--- weight: 1 title: Quick Start menu: docs: parent: "anomaly-detection" identifier: "vmanomaly-quick-start" weight: 1 title: Quick Start tags: - metrics - enterprise - guide aliases: - /anomaly-detection/QuickStart.html --- For a broader overview please visit the [navigation page](https://docs.victoriametrics.com/anomaly-detection/). ## 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/). The following options are available: - [To run Docker image](#docker) - [To run in Kubernetes with Helm charts](#kubernetes-with-helm-charts) > Anomaly detection models can be kept {{% available_from "v1.13.0" anomaly %}} **on host filesystem after `fit` stage** (instead of default in-memory option); This will drastically reduce RAM for larger configurations. Similar optimization {{% available_from "v1.16.0" anomaly %}} can be applied to data read from VictoriaMetrics TSDB. See instructions of how to enable it [here](https://docs.victoriametrics.com/anomaly-detection/faq/#on-disk-mode). ### Command-line arguments The `vmanomaly` service supports a set of command-line arguments to configure its behavior, including options for licensing, logging levels, and more. > `vmanomaly` supports {{% available_from "v1.18.5" anomaly %}} running on config **directories**, see the `config` positional arg description in help message below. > Single-dashed command-line argument {{% available_from "v1.23.3" anomaly %}} format can be used, e.g. `-license.forceOffline` in addition to `--license.forceOffline`. This aligns better with other VictoriaMetrics ecosystem components. Mixing the two styles is also supported, e.g. `-license.forceOffline --loggerLevel INFO`. ```shellhelp usage: vmanomaly.py [-h] [--license STRING | --licenseFile PATH] [--license.forceOffline] [--loggerLevel {DEBUG,WARNING,FATAL,ERROR,INFO}] [--watch] [--dryRun] [--outputSpec PATH] config [config ...] VictoriaMetrics Anomaly Detection Service positional arguments: config YAML config file(s) or directories containing YAML files. Multiple files will recursively merge each other values so multiple configs can be combined. If a directory is provided, all `.yaml` files inside will be merged, without recursion. Default: vmanomaly.yaml is expected in the current directory. options: -h Show this help message and exit --license STRING License key for VictoriaMetrics Enterprise. See https://victoriametrics.com/products/enterprise/trial/ to obtain a trial license. --licenseFile PATH Path to file with license key for VictoriaMetrics Enterprise. See https://victoriametrics.com/products/enterprise/trial/ to obtain a trial license. --license.forceOffline Whether to force offline verification for VictoriaMetrics Enterprise license key, which has been passed either via -license or via -licenseFile command-line flag. The issued license key must support offline verification feature. Contact info@victoriametrics.com if you need offline license verification. --loggerLevel {DEBUG,WARNING,FATAL,ERROR,INFO} Minimum level to log. Possible values: DEBUG, INFO, WARNING, ERROR, FATAL. --watch Watch config files for changes and trigger hot reloads. Watches the specified config file or directory for modifications, deletions, or additions. Upon detecting changes, triggers config reload. If new config validation fails, continues with previous valid config and state. --dryRun Validate only: parse + merge all YAML(s) and run schema checks, then exit. Does not require a license to run. Does not expose metrics, or launch vmanomaly service(s). --outputSpec PATH Target location of .yaml output spec. ``` You can specify these options when running `vmanomaly` to fine-tune logging levels or handle licensing configurations, as per your requirements. ### Licensing The license key can be specified with the help of the following [command-line](#command-line-arguments) arguments: `--license`, `--licenseFile`, `--license.forceOffline` In order to make it easier to monitor the license expiration date, the following metrics are exposed (see [Monitoring](https://docs.victoriametrics.com/anomaly-detection/components/monitoring/) section for details on how to scrape them): ```promtextmetric # HELP vm_license_expires_at When the license expires as a Unix timestamp in seconds # TYPE vm_license_expires_at gauge vm_license_expires_at 1.6963776e+09 # HELP vm_license_expires_in_seconds Amount of seconds until the license expires # TYPE vm_license_expires_in_seconds gauge vm_license_expires_in_seconds 4.886608e+06 ``` Example alerts for [vmalert](https://docs.victoriametrics.com/victoriametrics/vmalert/): ```yaml groups: - name: vm-license # note the `job` label and update accordingly to your setup rules: - alert: LicenseExpiresInLessThan30Days expr: vm_license_expires_in_seconds < 30 * 24 * 3600 labels: severity: warning annotations: summary: "{{ $labels.job }} instance {{ $labels.instance }} license expires in less than 30 days" description: "{{ $labels.instance }} of job {{ $labels.job }} license expires in {{ $value | humanizeDuration }}. Please make sure to update the license before it expires." - alert: LicenseExpiresInLessThan7Days expr: vm_license_expires_in_seconds < 7 * 24 * 3600 labels: severity: critical annotations: summary: "{{ $labels.job }} instance {{ $labels.instance }} license expires in less than 7 days" description: "{{ $labels.instance }} of job {{ $labels.job }} license expires in {{ $value | humanizeDuration }}. Please make sure to update the license before it expires." ``` ### Docker > To run `vmanomaly`, you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/).

> Due to the upcoming [DockerHub pull limits](https://docs.docker.com/docker-hub/usage/pulls), an additional image registry, **Quay.io**, has been introduced for VictoriaMetrics images, including [`vmanomaly`](https://quay.io/repository/victoriametrics/vmanomaly). If you encounter pull rate limits, switch from: > ``` > docker pull victoriametrics/vmanomaly:vX.Y.Z > ``` > to: > ``` > docker pull quay.io/victoriametrics/vmanomaly:vX.Y.Z > ``` Below are the steps to get `vmanomaly` up and running inside a Docker container: 1. Pull Docker image: ```sh docker pull victoriametrics/vmanomaly:v1.25.0 ``` 2. (Optional step) tag the `vmanomaly` Docker image: ```sh docker image tag victoriametrics/vmanomaly:v1.25.0 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 \ --licenseFile=/license \ --loggerLevel=INFO \ --watch ``` In case you found `PermissionError: [Errno 13] Permission denied:` in `vmanomaly` logs, set user/user group to 1000 in the run command above / in a docker-compose file: ```sh export YOUR_LICENSE_FILE_PATH=path/to/license/file export YOUR_CONFIG_FILE_PATH=path/to/config/file docker run -it --user 1000:1000 \ -v $YOUR_LICENSE_FILE_PATH:/license \ -v $YOUR_CONFIG_FILE_PATH:/config.yml \ vmanomaly /config.yml \ --licenseFile=/license \ --loggerLevel=INFO \ --watch ``` ```yaml # docker-compose file services: # ... vmanomaly: image: victoriametrics/vmanomaly:v1.25.0 volumes: $YOUR_LICENSE_FILE_PATH:/license $YOUR_CONFIG_FILE_PATH:/config.yml command: - "/config.yml" - "--licenseFile=/license" - "--loggerLevel=INFO" - "--watch" # ... ``` For a complete docker-compose example please refer to [our alerting guide](https://docs.victoriametrics.com/anomaly-detection/guides/guide-vmanomaly-vmalert/), chapter [docker-compose](https://docs.victoriametrics.com/anomaly-detection/guides/guide-vmanomaly-vmalert/#docker-compose) See also: - Verify the license online OR offline. See the details [here](https://docs.victoriametrics.com/anomaly-detection/quickstart/#licensing). - [How to configure `vmanomaly`](#how-to-configure-vmanomaly) ### Kubernetes with Helm charts > To run `vmanomaly`, you need to have VictoriaMetrics Enterprise license. You can get a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/). > With the forthcoming [DockerHub pull limits](https://docs.docker.com/docker-hub/usage/pulls) additional image registry was introduced (quay.io) for VictoriaMetric images, [vmanomaly images in particular](https://quay.io/repository/victoriametrics/vmanomaly). If hitting pull limits, try switching your `docker pull quay.io/victoriametrics/vmanomaly:vX.Y.Z` to `docker pull quay.io/victoriametrics/vmanomaly:vX.Y.Z` 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). ### VM Operator If you are using [VM Operator](https://docs.victoriametrics.com/operator/) to manage your Kubernetes cluster, `vmanomaly` can be deployed using the following [custom resource guide](https://docs.victoriametrics.com/operator/resources/vmanomaly/). ## How to configure vmanomaly To run `vmanomaly`, use YAML files or directories containing YAML files. The configuration files support shallow merge, allowing splitting the configuration into multiple files for better organization. > If you are using directories, all `.yaml` files inside will be shallow merged, without deeper recursion. If you want to merge multiple YAML files, you can specify them as separate arguments, e.g. > ```shellhelp > vmanomaly config1.yaml config2.yaml ./config_dir/ > ``` Before deploying, check the correctness of your configuration validate config file(s) with `--dryRun` [command-line](#command-line-arguments) flag for chosen deployment method (Docker, Kubernetes, etc.). This will parse and merge all YAML files, run schema checks, log errors and warnings (if found) and then exit without starting the service