--- sort: 3 title: Scheduler weight: 3 menu: docs: parent: "vmanomaly-components" weight: 3 aliases: - /anomaly-detection/components/scheduler.html --- # Scheduler Scheduler defines how often to run and make inferences, as well as what timerange to use to train the model. Is specified in `scheduler` section of a config for VictoriaMetrics Anomaly Detection. ## Parameters `class`: str, default=`"scheduler.periodic.PeriodicScheduler"`, options={`"scheduler.periodic.PeriodicScheduler"`, `"scheduler.oneoff.OneoffScheduler"`, `"scheduler.backtesting.BacktestingScheduler"`} - `"scheduler.periodic.PeriodicScheduler"`: periodically runs the models on new data. Useful for consecutive re-trainings to counter [data drift](https://www.datacamp.com/tutorial/understanding-data-drift-model-drift) and model degradation over time. - `"scheduler.oneoff.OneoffScheduler"`: runs the process once and exits. Useful for testing. - `"scheduler.backtesting.BacktestingScheduler"`: imitates consecutive backtesting runs of OneoffScheduler. Runs the process once and exits. Use to get more granular control over testing on historical data. **Depending on selected class, different parameters should be used** ## Periodic scheduler ### Parameters For periodic scheduler parameters are defined as differences in times, expressed in difference units, e.g. days, hours, minutes, seconds. Examples: `"50s"`, `"4m"`, `"3h"`, `"2d"`, `"1w"`. <table> <thead> <tr> <th></th> <th>Time granularity</th> </tr> </thead> <tbody> <tr> <td>s</td> <td>seconds</td> </tr> <tr> <td>m</td> <td>minutes</td> </tr> <tr> <td>h</td> <td>hours</td> </tr> <tr> <td>d</td> <td>days</td> </tr> <tr> <td>w</td> <td>weeks</td> </tr> </tbody> </table> <table> <thead> <tr> <th>Parameter</th> <th>Type</th> <th>Example</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>fit_window</code></td> <td>str</td> <td><code>"14d"</code></td> <td>What time range to use for training the models. Must be at least 1 second.</td> </tr> <tr> <td><code>infer_every</code></td> <td>str</td> <td><code>"1m"</code></td> <td>How often a model will write its conclusions on newly added data. Must be at least 1 second.</td> </tr> <tr> <td><code>fit_every</code></td> <td>str, Optional</td> <td><code>"1h"</code></td> <td>How often to completely retrain the models. If missing value of <code>infer_every</code> is used and retrain on every inference run.</td> </tr> </tbody> </table> ### Periodic scheduler config example ```yaml scheduler: class: "scheduler.periodic.PeriodicScheduler" fit_window: "14d" infer_every: "1m" fit_every: "1h" ``` This part of the config means that `vmanomaly` will calculate the time window of the previous 14 days and use it to train a model. Every hour model will be retrained again on 14 days’ data, which will include + 1 hour of new data. The time window is strictly the same 14 days and doesn't extend for the next retrains. Every minute `vmanomaly` will produce model inferences for newly added data points by using the model that is kept in memory at that time. ## Oneoff scheduler ### Parameters For Oneoff scheduler timeframes can be defined in Unix time in seconds or ISO 8601 string format. ISO format supported time zone offset formats are: * Z (UTC) * ±HH:MM * ±HHMM * ±HH If a time zone is omitted, a timezone-naive datetime is used. ### Defining fitting timeframe <table> <thead> <tr> <th>Format</th> <th>Parameter</th> <th>Type</th> <th>Example</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>ISO 8601</td> <td><code>fit_start_iso</code></td> <td>str</td> <td><code>"2022-04-01T00:00:00Z", "2022-04-01T00:00:00+01:00", "2022-04-01T00:00:00+0100", "2022-04-01T00:00:00+01"</code></td> <td rowspan=2>Start datetime to use for training a model. ISO string or UNIX time in seconds.</td> </tr> <tr> <td>UNIX time</td> <td><code>fit_start_s</code></td> <td>float</td> <td>1648771200</td> </tr> <tr> <td>ISO 8601</td> <td><code>fit_end_iso</code></td> <td>str</td> <td><code>"2022-04-10T00:00:00Z", "2022-04-10T00:00:00+01:00", "2022-04-10T00:00:00+0100", "2022-04-10T00:00:00+01"</code></td> <td rowspan=2>End datetime to use for training a model. Must be greater than <code>fit_start_*</code>. ISO string or UNIX time in seconds.</td> </tr> <tr> <td>UNIX time</td> <td><code>fit_end_s</code></td> <td>float</td> <td>1649548800</td> </tr> </tbody> </table> ### Defining inference timeframe <table> <thead> <tr> <th>Format</th> <th>Parameter</th> <th>Type</th> <th>Example</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>ISO 8601</td> <td><code>infer_start_iso</code></td> <td>str</td> <td><code>"2022-04-11T00:00:00Z", "2022-04-11T00:00:00+01:00", "2022-04-11T00:00:00+0100", "2022-04-11T00:00:00+01"</code></td> <td rowspan=2>Start datetime to use for a model inference. ISO string or UNIX time in seconds.