Use static thread-pool for training. (#14702)

* Use static thread-pool for training.

* Add missing function definition

* disable training stats chart

* Add config option to explicitly enable ML stats charts.

---------

Co-authored-by: Costa Tsaousis <costa@netdata.cloud>

1 files changed, 273 insertions, 273 deletions

diff --git a/ml/ml.cc b/ml/ml.cc
index cf9ea379a6..c7d4671c04 100644
--- a/ml/ml.cc
+++ b/ml/ml.cc
@@ -8,14 +8,13 @@
 
 #include "ad_charts.h"
 
-typedef struct {
-    calculated_number_t *training_cns;
-    calculated_number_t *scratch_training_cns;
-
-    std::vector<DSample> training_samples;
-} ml_tls_data_t;
-
-static thread_local ml_tls_data_t tls_data;
+#define WORKER_TRAIN_QUEUE_POP         0
+#define WORKER_TRAIN_ACQUIRE_DIMENSION 1
+#define WORKER_TRAIN_QUERY             2
+#define WORKER_TRAIN_KMEANS            3
+#define WORKER_TRAIN_UPDATE_MODELS     4
+#define WORKER_TRAIN_RELEASE_DIMENSION 5
+#define WORKER_TRAIN_UPDATE_HOST       6
 
 /*
  * Functions to convert enums to strings
@@ -264,7 +263,14 @@ ml_queue_pop(ml_queue_t *q)
 {
     netdata_mutex_lock(&q->mutex);
 
-    ml_training_request_t req = { NULL, NULL, 0, 0, 0 };
+    ml_training_request_t req = {
+        NULL, // host_id
+        NULL, // chart id
+        NULL, // dimension id
+        0, // current time
+        0, // first entry
+        0  // last entry
+    };
 
     while (q->internal.empty()) {
         pthread_cond_wait(&q->cond_var, &q->mutex);
@@ -307,7 +313,7 @@ ml_queue_signal(ml_queue_t *q)
 */
 
 static std::pair<calculated_number_t *, ml_training_response_t>
-ml_dimension_calculated_numbers(ml_dimension_t *dim, const ml_training_request_t &training_request)
+ml_dimension_calculated_numbers(ml_training_thread_t *training_thread, ml_dimension_t *dim, const ml_training_request_t &training_request)
 {
     ml_training_response_t training_response = {};
 
@@ -351,7 +357,7 @@ ml_dimension_calculated_numbers(ml_dimension_t *dim, const ml_training_request_t
               STORAGE_PRIORITY_BEST_EFFORT);
 
     size_t idx = 0;
-    memset(tls_data.training_cns, 0, sizeof(calculated_number_t) * max_n * (Cfg.lag_n + 1));
+    memset(training_thread->training_cns, 0, sizeof(calculated_number_t) * max_n * (Cfg.lag_n + 1));
     calculated_number_t last_value = std::numeric_limits<calculated_number_t>::quiet_NaN();
 
     while (!ops->is_finished(&handle)) {
@@ -368,11 +374,11 @@ ml_dimension_calculated_numbers(ml_dimension_t *dim, const ml_training_request_t
                 training_response.db_after_t = timestamp;
             training_response.db_before_t = timestamp;
 
-            tls_data.training_cns[idx] = value;
-            last_value = tls_data.training_cns[idx];
+            training_thread->training_cns[idx] = value;
+            last_value = training_thread->training_cns[idx];
             training_response.collected_values++;
         } else
-            tls_data.training_cns[idx] = last_value;
+            training_thread->training_cns[idx] = last_value;
 
         idx++;
     }
@@ -387,20 +393,21 @@ ml_dimension_calculated_numbers(ml_dimension_t *dim, const ml_training_request_t
     }
 
     // Find first non-NaN value.
-    for (idx = 0; std::isnan(tls_data.training_cns[idx]); idx++, training_response.total_values--) { }
+    for (idx = 0; std::isnan(training_thread->training_cns[idx]); idx++, training_response.total_values--) { }
 
