From 6278c6ddf2536e1aebc9cfd87f9c6a1396ecd8dc Mon Sep 17 00:00:00 2001
From: Fred Navruzov <fred-navruzov@users.noreply.github.com>
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. <br><br> 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. <br><br> 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
<td></td>
<td>BasicAuth password</td>
</tr>
+ <tr>
+ <td><code>verify_tls</code></td>
+ <td><code>False</code></td>
+ <td>Allows disabling TLS verification of the remote certificate.</td>
+ </tr>
<tr>
<td><code>timeout</code></td>
<td><code>"5s"</code></td>
@@ -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"