Port ML from C++ to C. (#14567)

* 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.

26 files changed, 2114 insertions, 2537 deletions

diff --git a/ml/ADCharts.cc b/ml/ADCharts.cc
deleted file mode 100644
index cbb13f5d19..0000000000
--- a/ml/ADCharts.cc
+++ /dev/null
@@ -1,518 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#include "ADCharts.h"
-#include "Config.h"
-
-void ml::updateDimensionsChart(RRDHOST *RH, const MachineLearningStats &MLS) {
-    /*
-     * Machine learning status
-    */
-    {
-        static thread_local RRDSET *MachineLearningStatusRS = nullptr;
-
-        static thread_local RRDDIM *Enabled = nullptr;
-        static thread_local RRDDIM *DisabledUE = nullptr;
-        static thread_local RRDDIM *DisabledSP = nullptr;
-
-        if (!MachineLearningStatusRS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "machine_learning_status_on_" << localhost->machine_guid;
-            NameSS << "machine_learning_status_on_" << rrdhost_hostname(localhost);
-
-            MachineLearningStatusRS = rrdset_create(
-                    RH,
-                    "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                NETDATA_ML_CHART_FAMILY, // family
-                "netdata.machine_learning_status", // ctx
-                "Machine learning status", // title
-                "dimensions", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_TRAINING, // module
-                NETDATA_ML_CHART_PRIO_MACHINE_LEARNING_STATUS, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_LINE // chart_type
-            );
-            rrdset_flag_set(MachineLearningStatusRS , RRDSET_FLAG_ANOMALY_DETECTION);
-
-            Enabled = rrddim_add(MachineLearningStatusRS, "enabled", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            DisabledUE = rrddim_add(MachineLearningStatusRS, "disabled-ue", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            DisabledSP = rrddim_add(MachineLearningStatusRS, "disabled-sp", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-        }
-
-        rrddim_set_by_pointer(MachineLearningStatusRS, Enabled, MLS.NumMachineLearningStatusEnabled);
-        rrddim_set_by_pointer(MachineLearningStatusRS, DisabledUE, MLS.NumMachineLearningStatusDisabledUE);
-        rrddim_set_by_pointer(MachineLearningStatusRS, DisabledSP, MLS.NumMachineLearningStatusDisabledSP);
-
-        rrdset_done(MachineLearningStatusRS);
-    }
-
-    /*
-     * Metric type
-    */
-    {
-        static thread_local RRDSET *MetricTypesRS = nullptr;
-
-        static thread_local RRDDIM *Constant = nullptr;
-        static thread_local RRDDIM *Variable = nullptr;
-
-        if (!MetricTypesRS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "metric_types_on_" << localhost->machine_guid;
-            NameSS << "metric_types_on_" << rrdhost_hostname(localhost);
-
-            MetricTypesRS = rrdset_create(
-                    RH,
-                    "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                NETDATA_ML_CHART_FAMILY, // family
-                "netdata.metric_types", // ctx
-                "Dimensions by metric type", // title
-                "dimensions", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_TRAINING, // module
-                NETDATA_ML_CHART_PRIO_METRIC_TYPES, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_LINE // chart_type
-            );
-            rrdset_flag_set(MetricTypesRS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-            Constant = rrddim_add(MetricTypesRS, "constant", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            Variable = rrddim_add(MetricTypesRS, "variable", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-        }
-
-        rrddim_set_by_pointer(MetricTypesRS, Constant, MLS.NumMetricTypeConstant);
-        rrddim_set_by_pointer(MetricTypesRS, Variable, MLS.NumMetricTypeVariable);
-