</td> </tr> <tr> <td>UNIX time</td> <td><code>infer_start_s</code></td> <td>float</td> <td>1649635200</td> </tr> <tr> <td>ISO 8601</td> <td><code>infer_end_iso</code></td> <td>str</td> <td><code>"2022-04-14T00:00:00Z", "2022-04-14T00:00:00+01:00", "2022-04-14T00:00:00+0100", "2022-04-14T00:00:00+01"</code></td> <td rowspan=2>End datetime to use for a model inference. Must be greater than <code>infer_start_*</code>. ISO string or UNIX time in seconds.</td> </tr> <tr> <td>UNIX time</td> <td><code>infer_end_s</code></td> <td>float</td> <td>1649894400</td> </tr> </tbody> </table> ### ISO format scheduler config example ```yaml scheduler: class: "scheduler.oneoff.OneoffScheduler" fit_start_iso: "2022-04-01T00:00:00Z" fit_end_iso: "2022-04-10T00:00:00Z" infer_start_iso: "2022-04-11T00:00:00Z" infer_end_iso: "2022-04-14T00:00:00Z" ``` ### UNIX time format scheduler config example ```yaml scheduler: class: "scheduler.oneoff.OneoffScheduler" fit_start_iso: 1648771200 fit_end_iso: 1649548800 infer_start_iso: 1649635200 infer_end_iso: 1649894400 ``` ## Backtesting scheduler ### Parameters As for [Oneoff scheduler](#oneoff-scheduler), timeframes can be defined in Unix time in seconds or ISO 8601 string format. ISO format supported time zone offset formats are: * Z (UTC) * ±HH:MM * ±HHMM * ±HH If a time zone is omitted, a timezone-naive datetime is used. ### Defining overall timeframe This timeframe will be used for slicing on intervals `(fit_window, infer_window == fit_every)`, starting from the *latest available* time point, which is `to_*` and going back, until no full `fit_window + infer_window` interval exists within the provided timeframe. <table> <thead> <tr> <th>Format</th> <th>Parameter</th> <th>Type</th> <th>Example</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>ISO 8601</td> <td><code>from_iso</code></td> <td>str</td> <td><code>"2022-04-01T00:00:00Z", "2022-04-01T00:00:00+01:00", "2022-04-01T00:00:00+0100", "2022-04-01T00:00:00+01"</code></td> <td rowspan=2>Start datetime to use for backtesting.</td> </tr> <tr> <td>UNIX time</td> <td><code>from_s</code></td> <td>float</td> <td>1648771200</td> </tr> <tr> <td>ISO 8601</td> <td><code>to_iso</code></td> <td>str</td> <td><code>"2022-04-10T00:00:00Z", "2022-04-10T00:00:00+01:00", "2022-04-10T00:00:00+0100", "2022-04-10T00:00:00+01"</code></td> <td rowspan=2>End datetime to use for backtesting. Must be greater than <code>from_start_*</code>.</td> </tr> <tr> <td>UNIX time</td> <td><code>to_s</code></td> <td>float</td> <td>1649548800</td> </tr> </tbody> </table> ### Defining training timeframe The same *explicit* logic as in [Periodic scheduler](#periodic-scheduler) <table> <thead> <tr> <th>Format</th> <th>Parameter</th> <th>Type</th> <th>Example</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>ISO 8601</td> <td rowspan=2><code>fit_window</code></td> <td rowspan=2>str</td> <td><code>"PT1M", "P1H"</code></td> <td rowspan=2>What time range to use for training the models. Must be at least 1 second.</td> </tr> <tr> <td>Prometheus-compatible</td> <td><code>"1m", "1h"</code></td> </tr> </tbody> </table> ### Defining inference timeframe In `BacktestingScheduler`, the inference window is *implicitly* defined as a period between 2 consecutive model `fit_every` runs. The *latest* inference window starts from `to_s` - `fit_every` and ends on the *latest available* time point, which is `to_s`. The previous periods for fit/infer are defined the same way, by shifting `fit_every` seconds backwards until we get the last full fit period of `fit_window` size, which start is >= `from_s`. <table> <thead> <tr> <th>Format</th> <th>Parameter</th> <th>Type</th> <th>Example</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>ISO 8601</td> <td rowspan=2><code>fit_every</code></td> <td rowspan=2>str</td> <td><code>"PT1M", "P1H"</code></td> <td rowspan=2>What time range to use previously trained model to infer on new data until next retrain happens.</td> </tr> <tr> <td>Prometheus-compatible</td> <td><code>"1m", "1h"</code></td> </tr> </tbody> </table> ### ISO format scheduler config example ```yaml scheduler: class: "scheduler.backtesting.BacktestingScheduler" from_start_iso: '2021-01-01T00:00:00Z' to_end_iso: '2021-01-14T00:00:00Z' fit_window: 'P14D' fit_every: 'PT1H' ``` ### UNIX time format scheduler config example ```yaml scheduler: class: "scheduler.backtesting.BacktestingScheduler" from_start_s: 167253120 to_end_s: 167443200 fit_window: '14d' fit_every: '1h' ```