     // Overwrite NaN values.
     if (idx != 0)
-        memmove(tls_data.training_cns, &tls_data.training_cns[idx], sizeof(calculated_number_t) * training_response.total_values);
+        memmove(training_thread->training_cns, &training_thread->training_cns[idx], sizeof(calculated_number_t) * training_response.total_values);
 
     training_response.result = TRAINING_RESULT_OK;
-    return { tls_data.training_cns, training_response };
+    return { training_thread->training_cns, training_response };
 }
 
 static enum ml_training_result
-ml_dimension_train_model(ml_dimension_t *dim, const ml_training_request_t &training_request)
+ml_dimension_train_model(ml_training_thread_t *training_thread, ml_dimension_t *dim, const ml_training_request_t &training_request)
 {
-    auto P = ml_dimension_calculated_numbers(dim, training_request);
+    worker_is_busy(WORKER_TRAIN_QUERY);
+    auto P = ml_dimension_calculated_numbers(training_thread, dim, training_request);
     ml_training_response_t training_response = P.second;
 
     if (training_response.result != TRAINING_RESULT_OK) {
@@ -429,15 +436,16 @@ ml_dimension_train_model(ml_dimension_t *dim, const ml_training_request_t &train
     }
 
     // compute kmeans
+    worker_is_busy(WORKER_TRAIN_KMEANS);
     {
-        memcpy(tls_data.scratch_training_cns, tls_data.training_cns,
+        memcpy(training_thread->scratch_training_cns, training_thread->training_cns,
                training_response.total_values * sizeof(calculated_number_t));
 
         ml_features_t features = {
             Cfg.diff_n, Cfg.smooth_n, Cfg.lag_n,
-            tls_data.scratch_training_cns, training_response.total_values,
-            tls_data.training_cns, training_response.total_values,
-            tls_data.training_samples
+            training_thread->scratch_training_cns, training_response.total_values,
+            training_thread->training_cns, training_response.total_values,
+            training_thread->training_samples
         };
         ml_features_preprocess(&features);
 
@@ -446,6 +454,7 @@ ml_dimension_train_model(ml_dimension_t *dim, const ml_training_request_t &train
     }
 
     // update kmeans models
+    worker_is_busy(WORKER_TRAIN_UPDATE_MODELS);
     {
         netdata_mutex_lock(&dim->mutex);
 
@@ -497,11 +506,16 @@ ml_dimension_schedule_for_training(ml_dimension_t *dim, time_t curr_time)
     }
 
     if (schedule_for_training) {
-        ml_host_t *host = (ml_host_t *) dim->rd->rrdset->rrdhost->ml_host;
         ml_training_request_t req = {
-            string_dup(dim->rd->rrdset->id), string_dup(dim->rd->id),
-            curr_time, rrddim_first_entry_s(dim->rd), rrddim_last_entry_s(dim->rd),
+            string_dup(dim->rd->rrdset->rrdhost->hostname),
+            string_dup(dim->rd->rrdset->id),
+            string_dup(dim->rd->id),
+            curr_time,
+            rrddim_first_entry_s(dim->rd),
+            rrddim_last_entry_s(dim->rd),
         };
+
+        ml_host_t *host = (ml_host_t *) dim->rd->rrdset->rrdhost->ml_host;
         ml_queue_push(host->training_queue, req);
     }
 }
@@ -677,7 +691,6 @@ ml_host_detect_once(ml_host_t *host)
 
     host->mls = {};
     ml_machine_learning_stats_t mls_copy = {};
-    ml_training_stats_t ts_copy = {};
 
     {
         netdata_mutex_lock(&host->mutex);
@@ -721,54 +734,14 @@ ml_host_detect_once(ml_host_t *host)
 
         mls_copy = host->mls;
 
