**Preset** mode allows for simpler configuration and anomaly detection with `vmanomaly` on widely-recognized metrics, such as those generated by [node_exporter](https://github.com/prometheus/node_exporter), which are typically challenging to monitor using static threshold-based alerting rules.
This approach represents a paradigm shift from traditional [static threshold-based alerting rules](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#rule-based-alerting), focused on *raw metric values*, to *static* rules based on [`anomaly_scores`](./FAQ.md#what-is-anomaly-score). These scores offer a consistent, default threshold that remains stable over time, being adjusted for trends, seasonality, data scale, thus, reducing the engineering effort required for maintenance. Anomaly scores are produced by [machine learning models](./components/models.md), which are regularly retrained on varying time frames, ensuring alerts remain current and responsive to evolving data patterns.
Additionally, **preset mode** minimizes user input needed to run the service. You can configure `vmanomaly` by specifying only the preset name and data sources in the [`reader`](./components/reader.md) and [`writer`](./components/writer.md) sections of the configuration file. All other parameters are already preconfigured.
After you run `vmanomaly` with `preset` arg specified, available assets can be viewed, copied and downloaded at `http://localhost:8490/presets/` endpoint.
The Node-Exporter preset simplifies the monitoring and anomaly detection of key system metrics collected by [`node_exporter`](https://github.com/prometheus/node_exporter). This preset reduces the need for manual configuration and detects anomalies in metrics such as CPU usage, network errors, and disk latency, ensuring timely identification of potential issues. Below are detailed instructions on enabling and using the Node-Exporter preset, along with a list of included assets like alerting rules and Grafana dashboard.
> **Note: Node-Exporter preset assets can be also found [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/)**
For enabling Node-Exporter in config file set the `preset` arg accordingly. Also, include at least `datasource_url`-s (and `tenant_id` if using cluster version of VictoriaMetrics) in reader and writer sections, like that:
Machine learning models will be fit for each timeseries, returned by underlying [MetricsQL](../MetricsQL.md) queries.
Anomaly score metric labels will also contain [model classes](./components/models.md) and [schedulers](./components/scheduler.md) for labelset uniqueness.
> For optimal alerting experience, we include [Awesome alerts](https://github.com/samber/awesome-prometheus-alerts) to cover indicators not addressed by the preset, as static thresholds can effectively complement our machine learning approach.
Grafana dashboard `.json` file can be found [here](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/dashboard.json): `http://localhost:8490/presets/dashboard.json`
Number of major faults that have occurred since the last update. Major faults occur when a process tries to access a page in memory that is not currently mapped in the process's address space, and it requires loading data from the disk.
Here's how attached [Grafana dashboard](https://github.com/VictoriaMetrics/VictoriaMetrics/tree/master/deployment/docker/vmanomaly/vmanomaly-node-exporter-preset/dashboard.json) can be used to drill down anomalies:
On the (global) graph **'Percentage of Anomalies'**, you can see a spike 8.75% of anomalies at the timestamp '2024-06-03 10:35:00'. The (global) graph **'Anomalies per Indicator'** shows the indicators that were anomalous at the corresponding time.