and requiring a license. ### Example Here is an example of config file that will run [Prophet](https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet) model on `vm_cache_entries` metric, with periodic scheduler that runs inference every minute and fits the model every day. The model will be trained on the last 2 weeks of data each time it is (re)fitted. The model will produce `anomaly_score`, `yhat`, `yhat_lower`, and `yhat_upper` [series](https://docs.victoriametrics.com/anomaly-detection/components/models/#vmanomaly-output) for debugging purposes. The model will be timezone-aware and will use cyclical encoding for the hour of the day and day of the week seasonality. ```yaml settings: # https://docs.victoriametrics.com/anomaly-detection/components/settings/ n_workers: 4 # number of workers to run workload in parallel, set to 0 or negative number to use all available CPU cores anomaly_score_outside_data_range: 5.0 # default anomaly score for anomalies outside expected data range restore_state: True # restore state from previous run, available since v1.24.0 schedulers: 1d_1m: # https://docs.victoriametrics.com/anomaly-detection/components/scheduler/#periodic-scheduler class: 'periodic' infer_every: '1m' fit_every: '1d' fit_window: '2w' models: # https://docs.victoriametrics.com/anomaly-detection/components/models/#prophet prophet_model: class: 'prophet' provide_series: ['anomaly_score', 'yhat', 'yhat_lower', 'yhat_upper'] # for debugging tz_aware: True # set to True if your data is timezone-aware, to deal with DST changes correctly tz_use_cyclical_encoding: True tz_seasonalities: # intra-day + intra-week seasonality - name: 'hod' # intra-day seasonality, hour of the day fourier_order: 4 # keep it 3-8 based on intraday pattern complexity prior_scale: 10 - name: 'dow' # intra-week seasonality, time of the week fourier_order: 2 # keep it 2-4, as dependencies are learned separately for each weekday # inner model args (key-value pairs) accepted by # https://facebook.github.io/prophet/docs/quick_start#python-api args: interval_width: 0.98 # see https://facebook.github.io/prophet/docs/uncertainty_intervals reader: class: 'vm' # use VictoriaMetrics as a data source # https://docs.victoriametrics.com/anomaly-detection/components/reader/#vm-reader datasource_url: "http://victoriametrics:8428/" # [YOUR_DATASOURCE_URL] sampling_period: "1m" queries: # define your queries with MetricsQL - https://docs.victoriametrics.com/victoriametrics/metricsql/ cache: "sum(rate(vm_cache_entries))" writer: class: 'vm' # use VictoriaMetrics as a data destination # https://docs.victoriametrics.com/anomaly-detection/components/writer/#vm-writer datasource_url: "http://victoriametrics:8428/" # [YOUR_DATASOURCE_URL] ``` ### Recommended steps For optimal service behavior, consider the following tweaks when configuring `vmanomaly`: - Set `settings.n_workers` {{% available_from "v1.23.0" anomaly %}} [arg](https://docs.victoriametrics.com/anomaly-detection/components/settings/#parallelization) > 1 to utilize more of available CPU cores for parallel workload processing. This can significantly improve performance, especially on larger datasets with a lot of `reader.queries` and longer `scheduler.fit_window` intervals. Setting it to zero or negative number will enable using all available CPU cores. - Set up [on-disk mode](https://docs.victoriametrics.com/anomaly-detection/faq/#on-disk-mode) {{% available_from "v1.13.0" anomaly %}} to reduce RAM usage, especially for larger datasets. This mode allows `vmanomaly` to keep models and the data on the host filesystem after the `fit` stage, rather than in memory. - Set up **state restoration** {{% available_from "v1.24.0" anomaly %}} to resume from the last known state for long-term stability. This is controlled by the `settings.restore_state` boolean [arg](https://docs.victoriametrics.com/anomaly-detection/components/settings/#state-restoration). - Set up **config hot-reloading** {{% available_from "v1.25.0" anomaly %}} to automatically reload configurations on config files changes. This can be enabled via the `--watch` [CLI argument](https://docs.victoriametrics.com/anomaly-detection/quickstart/#command-line-arguments) and allows for configuration updates without explicit service restarts. **Schedulers**: - Configure the **inference frequency** in the [scheduler](https://docs.victoriametrics.com/anomaly-detection/components/scheduler/) section of the configuration file. - Ensure that `infer_every` aligns with your **minimum required alerting frequency**. - For example, if receiving **alerts every 15 minutes** is sufficient (when `anomaly_score > 1`), set `infer_every` to match `reader.sampling_period` or