-        /*
-         * training stats
-        */
-        ts_copy = host->ts;
-
-        host->ts.queue_size = 0;
-        host->ts.num_popped_items = 0;
-
-        host->ts.allotted_ut = 0;
-        host->ts.consumed_ut = 0;
-        host->ts.remaining_ut = 0;
-
-        host->ts.training_result_ok = 0;
-        host->ts.training_result_invalid_query_time_range = 0;
-        host->ts.training_result_not_enough_collected_values = 0;
-        host->ts.training_result_null_acquired_dimension = 0;
-        host->ts.training_result_chart_under_replication = 0;
-
         netdata_mutex_unlock(&host->mutex);
     }
 
-    // Calc the avg values
-    if (ts_copy.num_popped_items) {
-        ts_copy.queue_size /= ts_copy.num_popped_items;
-        ts_copy.allotted_ut /= ts_copy.num_popped_items;
-        ts_copy.consumed_ut /= ts_copy.num_popped_items;
-        ts_copy.remaining_ut /= ts_copy.num_popped_items;
-
-        ts_copy.training_result_ok /= ts_copy.num_popped_items;
-        ts_copy.training_result_invalid_query_time_range /= ts_copy.num_popped_items;
-        ts_copy.training_result_not_enough_collected_values /= ts_copy.num_popped_items;
-        ts_copy.training_result_null_acquired_dimension /= ts_copy.num_popped_items;
-        ts_copy.training_result_chart_under_replication /= ts_copy.num_popped_items;
-    } else {
-        ts_copy.queue_size = 0;
-        ts_copy.allotted_ut = 0;
-        ts_copy.consumed_ut = 0;
-        ts_copy.remaining_ut = 0;
-    }
-
     worker_is_busy(WORKER_JOB_DETECTION_DIM_CHART);
     ml_update_dimensions_chart(host, mls_copy);
 
     worker_is_busy(WORKER_JOB_DETECTION_HOST_CHART);
     ml_update_host_and_detection_rate_charts(host, host->host_anomaly_rate * 10000.0);
-
-    worker_is_busy(WORKER_JOB_DETECTION_STATS);
-    ml_update_training_statistics_chart(host, ts_copy);
 }
 
 typedef struct {
@@ -777,18 +750,21 @@ typedef struct {
 } ml_acquired_dimension_t;
 
 static ml_acquired_dimension_t
-ml_acquired_dimension_get(RRDHOST *rh, STRING *chart_id, STRING *dimension_id)
+ml_acquired_dimension_get(STRING *host_id, STRING *chart_id, STRING *dimension_id)
 {
     RRDDIM_ACQUIRED *acq_rd = NULL;
     ml_dimension_t *dim = NULL;
 
-    RRDSET *rs = rrdset_find(rh, string2str(chart_id));
-    if (rs) {
-        acq_rd = rrddim_find_and_acquire(rs, string2str(dimension_id));
-        if (acq_rd) {
-            RRDDIM *rd = rrddim_acquired_to_rrddim(acq_rd);
-            if (rd)
-                dim = (ml_dimension_t *) rd->ml_dimension;
+    RRDHOST *rh = rrdhost_find_by_hostname(string2str(host_id));
+    if (rh) {
+        RRDSET *rs = rrdset_find(rh, string2str(chart_id));
+        if (rs) {
+            acq_rd = rrddim_find_and_acquire(rs, string2str(dimension_id));
+            if (acq_rd) {
+                RRDDIM *rd = rrddim_acquired_to_rrddim(acq_rd);
+                if (rd)
+                    dim = (ml_dimension_t *) rd->ml_dimension;
+            }
         }
     }
 
@@ -809,110 +785,12 @@ ml_acquired_dimension_release(ml_acquired_dimension_t acq_dim)
 }
 
 static enum ml_training_result
-ml_acquired_dimension_train(ml_acquired_dimension_t acq_dim, const ml_training_request_t &TR)
+ml_acquired_dimension_train(ml_training_thread_t *training_thread, ml_acquired_dimension_t acq_dim, const ml_training_request_t &tr)
 {
     if (!acq_dim.dim)
         return TRAINING_RESULT_NULL_ACQUIRED_DIMENSION;
 