-        rrdset_done(MetricTypesRS);
-    }
-
-    /*
-     * Training status
-    */
-    {
-        static thread_local RRDSET *TrainingStatusRS = nullptr;
-
-        static thread_local RRDDIM *Untrained = nullptr;
-        static thread_local RRDDIM *PendingWithoutModel = nullptr;
-        static thread_local RRDDIM *Trained = nullptr;
-        static thread_local RRDDIM *PendingWithModel = nullptr;
-
-        if (!TrainingStatusRS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "training_status_on_" << localhost->machine_guid;
-            NameSS << "training_status_on_" << rrdhost_hostname(localhost);
-
-            TrainingStatusRS = rrdset_create(
-                    RH,
-                    "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                NETDATA_ML_CHART_FAMILY, // family
-                "netdata.training_status", // ctx
-                "Training status of dimensions", // title
-                "dimensions", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_TRAINING, // module
-                NETDATA_ML_CHART_PRIO_TRAINING_STATUS, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_LINE // chart_type
-            );
-
-            rrdset_flag_set(TrainingStatusRS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-            Untrained = rrddim_add(TrainingStatusRS, "untrained", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            PendingWithoutModel = rrddim_add(TrainingStatusRS, "pending-without-model", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            Trained = rrddim_add(TrainingStatusRS, "trained", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            PendingWithModel = rrddim_add(TrainingStatusRS, "pending-with-model", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-        }
-
-        rrddim_set_by_pointer(TrainingStatusRS, Untrained, MLS.NumTrainingStatusUntrained);
-        rrddim_set_by_pointer(TrainingStatusRS, PendingWithoutModel, MLS.NumTrainingStatusPendingWithoutModel);
-        rrddim_set_by_pointer(TrainingStatusRS, Trained, MLS.NumTrainingStatusTrained);
-        rrddim_set_by_pointer(TrainingStatusRS, PendingWithModel, MLS.NumTrainingStatusPendingWithModel);
-
-        rrdset_done(TrainingStatusRS);
-    }
-
-    /*
-     * Prediction status
-    */
-    {
-        static thread_local RRDSET *PredictionRS = nullptr;
-
-        static thread_local RRDDIM *Anomalous = nullptr;
-        static thread_local RRDDIM *Normal = nullptr;
-
-        if (!PredictionRS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "dimensions_on_" << localhost->machine_guid;
-            NameSS << "dimensions_on_" << rrdhost_hostname(localhost);
-
-            PredictionRS = rrdset_create(
-                    RH,
-                    "anomaly_detection", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                "dimensions", // family
-                "anomaly_detection.dimensions", // ctx
-                "Anomaly detection dimensions", // title
-                "dimensions", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_TRAINING, // module
-                ML_CHART_PRIO_DIMENSIONS, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_LINE // chart_type
-            );
-            rrdset_flag_set(PredictionRS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-            Anomalous = rrddim_add(PredictionRS, "anomalous", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            Normal = rrddim_add(PredictionRS, "normal", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-        }
-
-        rrddim_set_by_pointer(PredictionRS, Anomalous, MLS.NumAnomalousDimensions);
-        rrddim_set_by_pointer(PredictionRS, Normal, MLS.NumNormalDimensions);
-
-        rrdset_done(PredictionRS);
-    }
-
-}
-
-void ml::updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyRate) {
-    static thread_local RRDSET *HostRateRS = nullptr;
