[ML] Use disk storage for forecasting large models #36
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
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