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* Port ML from C++ to C.
Pretty much everything is a non-functional change, ie.
the functionality is identical to the one provided
by the existing implementation that is written in C++.
Performance-wise, this implementation:
- Eliminates/reduces the number of allocations and
deallocations we have to do for training/detection,
- Uses just a single thread to perform detection for
*all* the hosts (ie. reduces the number of required
threads by 50% on parents), and
- Allows training, prediction and detection of
dimensions that have an update_every that is
different from that of the localhost.
The only C++ functionality that we still use
is vectors, because they make our life easier
and they are pretty much a requirement imposed
by dlib.
* Remove profile.plugin
It was useful only for testing during development.
* Limit logs to 200 lines per period
* Properly generate ml_info in /api/v1/info endpoint.
* Remove resource usage charts since we use worker charts.
* Use a temporary to make linters happy.
* Rebase.
* Fix builds that have ML functionality disabled.
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* Add profile.plugin
Creates the specified number of charts/dimensions, and supports
backfilling with pseudo-historical data.
* Bump
* Remove wrongly merged line.
* Use the number of models specified from the config section.
* Add option to consult all ML models.
* Remove profiling option consuming all models.
* Add underscore after chart name prefix.
* prediction -> dimensions chart
* reorder funcs
* Split charts across types with correct priority
* Ignore training request when chart is under replication.
* Track global number of models consulted.
* Cleanup config.
* initial readme updates
* fix readme
* readme
* Fix function definition when ML is disabled.
* Add dummy ml_chart_update_{begin,end}
* Remove profile_plugin
* Define chart priorities under collectors/all.h
* s/curr_t/current_time/
* Use libnetdata's lock/thread wrappers.
* Fix autotools & cmake builds.
* Delete ML dimensions & charts.
* Let users of buffer preprocessing to handle memory.
* Add separate API calls to start/stop ML threads.
Co-authored-by: Andrew Maguire <andrewm4894@gmail.com>
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(#14172)
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* Add profile.plugin
Creates the specified number of charts/dimensions, and supports
backfilling with pseudo-historical data.
* Bump
* Remove wrongly merged line.
* Use the number of models specified from the config section.
* Add option to consult all ML models.
* Remove profiling option consuming all models.
* Add underscore after chart name prefix.
* prediction -> dimensions chart
* reorder funcs
* Split charts across types with correct priority
* Ignore training request when chart is under replication.
* Track global number of models consulted.
* Cleanup config.
* initial readme updates
* fix readme
* readme
* Fix function definition when ML is disabled.
* Add dummy ml_chart_update_{begin,end}
* Remove profile_plugin
* Define chart priorities under collectors/all.h
* s/curr_t/current_time/
Co-authored-by: Andrew Maguire <andrewm4894@gmail.com>
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* Move all dims under one class.
* Dimension owns anomaly rate RD.
* Remove Dimension::isAnomalous()
* Remove Dimension::trainEvery()
* Rm ml/kmeans
* Remove anomaly detector
The same logic can be implemented by using the host anomaly rate dim.
* Profile plugin.
* Revert "Profile plugin."
This reverts commit e3db37cb49c514502c5216cfe7bca2a003fb90f1.
* Add separate source files for anomaly detection charts.
* Handle training/prediction sync at the dimension level.
* Keep multiple KMeans models in mem.
* Move feature extraction outside KMeans class.
* Use multiple models.
* Add /api/v1/ml_models endpoint.
* Remove Dimension::getID()
* Use just 1 model and fix tests.
* Add detection logic based on rrdr.
* Remove config options related to anomaly detection.
* Make anomaly detection queries configurable.
* Fix ad query duration option.
* Finalize queries in all code paths.
* Check if query was initialized before finalizing it
* Do not leak OWA
* Profile plugin.
* Revert "Profile plugin."
This reverts commit 5c77145d0df7e091d030476c480ab8d9cbceb89e.
* Change context from anomaly_detection to detector_events.
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