-    static thread_local RRDDIM *AnomalyRateRD = nullptr;
-
-    if (!HostRateRS) {
-        std::stringstream IdSS, NameSS;
-
-        IdSS << "anomaly_rate_on_" << localhost->machine_guid;
-        NameSS << "anomaly_rate_on_" << rrdhost_hostname(localhost);
-
-        HostRateRS = rrdset_create(
-                RH,
-                "anomaly_detection", // type
-            IdSS.str().c_str(), // id
-            NameSS.str().c_str(), // name
-            "anomaly_rate", // family
-            "anomaly_detection.anomaly_rate", // ctx
-            "Percentage of anomalous dimensions", // title
-            "percentage", // units
-            NETDATA_ML_PLUGIN, // plugin
-            NETDATA_ML_MODULE_DETECTION, // module
-            ML_CHART_PRIO_ANOMALY_RATE, // priority
-            RH->rrd_update_every, // update_every
-            RRDSET_TYPE_LINE // chart_type
-        );
-        rrdset_flag_set(HostRateRS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-        AnomalyRateRD = rrddim_add(HostRateRS, "anomaly_rate", NULL,
-                1, 100, RRD_ALGORITHM_ABSOLUTE);
-    }
-
-    rrddim_set_by_pointer(HostRateRS, AnomalyRateRD, AnomalyRate);
-    rrdset_done(HostRateRS);
-
-    static thread_local RRDSET *AnomalyDetectionRS = nullptr;
-    static thread_local RRDDIM *AboveThresholdRD = nullptr;
-    static thread_local RRDDIM *NewAnomalyEventRD = nullptr;
-
-    if (!AnomalyDetectionRS) {
-        std::stringstream IdSS, NameSS;
-
-        IdSS << "anomaly_detection_on_" << localhost->machine_guid;
-        NameSS << "anomaly_detection_on_" << rrdhost_hostname(localhost);
-
-        AnomalyDetectionRS = rrdset_create(
-                RH,
-                "anomaly_detection", // type
-            IdSS.str().c_str(), // id
-            NameSS.str().c_str(), // name
-            "anomaly_detection", // family
-            "anomaly_detection.detector_events", // ctx
-            "Anomaly detection events", // title
-            "percentage", // units
-            NETDATA_ML_PLUGIN, // plugin
-            NETDATA_ML_MODULE_DETECTION, // module
-            ML_CHART_PRIO_DETECTOR_EVENTS, // priority
-            RH->rrd_update_every, // update_every
-            RRDSET_TYPE_LINE // chart_type
-        );
-        rrdset_flag_set(AnomalyDetectionRS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-        AboveThresholdRD  = rrddim_add(AnomalyDetectionRS, "above_threshold", NULL,
-                                       1, 1, RRD_ALGORITHM_ABSOLUTE);
-        NewAnomalyEventRD = rrddim_add(AnomalyDetectionRS, "new_anomaly_event", NULL,
-                                       1, 1, RRD_ALGORITHM_ABSOLUTE);
-    }
-
-    /*
-     * Compute the values of the dimensions based on the host rate chart
-    */
-    ONEWAYALLOC *OWA = onewayalloc_create(0);
-    time_t Now = now_realtime_sec();
-    time_t Before = Now - RH->rrd_update_every;
-    time_t After = Before - Cfg.AnomalyDetectionQueryDuration;
-    RRDR_OPTIONS Options = static_cast<RRDR_OPTIONS>(0x00000000);
-
-    RRDR *R = rrd2rrdr_legacy(
-            OWA, HostRateRS,
-            1 /* points wanted */,
-            After,
-            Before,
-            Cfg.AnomalyDetectionGroupingMethod,
-            0 /* resampling time */,
-            Options, "anomaly_rate",
-            NULL /* group options */,
-            0, /* timeout */
-            0, /* tier */
-            QUERY_SOURCE_ML,
-            STORAGE_PRIORITY_BEST_EFFORT
-    );
-
-    if(R) {
-        if(R->d == 1 && R->n == 1 && R->rows == 1) {
-            static thread_local bool PrevAboveThreshold = false;
-            bool AboveThreshold = R->v[0] >= Cfg.HostAnomalyRateThreshold;
-            bool NewAnomalyEvent = AboveThreshold && !PrevAboveThreshold;
-            PrevAboveThreshold = AboveThreshold;
-
-            rrddim_set_by_pointer(AnomalyDetectionRS, AboveThresholdRD, AboveThreshold);