-    return ml_dimension_train_model(acq_dim.dim, TR);
-}
-
-#define WORKER_JOB_TRAINING_FIND 0
-#define WORKER_JOB_TRAINING_TRAIN 1
-#define WORKER_JOB_TRAINING_STATS 2
-
-static void
-ml_host_train(ml_host_t *host)
-{
-    worker_register("MLTRAIN");
-    worker_register_job_name(WORKER_JOB_TRAINING_FIND, "find");
-    worker_register_job_name(WORKER_JOB_TRAINING_TRAIN, "train");
-    worker_register_job_name(WORKER_JOB_TRAINING_STATS, "stats");
-
-    service_register(SERVICE_THREAD_TYPE_NETDATA, NULL, (force_quit_t ) ml_host_cancel_training_thread, host->rh, true);
-
-    while (service_running(SERVICE_ML_TRAINING)) {
-        ml_training_request_t training_req = ml_queue_pop(host->training_queue);
-        size_t queue_size = ml_queue_size(host->training_queue) + 1;
-
-        if (host->threads_cancelled) {
-            info("Stopping training thread for host %s because it was cancelled", rrdhost_hostname(host->rh));
-            break;
-        }
-
-        usec_t allotted_ut = (Cfg.train_every * host->rh->rrd_update_every * USEC_PER_SEC) / queue_size;
-        if (allotted_ut > USEC_PER_SEC)
-            allotted_ut = USEC_PER_SEC;
-
-        usec_t start_ut = now_monotonic_usec();
-        enum ml_training_result training_res;
-        {
-            worker_is_busy(WORKER_JOB_TRAINING_FIND);
-            ml_acquired_dimension_t acq_dim = ml_acquired_dimension_get(host->rh, training_req.chart_id, training_req.dimension_id);
-
-            worker_is_busy(WORKER_JOB_TRAINING_TRAIN);
-            training_res = ml_acquired_dimension_train(acq_dim, training_req);
-
-            string_freez(training_req.chart_id);
-            string_freez(training_req.dimension_id);
-
-            ml_acquired_dimension_release(acq_dim);
-        }
-        usec_t consumed_ut = now_monotonic_usec() - start_ut;
-
-        worker_is_busy(WORKER_JOB_TRAINING_STATS);
-
-        usec_t remaining_ut = 0;
-        if (consumed_ut < allotted_ut)
-            remaining_ut = allotted_ut - consumed_ut;
-
-        {
-            netdata_mutex_lock(&host->mutex);
-
-            host->ts.queue_size += queue_size;
-            host->ts.num_popped_items += 1;
-
-            host->ts.allotted_ut += allotted_ut;
-            host->ts.consumed_ut += consumed_ut;
-            host->ts.remaining_ut += remaining_ut;
-
-            switch (training_res) {
-                case TRAINING_RESULT_OK:
-                    host->ts.training_result_ok += 1;
-                    break;
-                case TRAINING_RESULT_INVALID_QUERY_TIME_RANGE:
-                    host->ts.training_result_invalid_query_time_range += 1;
-                    break;
-                case TRAINING_RESULT_NOT_ENOUGH_COLLECTED_VALUES:
-                    host->ts.training_result_not_enough_collected_values += 1;
-                    break;
-                case TRAINING_RESULT_NULL_ACQUIRED_DIMENSION:
-                    host->ts.training_result_null_acquired_dimension += 1;
-                    break;
-                case TRAINING_RESULT_CHART_UNDER_REPLICATION:
-                    host->ts.training_result_chart_under_replication += 1;
-                    break;
-            }
-
-            netdata_mutex_unlock(&host->mutex);
-        }
-
-        worker_is_idle();
-        std::this_thread::sleep_for(std::chrono::microseconds{remaining_ut});
-        worker_is_busy(0);
-    }
-}
-
-static void *
-train_main(void *arg)
-{
-    size_t max_elements_needed_for_training = Cfg.max_train_samples * (Cfg.lag_n + 1);
-    tls_data.training_cns = new calculated_number_t[max_elements_needed_for_training]();
-    tls_data.scratch_training_cns = new calculated_number_t[max_elements_needed_for_training]();
-
-    ml_host_t *host = (ml_host_t *) arg;
-    ml_host_train(host);
-    return NULL;
+    return ml_dimension_train_model(training_thread, acq_dim.dim, tr);
 }
 
