
Todo:
To be merged together once corresponding X-Pack PR and this PR are ready.
Release note: Forecasting of large machine learning jobs is now supported by temporarily storing
model state on disk