-            rrddim_set_by_pointer(AnomalyDetectionRS, NewAnomalyEventRD, NewAnomalyEvent);
-            rrdset_done(AnomalyDetectionRS);
-        }
-
-        rrdr_free(OWA, R);
-    }
-
-    onewayalloc_destroy(OWA);
-}
-
-void ml::updateResourceUsageCharts(RRDHOST *RH, const struct rusage &PredictionRU, const struct rusage &TrainingRU) {
-    /*
-     * prediction rusage
-    */
-    {
-        static thread_local RRDSET *RS = nullptr;
-
-        static thread_local RRDDIM *User = nullptr;
-        static thread_local RRDDIM *System = nullptr;
-
-        if (!RS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "prediction_usage_for_" << RH->machine_guid;
-            NameSS << "prediction_usage_for_" << rrdhost_hostname(RH);
-
-            RS = rrdset_create_localhost(
-                    "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                NETDATA_ML_CHART_FAMILY, // family
-                "netdata.prediction_usage", // ctx
-                "Prediction resource usage", // title
-                "milliseconds/s", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_PREDICTION, // module
-                NETDATA_ML_CHART_PRIO_PREDICTION_USAGE, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_STACKED // chart_type
-            );
-            rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-            User = rrddim_add(RS, "user", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL);
-            System = rrddim_add(RS, "system", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL);
-        }
-
-        rrddim_set_by_pointer(RS, User, PredictionRU.ru_utime.tv_sec * 1000000ULL + PredictionRU.ru_utime.tv_usec);
-        rrddim_set_by_pointer(RS, System, PredictionRU.ru_stime.tv_sec * 1000000ULL + PredictionRU.ru_stime.tv_usec);
-
-        rrdset_done(RS);
-    }
-
-    /*
-     * training rusage
-    */
-    {
-        static thread_local RRDSET *RS = nullptr;
-
-        static thread_local RRDDIM *User = nullptr;
-        static thread_local RRDDIM *System = nullptr;
-
-        if (!RS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "training_usage_for_" << RH->machine_guid;
-            NameSS << "training_usage_for_" << rrdhost_hostname(RH);
-
-            RS = rrdset_create_localhost(
-                    "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                NETDATA_ML_CHART_FAMILY, // family
-                "netdata.training_usage", // ctx
-                "Training resource usage", // title
-                "milliseconds/s", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_TRAINING, // module
-                NETDATA_ML_CHART_PRIO_TRAINING_USAGE, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_STACKED // chart_type
-            );
-            rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-            User = rrddim_add(RS, "user", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL);
-            System = rrddim_add(RS, "system", NULL, 1, 1000, RRD_ALGORITHM_INCREMENTAL);
-        }
-
-        rrddim_set_by_pointer(RS, User, TrainingRU.ru_utime.tv_sec * 1000000ULL + TrainingRU.ru_utime.tv_usec);
-        rrddim_set_by_pointer(RS, System, TrainingRU.ru_stime.tv_sec * 1000000ULL + TrainingRU.ru_stime.tv_usec);
-
-        rrdset_done(RS);
-    }
-}
-
-void ml::updateTrainingStatisticsChart(RRDHOST *RH, const TrainingStats &TS) {
-    /*
-     * queue stats
-    */
-    {
-        static thread_local RRDSET *RS = nullptr;
-
-        static thread_local RRDDIM *QueueSize = nullptr;
-        static thread_local RRDDIM *PoppedItems = nullptr;
-
-        if (!RS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "queue_stats_on_" << localhost->machine_guid;
-            NameSS << "queue_stats_on_" << rrdhost_hostname(localhost);
-
-            RS = rrdset_create(
-                    RH,
-                    "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                NETDATA_ML_CHART_FAMILY, // family