 static void *
@@ -926,12 +804,10 @@ ml_detect_main(void *arg)
     worker_register_job_name(WORKER_JOB_DETECTION_HOST_CHART, "host chart");
     worker_register_job_name(WORKER_JOB_DETECTION_STATS, "training stats");
 
-    service_register(SERVICE_THREAD_TYPE_NETDATA, NULL, NULL, NULL, true);
-
     heartbeat_t hb;
     heartbeat_init(&hb);
 
-    while (service_running((SERVICE_TYPE)(SERVICE_ML_PREDICTION | SERVICE_COLLECTORS))) {
+    while (!Cfg.detection_stop) {
         worker_is_idle();
         heartbeat_next(&hb, USEC_PER_SEC);
 
@@ -945,6 +821,39 @@ ml_detect_main(void *arg)
             ml_host_detect_once((ml_host_t *) rh->ml_host);
         }
         dfe_done(rhp);
+
+        if (Cfg.enable_statistics_charts) {
+            // collect and update training thread stats
+            for (size_t idx = 0; idx != Cfg.num_training_threads; idx++) {
+                ml_training_thread_t *training_thread = &Cfg.training_threads[idx];
+
+                netdata_mutex_lock(&training_thread->nd_mutex);
+                ml_training_stats_t training_stats = training_thread->training_stats;
+                training_thread->training_stats = {};
+                netdata_mutex_unlock(&training_thread->nd_mutex);
+
+                // calc the avg values
+                if (training_stats.num_popped_items) {
+                    training_stats.queue_size /= training_stats.num_popped_items;
+                    training_stats.allotted_ut /= training_stats.num_popped_items;
+                    training_stats.consumed_ut /= training_stats.num_popped_items;
+                    training_stats.remaining_ut /= training_stats.num_popped_items;
+                } else {
+                    training_stats.queue_size = 0;
+                    training_stats.allotted_ut = 0;
+                    training_stats.consumed_ut = 0;
+                    training_stats.remaining_ut = 0;
+
+                    training_stats.training_result_ok = 0;
+                    training_stats.training_result_invalid_query_time_range = 0;
+                    training_stats.training_result_not_enough_collected_values = 0;
+                    training_stats.training_result_null_acquired_dimension = 0;
+                    training_stats.training_result_chart_under_replication = 0;
+                }
+
+                ml_update_training_statistics_chart(training_thread, training_stats);
+            }
+        }
     }
 
     return NULL;
@@ -978,31 +887,6 @@ bool ml_streaming_enabled()
     return Cfg.stream_anomaly_detection_charts;
 }
 
-void ml_init()
-{
-    // Read config values
-    ml_config_load(&Cfg);
-
-    if (!Cfg.enable_anomaly_detection)
-        return;
-
-    // Generate random numbers to efficiently sample the features we need
-    // for KMeans clustering.
-    std::random_device RD;
-    std::mt19937 Gen(RD());
-
-    Cfg.random_nums.reserve(Cfg.max_train_samples);
-    for (size_t Idx = 0; Idx != Cfg.max_train_samples; Idx++)
-        Cfg.random_nums.push_back(Gen());
-
-
-    // start detection & training threads
-    char tag[NETDATA_THREAD_TAG_MAX + 1];
-
-    snprintfz(tag, NETDATA_THREAD_TAG_MAX, "%s", "PREDICT");
-    netdata_thread_create(&Cfg.detection_thread, tag, NETDATA_THREAD_OPTION_JOINABLE, ml_detect_main, NULL);
-}
-
 void ml_host_new(RRDHOST *rh)
 {
     if (!ml_enabled(rh))
@@ -1012,14 +896,12 @@ void ml_host_new(RRDHOST *rh)
 
     host->rh = rh;
     host->mls = ml_machine_learning_stats_t();
-    host->ts = ml_training_stats_t();
+    //host->ts = ml_training_stats_t();
 