override it per query via `reader.queries.query_xxx.step` for an optimal setup. **Reader**: - Setup the datasource to read data from in the [reader](https://docs.victoriametrics.com/anomaly-detection/components/reader/) section. Include tenant ID if using a [cluster version of VictoriaMetrics](https://docs.victoriametrics.com/victoriametrics/cluster-victoriametrics/) (`multitenant` value {{% available_from "v1.16.2" anomaly %}} can be also used here). - Define queries for input data using [MetricsQL](https://docs.victoriametrics.com/victoriametrics/metricsql/) under `reader.queries` section. Note, it's possible to override reader-level arguments at query level for increased flexibility, e.g. specifying per-query [timezone](https://docs.victoriametrics.com/anomaly-detection/faq#handling-timezones) or [sampling period](https://docs.victoriametrics.com/anomaly-detection/components/reader/#sampling-period). - For longer `fit_window` intervals in scheduler, consider splitting queries into smaller time ranges to avoid excessive memory usage, timeouts and hitting server-side constraints, so they can be queried separately and reconstructed on `vmanomaly` side. Please refer to this [example](https://docs.victoriametrics.com/anomaly-detection/faq/#handling-large-queries-in-vmanomaly) for more details. **Writer**: - Specify where and how to store anomaly detection metrics in the [writer](https://docs.victoriametrics.com/anomaly-detection/components/writer/) section. - Include tenant ID if using a [cluster version of VictoriaMetrics](https://docs.victoriametrics.com/victoriametrics/cluster-victoriametrics/) for writing the results. - Adding `for` label to `metric_format` argument is recommended for smoother visual experience in the [anomaly score dashboard](https://docs.victoriametrics.com/anomaly-detection/presets/#default). Please refer to `metric_format` argument description [here](https://docs.victoriametrics.com/anomaly-detection/components/writer/?highlight=metric_format#config-parameters). **Models**: - Configure built-in models hyperparameters according to your needs in the [models](https://docs.victoriametrics.com/anomaly-detection/components/models/) section. Where possible, incorporate [domain knowledge](https://docs.victoriametrics.com/anomaly-detection/faq/#incorporating-domain-knowledge) for optimal results. - (Optional) Develop or integrate your [custom models](https://docs.victoriametrics.com/anomaly-detection/components/models/#custom-model-guide) with `vmanomaly`. - Adding `y` to `provide_series` [argument](https://docs.victoriametrics.com/anomaly-detection/components/models/#provide-series) values is recommended for smoother visual experience in the [anomaly score dashboard](https://docs.victoriametrics.com/anomaly-detection/presets/#default). Also, other `vmanomaly` [output](https://docs.victoriametrics.com/anomaly-detection/components/models#vmanomaly-output) series can be specified in `provide_series`, such as `yhat`, `yhat_lower`, `yhat_upper`, etc. This will allow you to visualize the expected values and their confidence intervals in the dashboard. > Only [univariate models](https://docs.victoriametrics.com/anomaly-detection/components/models/#univariate-models) support the generation of such output. Other models, such as [multivariate](https://docs.victoriametrics.com/anomaly-detection/components/models/#multivariate-models) or [custom](https://docs.victoriametrics.com/anomaly-detection/components/models/#custom-model-guide), may not support this feature. **Visualization**: - Set up [anomaly score dashboard](https://docs.victoriametrics.com/anomaly-detection/presets/#grafana-dashboard) to visualize the results of anomaly detection. - Set up [self-monitoring dashboard](https://docs.victoriametrics.com/anomaly-detection/self-monitoring/) to monitor the health of `vmanomaly` service and its components. ## Check also Please refer to the following links for a deeper understanding of Anomaly Detection and `vmanomaly`: - [High Availability](https://docs.victoriametrics.com/anomaly-detection/scaling-vmanomaly/#high-availability) and [Horizontal Scalability](https://docs.victoriametrics.com/anomaly-detection/scaling-vmanomaly/#horizontal-scalability) - [State Restoration](https://docs.victoriametrics.com/anomaly-detection/components/settings/#state-restoration) - [Guide: Anomaly Detection and Alerting Setup](https://docs.victoriametrics.com/anomaly-detection/guides/guide-vmanomaly-vmalert/) - [FAQ](https://docs.victoriametrics.com/anomaly-detection/faq/) - [CHANGELOG](https://docs.victoriametrics.com/anomaly-detection/changelog/) - [Anomaly Detection Blog](https://victoriametrics.com/tags/anomaly-detection/)