From 6278c6ddf2536e1aebc9cfd87f9c6a1396ecd8dc Mon Sep 17 00:00:00 2001 From: Fred Navruzov Date: Mon, 15 Jul 2024 16:05:33 +0200 Subject: [PATCH] docs/vmanomaly - v1.13.2 updates (#6646) ### Describe Your Changes Doc updates after v1.13.2 release of `vmanomaly` ### Checklist The following checks are **mandatory**: - [ ] My change adheres [VictoriaMetrics contributing guidelines](https://docs.victoriametrics.com/contributing/). --- docs/anomaly-detection/CHANGELOG.md | 7 +++++++ docs/anomaly-detection/components/models.md | 5 ++++- docs/anomaly-detection/components/monitoring.md | 6 ++++++ 3 files changed, 17 insertions(+), 1 deletion(-) diff --git a/docs/anomaly-detection/CHANGELOG.md b/docs/anomaly-detection/CHANGELOG.md index f81360202c..73e119ac86 100644 --- a/docs/anomaly-detection/CHANGELOG.md +++ b/docs/anomaly-detection/CHANGELOG.md @@ -17,6 +17,13 @@ Please find the changelog for VictoriaMetrics Anomaly Detection below. > **Important note: Users are strongly encouraged to upgrade to `vmanomaly` [v1.9.2](https://hub.docker.com/repository/docker/victoriametrics/vmanomaly/tags?page=1&ordering=name) or newer for optimal performance and accuracy.

This recommendation is crucial for configurations with a low `infer_every` parameter [in your scheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#parameters-1), and in scenarios where data exhibits significant high-order seasonality patterns (such as hourly or daily cycles). Previous versions from v1.5.1 to v1.8.0 were identified to contain a critical issue impacting model training, where models were inadvertently trained on limited data subsets, leading to suboptimal fits, affecting the accuracy of anomaly detection.

Upgrading to v1.9.2 addresses this issue, ensuring proper model training and enhanced reliability. For users utilizing Helm charts, it is recommended to upgrade to version [1.0.0](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/CHANGELOG.md#100) or newer.** +## v1.13.2 +Released: 2024-07-15 +- IMPROVEMENT: update `node-exporter` [preset](/anomaly-detection/presets/#node-exporter) to reduce [false positives](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/index.html#false-positive) +- FIX: add `verify_tls` arg for [`push`](/anomaly-detection/components/monitoring/#push-config-parameters) monitoring section. Also, `verify_tls` is now correctly used in [VmWriter](/anomaly-detection/components/writer/#vm-writer). +- FIX: now [`AutoTuned`](/anomaly-detection/components/models/#autotuned) model wrapper works correctly in [on-disk model storage mode](/anomaly-detection/faq/#resource-consumption-of-vmanomaly). +- FIX: now [rolling models](/anomaly-detection/components/models/#rolling-models), like [`RollingQuantile`](/anomaly-detection/components/models/#rolling-quantile) are properly handled in [One-off scheduler](/anomaly-detection/components/scheduler/#oneoff-scheduler), when wrapped in [`AutoTuned`](/anomaly-detection/components/models/#autotuned) + ## v1.13.0 Released: 2024-06-11 - FEATURE: Introduced `preset` [mode to run vmanomaly service](/anomaly-detection/presets) with minimal user input and on widely-known metrics, like those produced by [`node_exporter`](/anomaly-detection/presets#node-exporter). diff --git a/docs/anomaly-detection/components/models.md b/docs/anomaly-detection/components/models.md index eddec6d58b..961c42fc1b 100644 --- a/docs/anomaly-detection/components/models.md +++ b/docs/anomaly-detection/components/models.md @@ -353,7 +353,10 @@ models: # ... ``` -**Note**: Autotune can't be made on your [custom model](#custom-model-guide). Also, it can't be applied to itself (like `tuned_class_name: 'model.auto.AutoTunedModel'`) +> **Note**: There are some expected limitations of Autotune mode: +> - It can't be made on your [custom model](#custom-model-guide). +> - It can't be applied to itself (like `tuned_class_name: 'model.auto.AutoTunedModel'`) +> - `AutoTunedModel` can't be used on [rolling models](/anomaly-detection/components/models/#rolling-models) like [`RollingQuantile`](/anomaly-detection/components/models/#rolling-quantile) in combination with [on-disk model storage mode](/anomaly-detection/faq/#resource-consumption-of-vmanomaly), as the rolling models exists only during `infer` calls and aren't persisted neither in RAM, nor on disk. ### [Prophet](https://facebook.github.io/prophet/) diff --git a/docs/anomaly-detection/components/monitoring.md b/docs/anomaly-detection/components/monitoring.md index b86981728e..27be41d0b3 100644 --- a/docs/anomaly-detection/components/monitoring.md +++ b/docs/anomaly-detection/components/monitoring.md @@ -75,6 +75,11 @@ There are 2 models to monitor VictoriaMetrics Anomaly Detection behavior - [push BasicAuth password + + verify_tls + False + Allows disabling TLS verification of the remote certificate. + timeout "5s" @@ -100,6 +105,7 @@ monitoring: tenant_id: "0:0" # For cluster version only user: "USERNAME" password: "PASSWORD" + verify_tls: False timeout: "5s" extra_labels: job: "vmanomaly-push"