From 5da14042f70711ea5cf66e034699730335462f66 Mon Sep 17 00:00:00 2001 From: Daniel Baumann Date: Sun, 5 May 2024 14:08:03 +0200 Subject: Merging upstream version 1.45.3+dfsg. Signed-off-by: Daniel Baumann --- src/ml/Config.cc | 139 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 139 insertions(+) create mode 100644 src/ml/Config.cc (limited to 'src/ml/Config.cc') diff --git a/src/ml/Config.cc b/src/ml/Config.cc new file mode 100644 index 000000000..c6a750995 --- /dev/null +++ b/src/ml/Config.cc @@ -0,0 +1,139 @@ +// SPDX-License-Identifier: GPL-3.0-or-later + +#include "ml-private.h" + +/* + * Global configuration instance to be shared between training and + * prediction threads. + */ +ml_config_t Cfg; + +template +static T clamp(const T& Value, const T& Min, const T& Max) { + return std::max(Min, std::min(Value, Max)); +} + +/* + * Initialize global configuration variable. + */ +void ml_config_load(ml_config_t *cfg) { + const char *config_section_ml = CONFIG_SECTION_ML; + + bool enable_anomaly_detection = config_get_boolean(config_section_ml, "enabled", true); + + /* + * Read values + */ + + unsigned max_train_samples = config_get_number(config_section_ml, "maximum num samples to train", 6 * 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", 3 * 3600); + + unsigned num_models_to_use = config_get_number(config_section_ml, "number of models per dimension", 18); + unsigned delete_models_older_than = config_get_number(config_section_ml, "delete models older than", 60 * 60 * 24 * 7); + + unsigned diff_n = config_get_number(config_section_ml, "num samples to diff", 1); + unsigned smooth_n = config_get_number(config_section_ml, "num samples to smooth", 3); + unsigned lag_n = config_get_number(config_section_ml, "num samples to lag", 5); + + double random_sampling_ratio = config_get_float(config_section_ml, "random sampling ratio", 1.0 / 5.0 /* default lag_n */); + unsigned max_kmeans_iters = config_get_number(config_section_ml, "maximum number of k-means iterations", 1000); + + double dimension_anomaly_rate_threshold = config_get_float(config_section_ml, "dimension anomaly score threshold", 0.99); + + double host_anomaly_rate_threshold = config_get_float(config_section_ml, "host anomaly rate threshold", 1.0); + std::string anomaly_detection_grouping_method = config_get(config_section_ml, "anomaly detection grouping method", "average"); + time_t anomaly_detection_query_duration = config_get_number(config_section_ml, "anomaly detection grouping duration", 5 * 60); + + size_t num_training_threads = config_get_number(config_section_ml, "num training threads", 4); + size_t flush_models_batch_size = config_get_number(config_section_ml, "flush models batch size", 128); + + size_t suppression_window = config_get_number(config_section_ml, "dimension anomaly rate suppression window", 900); + size_t suppression_threshold = config_get_number(config_section_ml, "dimension anomaly rate suppression threshold", suppression_window / 2); + + bool enable_statistics_charts = config_get_boolean(config_section_ml, "enable statistics charts", false); + + /* + * Clamp + */ + + max_train_samples = clamp(max_train_samples, 1 * 3600, 24 * 3600); + min_train_samples = clamp(min_train_samples, 1 * 900, 6 * 3600); + train_every = clamp(train_every, 1 * 3600, 6 * 3600); + + num_models_to_use = clamp(num_models_to_use, 1, 7 * 24); + delete_models_older_than = clamp(delete_models_older_than, 60 * 60 * 24 * 1, 60 * 60 * 24 * 7); + + diff_n = clamp(diff_n, 0u, 1u); + smooth_n = clamp(smooth_n, 0u, 5u); + lag_n = clamp(lag_n, 1u, 5u); + + random_sampling_ratio = clamp(random_sampling_ratio, 0.2, 1.0); + max_kmeans_iters = clamp(max_kmeans_iters, 500u, 1000u); + + dimension_anomaly_rate_threshold = clamp(dimension_anomaly_rate_threshold, 0.01, 5.00); + + host_anomaly_rate_threshold = clamp(host_anomaly_rate_threshold, 0.1, 10.0); + anomaly_detection_query_duration = clamp(anomaly_detection_query_duration, 60, 15 * 60); + + num_training_threads = clamp(num_training_threads, 1, 128); + flush_models_batch_size = clamp(flush_models_batch_size, 8, 512); + + suppression_window = clamp(suppression_window, 1, max_train_samples); + suppression_threshold = clamp(suppression_threshold, 1, suppression_window); + + /* + * Validate + */ + + if (min_train_samples >= max_train_samples) { + netdata_log_error("invalid min/max train samples found (%u >= %u)", min_train_samples, max_train_samples); + + min_train_samples = 1 * 3600; + max_train_samples = 6 * 3600; + } + + /* + * Assign to config instance + */ + + cfg->enable_anomaly_detection = enable_anomaly_detection; + + cfg->max_train_samples = max_train_samples; + cfg->min_train_samples = min_train_samples; + cfg->train_every = train_every; + + cfg->num_models_to_use = num_models_to_use; + cfg->delete_models_older_than = delete_models_older_than; + + cfg->diff_n = diff_n; + cfg->smooth_n = smooth_n; + cfg->lag_n = lag_n; + + cfg->random_sampling_ratio = random_sampling_ratio; + cfg->max_kmeans_iters = max_kmeans_iters; + + cfg->host_anomaly_rate_threshold = host_anomaly_rate_threshold; + cfg->anomaly_detection_grouping_method = + time_grouping_parse(anomaly_detection_grouping_method.c_str(), RRDR_GROUPING_AVERAGE); + cfg->anomaly_detection_query_duration = anomaly_detection_query_duration; + cfg->dimension_anomaly_score_threshold = dimension_anomaly_rate_threshold; + + cfg->hosts_to_skip = config_get(config_section_ml, "hosts to skip from training", "!*"); + cfg->sp_host_to_skip = simple_pattern_create(cfg->hosts_to_skip.c_str(), NULL, SIMPLE_PATTERN_EXACT, true); + + // Always exclude anomaly_detection charts from training. + cfg->charts_to_skip = "anomaly_detection.* "; + cfg->charts_to_skip += config_get(config_section_ml, "charts to skip from training", "netdata.*"); + cfg->sp_charts_to_skip = simple_pattern_create(cfg->charts_to_skip.c_str(), NULL, SIMPLE_PATTERN_EXACT, true); + + cfg->stream_anomaly_detection_charts = config_get_boolean(config_section_ml, "stream anomaly detection charts", true); + + cfg->num_training_threads = num_training_threads; + cfg->flush_models_batch_size = flush_models_batch_size; + + cfg->suppression_window = suppression_window; + cfg->suppression_threshold = suppression_threshold; + + cfg->enable_statistics_charts = enable_statistics_charts; +} -- cgit v1.2.3