VictoriaMetrics/docs/anomaly-detection/QuickStart.md

---
sort: 1
weight: 1
title: VictoriaMetrics Anomaly Detection Quick Start
menu:
  docs:
    parent: "anomaly-detection"
    weight: 1
    title: Quick Start
aliases:
- /anomaly-detection/QuickStart.html
---

# VictoriaMetrics Anomaly Detection Quick Start

For service introduction visit [README](/anomaly-detection/) page
and [Overview](/anomaly-detection/overview.html) of how `vmanomaly` works.

## How to install and run `vmanomaly`

> To run `vmanomaly` you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/index.html).

The following options are available:

- [To run Docker image](#docker-image)
- [To run in Kubernetes with Helm charts](#helm-charts)


### Docker
> To run `vmanomaly` you need to have a VictoriaMetrics Enterprise [licence](https://victoriametrics.com/products/enterprise/) or request a trial [here](https://victoriametrics.com/products/enterprise/trial/index.html).

Below are the steps to get `vmanomaly` up and running inside a Docker container:

1. Pull Docker image:

```sh
docker pull victoriametrics/vmanomaly:latest
```

2. (Optional step) tag the `vmanomaly` Docker image:

```sh
docker image tag victoriametrics/vmanomaly:latest vmanomaly
```

3. Start the `vmanomaly` Docker container with a *license file*, use the command below.
**Make sure to replace `YOUR_LICENSE_FILE_PATH`, and `YOUR_CONFIG_FILE_PATH` with your specific details**:

```sh
export YOUR_LICENSE_FILE_PATH=path/to/license/file
export YOUR_CONFIG_FILE_PATH=path/to/config/file
docker run -it -v $YOUR_LICENSE_FILE_PATH:/license \
               -v $YOUR_CONFIG_FILE_PATH:/config.yml \
               vmanomaly /config.yml \
               --license-file=/license
```

See also:

- You can verify licence online and offline. See the details [here](/anomaly-detection/overview/#licensing).
- [How to configure `vmanomaly`](#how-to-configure-vmanomaly)

### Kubernetes with Helm charts

You can run `vmanomaly` in Kubernetes environment
with [these Helm charts](https://github.com/VictoriaMetrics/helm-charts/blob/master/charts/victoria-metrics-anomaly/README.md).


## How to configure vmanomaly
To run `vmanomaly` you need to set up configuration file in `yaml` format.

Here is an example of config file that will run [Facebook Prophet](https://facebook.github.io/prophet/) model, that will be retrained every 2 hours on 14 days of previous data. It will generate inference (including `anomaly_score` metric) every 1 minute.


```yaml
scheduler:
  infer_every: "1m"
  fit_every: "2h"
  fit_window: "14d"

models:
  prophet_model:
    class: "model.prophet.ProphetModel"
    args:
      interval_width: 0.98

reader:
  datasource_url: "http://victoriametrics:8428/" # [YOUR_DATASOURCE_URL]
  sampling_period: "1m"
  queries: 
    # define your queries with MetricsQL - https://docs.victoriametrics.com/metricsql/
    cache: "sum(rate(vm_cache_entries))"

writer:
  datasource_url:  "http://victoriametrics:8428/" # [YOUR_DATASOURCE_URL]
```


Next steps:
- Define how often to run and make inferences in the [scheduler](/anomaly-detection/components/scheduler/) section of a config file.
- Setup the datasource to read data from in the [reader](/anomaly-detection/components/reader/) section.
- Specify where and how to store anomaly detection metrics in the [writer](/anomaly-detection/components/writer/) section.
- Configure built-in models parameters according to your needs in the [models](/anomaly-detection/components/models/) section.
- Integrate your [custom models](/anomaly-detection/components/models/#custom-model-guide) with `vmanomaly`.
- Define queries for input data using [MetricsQL](https://docs.victoriametrics.com/metricsql/).


## Check also

Here are other materials that you might find useful:

- [Guide: Anomaly Detection and Alerting Setup](/anomaly-detection/guides/guide-vmanomaly-vmalert/)
- [FAQ](/anomaly-detection/faq/)
- [Changelog](/anomaly-detection/changelog/)
- [Anomaly Detection Blog](https://victoriametrics.com/blog/tags/anomaly-detection/)