-    host->host_anomaly_rate = 0.0;
-    host->threads_running = false;
-    host->threads_cancelled = false;
-    host->threads_joined = false;
+    static std::atomic<size_t> times_called(0);
+    host->training_queue = Cfg.training_threads[times_called++ % Cfg.num_training_threads].training_queue;
 
-    host->training_queue = ml_queue_init();
+    host->host_anomaly_rate = 0.0;
 
     netdata_mutex_init(&host->mutex);
 
@@ -1033,7 +915,6 @@ void ml_host_delete(RRDHOST *rh)
         return;
 
     netdata_mutex_destroy(&host->mutex);
-    ml_queue_destroy(host->training_queue);
 
     delete host;
     rh->ml_host = NULL;
@@ -1100,69 +981,6 @@ void ml_host_get_models(RRDHOST *rh, BUFFER *wb)
     error("Fetching KMeans models is not supported yet");
 }
 
-void ml_host_start_training_thread(RRDHOST *rh)
-{
-    if (!rh || !rh->ml_host)
-        return;
-
-    ml_host_t *host = (ml_host_t *) rh->ml_host;
-
-    if (host->threads_running) {
-        error("Anomaly detections threads for host %s are already-up and running.", rrdhost_hostname(host->rh));
-        return;
-    }
-
-    host->threads_running = true;
-    host->threads_cancelled = false;
-    host->threads_joined = false;
-
-    char tag[NETDATA_THREAD_TAG_MAX + 1];
-
-    snprintfz(tag, NETDATA_THREAD_TAG_MAX, "MLTR[%s]", rrdhost_hostname(host->rh));
-    netdata_thread_create(&host->training_thread, tag, NETDATA_THREAD_OPTION_JOINABLE, train_main, static_cast<void *>(host));
-}
-
-void ml_host_cancel_training_thread(RRDHOST *rh)
-{
-    if (!rh || !rh->ml_host)
-        return;
-
-    ml_host_t *host = (ml_host_t *) rh->ml_host;
-
-    if (!host->threads_running) {
-        error("Anomaly detections threads for host %s have already been stopped.", rrdhost_hostname(host->rh));
-        return;
-    }
-
-    if (!host->threads_cancelled) {
-        host->threads_cancelled = true;
-
-        // Signal the training queue to stop popping-items
-        ml_queue_signal(host->training_queue);
-        netdata_thread_cancel(host->training_thread);
-    }
-}
-
-void ml_host_stop_training_thread(RRDHOST *rh)
-{
-    if (!rh || !rh->ml_host)
-        return;
-
-    ml_host_cancel_training_thread(rh);
-
-    ml_host_t *host = (ml_host_t *) rh->ml_host;
-
-    if (!host->threads_joined) {
-        host->threads_joined = true;
-        host->threads_running = false;
-
-        delete[] tls_data.training_cns;
-        delete[] tls_data.scratch_training_cns;
-
-        netdata_thread_join(host->training_thread, NULL);
-    }
-}
-
 void ml_chart_new(RRDSET *rs)
 {
     ml_host_t *host = (ml_host_t *) rs->rrdhost->ml_host;
@@ -1267,3 +1085,185 @@ bool ml_dimension_is_anomalous(RRDDIM *rd, time_t curr_time, double value, bool
 