-                "netdata.queue_stats", // ctx
-                "Training queue stats", // title
-                "items", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_TRAINING, // module
-                NETDATA_ML_CHART_PRIO_QUEUE_STATS, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_LINE// chart_type
-            );
-            rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-            QueueSize = rrddim_add(RS, "queue_size", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            PoppedItems = rrddim_add(RS, "popped_items", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-        }
-
-        rrddim_set_by_pointer(RS, QueueSize, TS.QueueSize);
-        rrddim_set_by_pointer(RS, PoppedItems, TS.NumPoppedItems);
-
-        rrdset_done(RS);
-    }
-
-    /*
-     * training stats
-    */
-    {
-        static thread_local RRDSET *RS = nullptr;
-
-        static thread_local RRDDIM *Allotted = nullptr;
-        static thread_local RRDDIM *Consumed = nullptr;
-        static thread_local RRDDIM *Remaining = nullptr;
-
-        if (!RS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "training_time_stats_on_" << localhost->machine_guid;
-            NameSS << "training_time_stats_on_" << rrdhost_hostname(localhost);
-
-            RS = rrdset_create(
-                    RH,
-                    "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                NETDATA_ML_CHART_FAMILY, // family
-                "netdata.training_time_stats", // ctx
-                "Training time stats", // title
-                "milliseconds", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_TRAINING, // module
-                NETDATA_ML_CHART_PRIO_TRAINING_TIME_STATS, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_LINE// chart_type
-            );
-            rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-            Allotted = rrddim_add(RS, "allotted", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE);
-            Consumed = rrddim_add(RS, "consumed", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE);
-            Remaining = rrddim_add(RS, "remaining", NULL, 1, 1000, RRD_ALGORITHM_ABSOLUTE);
-        }
-
-        rrddim_set_by_pointer(RS, Allotted, TS.AllottedUT);
-        rrddim_set_by_pointer(RS, Consumed, TS.ConsumedUT);
-        rrddim_set_by_pointer(RS, Remaining, TS.RemainingUT);
-
-        rrdset_done(RS);
-    }
-
-    /*
-     * training result stats
-    */
-    {
-        static thread_local RRDSET *RS = nullptr;
-
-        static thread_local RRDDIM *Ok  = nullptr;
-        static thread_local RRDDIM *InvalidQueryTimeRange = nullptr;
-        static thread_local RRDDIM *NotEnoughCollectedValues = nullptr;
-        static thread_local RRDDIM *NullAcquiredDimension = nullptr;
-        static thread_local RRDDIM *ChartUnderReplication = nullptr;
-
-        if (!RS) {
-            std::stringstream IdSS, NameSS;
-
-            IdSS << "training_results_on_" << localhost->machine_guid;
-            NameSS << "training_results_on_" << rrdhost_hostname(localhost);
-
-            RS = rrdset_create(
-                    RH,
-                    "netdata", // type
-                IdSS.str().c_str(), // id
-                NameSS.str().c_str(), // name
-                NETDATA_ML_CHART_FAMILY, // family
-                "netdata.training_results", // ctx
-                "Training results", // title
-                "events", // units
-                NETDATA_ML_PLUGIN, // plugin
-                NETDATA_ML_MODULE_TRAINING, // module
-                NETDATA_ML_CHART_PRIO_TRAINING_RESULTS, // priority
-                RH->rrd_update_every, // update_every
-                RRDSET_TYPE_LINE// chart_type
-            );
-            rrdset_flag_set(RS, RRDSET_FLAG_ANOMALY_DETECTION);
-
-            Ok = rrddim_add(RS, "ok", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            InvalidQueryTimeRange = rrddim_add(RS, "invalid-queries", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            NotEnoughCollectedValues = rrddim_add(RS, "not-enough-values", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            NullAcquiredDimension = rrddim_add(RS, "null-acquired-dimensions", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-            ChartUnderReplication = rrddim_add(RS, "chart-under-replication", NULL, 1, 1, RRD_ALGORITHM_ABSOLUTE);
-        }
-
-        rrddim_set_by_pointer(RS, Ok, TS.TrainingResultOk);
-        rrddim_set_by_pointer(RS, InvalidQueryTimeRange, TS.TrainingResultInvalidQueryTimeRange);
-        rrddim_set_by_pointer(RS, NotEnoughCollectedValues, TS.TrainingResultNotEnoughCollectedValues);
-        rrddim_set_by_pointer(RS, NullAcquiredDimension, TS.TrainingResultNullAcquiredDimension);
-        rrddim_set_by_pointer(RS, ChartUnderReplication, TS.TrainingResultChartUnderReplication);
-
-        rrdset_done(RS);
-    }
-}
diff --git a/ml/ADCharts.h b/ml/ADCharts.h
deleted file mode 100644
index ee09669e22..0000000000
--- a/ml/ADCharts.h
+++ /dev/null
@@ -1,21 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#ifndef ML_ADCHARTS_H
-#define ML_ADCHARTS_H
-
-#include "Stats.h"
-#include "ml-private.h"
-
-namespace ml {
-
-void updateDimensionsChart(RRDHOST *RH, const MachineLearningStats &MLS);
-
-void updateHostAndDetectionRateCharts(RRDHOST *RH, collected_number AnomalyRate);
-
-void updateResourceUsageCharts(RRDHOST *RH, const struct rusage &PredictionRU, const struct rusage &TrainingRU);
-
-void updateTrainingStatisticsChart(RRDHOST *RH, const TrainingStats &TS);
-
-} // namespace ml
-
-#endif /* ML_ADCHARTS_H */
diff --git a/ml/Chart.cc b/ml/Chart.cc
deleted file mode 100644
index e69de29bb2..0000000000
--- a/ml/Chart.cc
+++ /dev/null
diff --git a/ml/Chart.h b/ml/Chart.h
deleted file mode 100644
index dbd6a910f9..0000000000
--- a/ml/Chart.h
+++ /dev/null
@@ -1,128 +0,0 @@
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-#ifndef ML_CHART_H
-#define ML_CHART_H
-
-#include "Config.h"
-#include "Dimension.h"
-
-#include "ml-private.h"
-#include "json/single_include/nlohmann/json.hpp"
-
-namespace ml
-{
-
-class Chart {
-public:
-    Chart(RRDSET *RS) :
-        RS(RS),
-        MLS()
-     { }
-
-    RRDSET *getRS() const {
-        return RS;
-    }
-
-    bool isAvailableForML() {
-        return rrdset_is_available_for_exporting_and_alarms(RS);
-    }
-
-    void addDimension(Dimension *D) {
-        std::lock_guard<Mutex> L(M);
-        Dimensions[D->getRD()] = D;
-    }
-
-    void removeDimension(Dimension *D) {
-        std::lock_guard<Mutex> L(M);
-        Dimensions.erase(D->getRD());
-    }
-
-    void getModelsAsJson(nlohmann::json &Json) {
-        std::lock_guard<Mutex> L(M);
-
-        for (auto &DP : Dimensions) {
-            Dimension *D = DP.second;
-            nlohmann::json JsonArray = nlohmann::json::array();
-            for (const KMeans &KM : D->getModels()) {
-                nlohmann::json J;
-                KM.toJson(J);
-                JsonArray.push_back(J);
-            }
-
-            Json[getMLDimensionID(D->getRD())] = JsonArray;
-        }
-    }
-
-    void updateBegin() {
-        M.lock();
-        MLS = {};
-    }
-
-    void updateDimension(Dimension *D, bool IsAnomalous) {
-        switch (D->getMLS()) {
-            case MachineLearningStatus::DisabledDueToUniqueUpdateEvery:
-                MLS.NumMachineLearningStatusDisabledUE++;
-                return;
-            case MachineLearningStatus::DisabledDueToExcludedChart:
-                MLS.NumMachineLearningStatusDisabledSP++;
-                return;
-            case MachineLearningStatus::Enabled: {
-                MLS.NumMachineLearningStatusEnabled++;
-
-                switch (D->getMT()) {
-                    case MetricType::Constant:
-                        MLS.NumMetricTypeConstant++;
-                        MLS.NumTrainingStatusTrained++;
-                        MLS.NumNormalDimensions++;
-                        return;
-                    case MetricType::Variable:
-                        MLS.NumMetricTypeVariable++;
-                        break;
-                }
-
-                switch (D->getTS()) {
-                    case TrainingStatus::Untrained:
-                        MLS.NumTrainingStatusUntrained++;
-                        return;
-                    case TrainingStatus::PendingWithoutModel:
-                        MLS.NumTrainingStatusPendingWithoutModel++;
-                        return;
-                    case TrainingStatus::Trained:
-                        MLS.NumTrainingStatusTrained++;
-
-                        MLS.NumAnomalousDimensions += IsAnomalous;
-                        MLS.NumNormalDimensions += !IsAnomalous;
-                        return;
-                    case TrainingStatus::PendingWithModel:
-                        MLS.NumTrainingStatusPendingWithModel++;
-
-                        MLS.NumAnomalousDimensions += IsAnomalous;
-                        MLS.NumNormalDimensions += !IsAnomalous;
-                        return;
-                }
-
-                return;
-            }
-        }
-    }
-
-    void updateEnd() {
-        M.unlock();
-    }
-
-    MachineLearningStats getMLS() {
-        std::lock_guard<Mutex> L(M);
-        return MLS;
-    }
-
-private:
-    RRDSET *RS;
-    MachineLearningStats MLS;
-
-    Mutex M;
-    std::unordered_map<RRDDIM *, Dimension *> Dimensions;
-};
-
-} // namespace ml
-
-#endif /* ML_CHART_H */
diff --git a/ml/Config.cc b/ml/Config.cc
index cdc43d1655..d7ddff800d 100644
--- a/ml/Config.cc
+++ b/ml/Config.cc
@@ -1,15 +1,12 @@
 // SPDX-License-Identifier: GPL-3.0-or-later
 
-#include "Config.h"
-#include "ml-private.h"
-
-using namespace ml;
+#include "nml.h"
 
 /*
  * Global configuration instance to be shared between training and
  * prediction threads.
  */
-Config ml::Cfg;
+nml_config_t Cfg;
 
 template <typename T>
 static T clamp(const T& Value, const T& Min, const T& Max) {
@@ -19,97 +16,97 @@ static T clamp(const T& Value, const T& Min, const T& Max) {
 /*
  * Initialize global configuration variable.
  */
-void Config::readMLConfig(void) {
-    const char *ConfigSectionML = CONFIG_SECTION_ML;
+void nml_config_load(nml_config_t *cfg) {
+    const char *config_section_ml = CONFIG_SECTION_ML;
 
-    bool EnableAnomalyDetection = config_get_boolean(ConfigSectionML, "enabled", true);
+    bool enable_anomaly_detection = config_get_boolean(config_section_ml, "enabled", true);
 
     /*
      * Read values
      */
 
-    unsigned MaxTrainSamples = config_get_number(ConfigSectionML, "maximum num samples to train", 4 * 3600);
-    unsigned MinTrainSamples = config_get_number(ConfigSectionML, "minimum num samples to train", 1 * 900);
-    unsigned TrainEvery = config_get_number(ConfigSectionML, "train every", 1 * 3600);
-    unsigned NumModelsToUse = config_get_number(ConfigSectionML, "number of models per dimension", 1);
+    unsigned max_train_samples = config_get_number(config_section_ml, "maximum num samples to train", 4 * 3600);
+    unsigned min_train_samples = config_get_number(config_section_ml, "minimum num samples to train", 1 * 900);
+    unsigned train_every = config_get_number(config_section_ml, "train every", 1 * 3600);
+    unsigned num_models_to_use = config_get_number(config_section_ml, "number of models per dimension", 1);
 
-    unsigned DiffN = config_get_number(ConfigSectionML, "num samples to diff", 1);
-    unsigned SmoothN = config_get_number(ConfigSectionML, "num samples to smooth", 3);
-    unsigned LagN = config_get_number(ConfigSectionML, "num samples to lag", 5);
+    unsigned diff_n = config_get_number(config_section_ml, "num samples to diff", 1);
+    unsigned