     return is_anomalous;
 }
+
+static void *ml_train_main(void *arg) {
+    ml_training_thread_t *training_thread = (ml_training_thread_t *) arg;
+
+    char worker_name[1024];
+    snprintfz(worker_name, 1024, "training_thread_%zu", training_thread->id);
+    worker_register("MLTRAIN");
+
+    worker_register_job_name(WORKER_TRAIN_QUEUE_POP, "pop queue");
+    worker_register_job_name(WORKER_TRAIN_ACQUIRE_DIMENSION, "acquire");
+    worker_register_job_name(WORKER_TRAIN_QUERY, "query");
+    worker_register_job_name(WORKER_TRAIN_KMEANS, "kmeans");
+    worker_register_job_name(WORKER_TRAIN_UPDATE_MODELS, "update models");
+    worker_register_job_name(WORKER_TRAIN_RELEASE_DIMENSION, "release");
+    worker_register_job_name(WORKER_TRAIN_UPDATE_HOST, "update host");
+
+    while (!Cfg.training_stop) {
+        worker_is_busy(WORKER_TRAIN_QUEUE_POP);
+
+        ml_training_request_t training_req = ml_queue_pop(training_thread->training_queue);
+
+        // we know this thread has been cancelled, when the queue starts
+        // returning "null" requests without blocking on queue's pop().
+        if (training_req.host_id == NULL)
+            break;
+
+        size_t queue_size = ml_queue_size(training_thread->training_queue) + 1;
+
+        usec_t allotted_ut = (Cfg.train_every * USEC_PER_SEC) / queue_size;
+        if (allotted_ut > USEC_PER_SEC)
+            allotted_ut = USEC_PER_SEC;
+
+        usec_t start_ut = now_monotonic_usec();
+
+        enum ml_training_result training_res;
+        {
+            worker_is_busy(WORKER_TRAIN_ACQUIRE_DIMENSION);
+            ml_acquired_dimension_t acq_dim = ml_acquired_dimension_get(
+                training_req.host_id,
+                training_req.chart_id,
+                training_req.dimension_id);
+
+            training_res = ml_acquired_dimension_train(training_thread, acq_dim, training_req);
+
+            string_freez(training_req.host_id);
+            string_freez(training_req.chart_id);
+            string_freez(training_req.dimension_id);
+
+            worker_is_busy(WORKER_TRAIN_RELEASE_DIMENSION);
+            ml_acquired_dimension_release(acq_dim);
+        }
+
+        usec_t consumed_ut = now_monotonic_usec() - start_ut;
+
+        usec_t remaining_ut = 0;
+        if (consumed_ut < allotted_ut)
+            remaining_ut = allotted_ut - consumed_ut;
+
+        if (Cfg.enable_statistics_charts) {
+            worker_is_busy(WORKER_TRAIN_UPDATE_HOST);
+
+            netdata_mutex_lock(&training_thread->nd_mutex);
+
+            training_thread->training_stats.queue_size += queue_size;
+            training_thread->training_stats.num_popped_items += 1;
+
+            training_thread->training_stats.allotted_ut += allotted_ut;
+            training_thread->training_stats.consumed_ut += consumed_ut;
+            training_thread->training_stats.remaining_ut += remaining_ut;
+
+            switch (training_res) {
+                case TRAINING_RESULT_OK:
+                    training_thread->training_stats.training_result_ok += 1;
+                    break;
+                case TRAINING_RESULT_INVALID_QUERY_TIME_RANGE:
+                    training_thread->training_stats.training_result_invalid_query_time_range += 1;
+                    break;
+                case TRAINING_RESULT_NOT_ENOUGH_COLLECTED_VALUES:
+                    training_thread->training_stats.training_result_not_enough_collected_values += 1;
+                    break;
+                case TRAINING_RESULT_NULL_ACQUIRED_DIMENSION:
+                    training_thread->training_stats.training_result_null_acquired_dimension += 1;
+                    break;
+                case TRAINING_RESULT_CHART_UNDER_REPLICATION:
+                    training_thread->training_stats.training_result_chart_under_replication += 1;
+                    break;
+            }
+
+            netdata_mutex_unlock(&training_thread->nd_mutex);
+        }
+
+        worker_is_idle();
+        std::this_thread::sleep_for(std::chrono::microseconds{remaining_ut});
+    }
+
+    return NULL;
+}
+
+void ml_init()
+{
+    // Read config values
+    ml_config_load(&Cfg);
+
+    if (!Cfg.enable_anomaly_detection)
+        return;
+
+    // Generate random numbers to efficiently sample the features we need
+    // for KMeans clustering.
+    std::random_device RD;
+    std::mt19937 Gen(RD());
+
+    Cfg.random_nums.reserve(Cfg.max_train_samples);
+    for (size_t Idx = 0; Idx != Cfg.max_train_samples; Idx++)
+        Cfg.random_nums.push_back(Gen());
+
+
+    // start detection & training threads
+    Cfg.detection_stop = false;
+    Cfg.training_stop = false;
+
+    char tag[NETDATA_THREAD_TAG_MAX + 1];
+
+    snprintfz(tag, NETDATA_THREAD_TAG_MAX, "%s", "PREDICT");
+    netdata_thread_create(&Cfg.detection_thread, tag, NETDATA_THREAD_OPTION_JOINABLE, ml_detect_main, NULL);
+
+    Cfg.training_threads.resize(Cfg.num_training_threads);
+    for (size_t idx = 0; idx != Cfg.num_training_threads; idx++) {
+        ml_training_thread_t *training_thread = &Cfg.training_threads[idx];
+
+
+        size_t max_elements_needed_for_training = Cfg.max_train_samples * (Cfg.lag_n + 1);
+        training_thread->training_cns = new calculated_number_t[max_elements_needed_for_training]();
+        training_thread->scratch_training_cns = new calculated_number_t[max_elements_needed_for_training]();
+
+        training_thread->id = idx;
+        training_thread->training_queue = ml_queue_init();
+        netdata_mutex_init(&training_thread->nd_mutex);
+
+        snprintfz(tag, NETDATA_THREAD_TAG_MAX, "TRAIN[%zu]", training_thread->id);
+        netdata_thread_create(&training_thread->nd_thread, tag, NETDATA_THREAD_OPTION_JOINABLE, ml_train_main, training_thread);
+    }
+}
+
+void ml_fini()
+{
+    Cfg.detection_stop = true;
+    Cfg.training_stop = true;
+
+    netdata_thread_cancel(Cfg.detection_thread);
+    netdata_thread_join(Cfg.detection_thread, NULL);
+
+    // signal the training queue of each thread
+    for (size_t idx = 0; idx != Cfg.num_training_threads; idx++) {
+        ml_training_thread_t *training_thread = &Cfg.training_threads[idx];
+
+        ml_queue_signal(training_thread->training_queue);
+    }
+
+    // cancel training threads
+    for (size_t idx = 0; idx != Cfg.num_training_threads; idx++) {
+        ml_training_thread_t *training_thread = &Cfg.training_threads[idx];
+
+        netdata_thread_cancel(training_thread->nd_thread);
+    }
+
+    // join training threads
+    for (size_t idx = 0; idx != Cfg.num_training_threads; idx++) {
+        ml_training_thread_t *training_thread = &Cfg.training_threads[idx];
+
+        netdata_thread_join(training_thread->nd_thread, NULL);
+    }
+
+    // clear training thread data
+    for (size_t idx = 0; idx != Cfg.num_training_threads; idx++) {
+        ml_training_thread_t *training_thread = &Cfg.training_threads[idx];
+
+        delete[] training_thread->training_cns;
+        delete[] training_thread->scratch_training_cns;
+        ml_queue_destroy(training_thread->training_queue);
+        netdata_mutex_destroy(&training_thread->nd_mutex);
+    }
+}