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### Describe Your Changes - change links from relative to absolute under Anomaly Detection section ### Checklist The following checks are **mandatory**: - [x] My change adheres [VictoriaMetrics contributing guidelines](https://docs.victoriametrics.com/contributing/).
534 lines
14 KiB
Markdown
534 lines
14 KiB
Markdown
---
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title: Scheduler
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weight: 3
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menu:
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docs:
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parent: "vmanomaly-components"
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weight: 3
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aliases:
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- /anomaly-detection/components/scheduler.html
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---
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Scheduler defines how often to run and make inferences, as well as what timerange to use to train the model.
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Is specified in `scheduler` section of a config for VictoriaMetrics Anomaly Detection.
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> **Note: Starting from [v1.11.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1110) scheduler section in config supports multiple schedulers via aliasing. <br>Also, `vmanomaly` expects scheduler section to be named `schedulers`. Using old (flat) format with `scheduler` key is deprecated and will be removed in future versions.**
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```yaml
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schedulers:
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scheduler_periodic_1m:
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# class: "periodic" # or class: "scheduler.periodic.PeriodicScheduler" until v1.13.0 with class alias support)
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infer_every: "1m"
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fit_every: "2m"
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fit_window: "3h"
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scheduler_periodic_5m:
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# class: "periodic" # or class: "scheduler.periodic.PeriodicScheduler" until v1.13.0 with class alias support)
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infer_every: "5m"
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fit_every: "10m"
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fit_window: "3h"
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...
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```
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Old-style configs (< [1.11.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1110))
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```yaml
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scheduler:
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# class: "periodic" # or class: "scheduler.periodic.PeriodicScheduler" until v1.13.0 with class alias support)
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infer_every: "1m"
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fit_every: "2m"
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fit_window: "3h"
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...
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```
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will be **implicitly** converted to
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```yaml
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schedulers:
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default_scheduler: # default scheduler alias added, for backward compatibility
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class: "scheduler.periodic.PeriodicScheduler"
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infer_every: "1m"
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fit_every: "2m"
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fit_window: "3h"
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...
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```
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## Parameters
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`class`: str, default=`"scheduler.periodic.PeriodicScheduler"`,
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options={`"scheduler.periodic.PeriodicScheduler"`, `"scheduler.oneoff.OneoffScheduler"`, `"scheduler.backtesting.BacktestingScheduler"`}
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- `"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.
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- `"scheduler.oneoff.OneoffScheduler"`: runs the process once and exits. Useful for testing.
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- `"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.
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> **Note**: starting from [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130), class aliases are supported, so `"scheduler.periodic.PeriodicScheduler"` can be substituted to `"periodic"`, `"scheduler.oneoff.OneoffScheduler"` - to `"oneoff"`, `"scheduler.backtesting.BacktestingScheduler"` - to `"backtesting"`
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**Depending on selected class, different parameters should be used**
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## Periodic scheduler
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### Parameters
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For periodic scheduler parameters are defined as differences in times, expressed in difference units, e.g. days, hours, minutes, seconds.
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Examples: `"50s"`, `"4m"`, `"3h"`, `"2d"`, `"1w"`.
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<table class="params">
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<thead>
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<tr>
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<th></th>
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<th>Time granularity</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>s</td>
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<td>seconds</td>
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</tr>
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<tr>
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<td>m</td>
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<td>minutes</td>
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</tr>
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<tr>
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<td>h</td>
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<td>hours</td>
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</tr>
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<tr>
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<td>d</td>
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<td>days</td>
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</tr>
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<tr>
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<td>w</td>
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<td>weeks</td>
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</tr>
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</tbody>
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</table>
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<table class="params">
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<thead>
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<tr>
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<th>Parameter</th>
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<th>Type</th>
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<th>Example</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>
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`fit_window`
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</td>
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<td>str</td>
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<td>
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`"14d"`
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</td>
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<td>What time range to use for training the models. Must be at least 1 second.</td>
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</tr>
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<tr>
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<td>
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`infer_every`
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</td>
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<td>str</td>
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<td>
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`"1m"`
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</td>
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<td>How often a model will write its conclusions on newly added data. Must be at least 1 second.</td>
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</tr>
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<tr>
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<td>
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`fit_every`
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</td>
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<td>str, Optional</td>
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<td>
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`"1h"`
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</td>
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<td>
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How often to completely retrain the models. If missing value of `infer_every` is used and retrain on every inference run.
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</td>
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</tr>
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</tbody>
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</table>
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### Periodic scheduler config example
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```yaml
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schedulers:
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periodic_scheduler_alias:
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class: "periodic"
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# (or class: "scheduler.periodic.PeriodicScheduler" until v1.13.0 with class alias support)
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fit_window: "14d"
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infer_every: "1m"
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fit_every: "1h"
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```
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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.
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## Oneoff scheduler
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### Parameters
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For Oneoff scheduler timeframes can be defined in Unix time in seconds or ISO 8601 string format.
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ISO format supported time zone offset formats are:
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* Z (UTC)
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* ±HH:MM
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* ±HHMM
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* ±HH
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If a time zone is omitted, a timezone-naive datetime is used.
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### Defining fitting timeframe
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<table class="params">
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<thead>
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<tr>
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<th>Format</th>
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<th>Parameter</th>
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<th>Type</th>
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<th>Example</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>ISO 8601</td>
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<td>
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`fit_start_iso`
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</td>
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<td>str</td>
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<td>
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`"2022-04-01T00:00:00Z", "2022-04-01T00:00:00+01:00", "2022-04-01T00:00:00+0100", "2022-04-01T00:00:00+01"`
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</td>
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<td rowspan=2>Start datetime to use for training a model. ISO string or UNIX time in seconds.</td>
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</tr>
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<tr>
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<td>UNIX time</td>
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<td>
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`fit_start_s`
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</td>
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<td>float</td>
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<td>1648771200</td>
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</tr>
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<tr>
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<td>ISO 8601</td>
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<td>
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`fit_end_iso`
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</td>
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<td>str</td>
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<td>
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`"2022-04-10T00:00:00Z", "2022-04-10T00:00:00+01:00", "2022-04-10T00:00:00+0100", "2022-04-10T00:00:00+01"`
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</td>
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<td rowspan=2>End datetime to use for training a model. Must be greater than
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`fit_start_*`
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. ISO string or UNIX time in seconds.</td>
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</tr>
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<tr>
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<td>UNIX time</td>
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<td>
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`fit_end_s`
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</td>
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<td>float</td>
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<td>1649548800</td>
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</tr>
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</tbody>
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</table>
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### Defining inference timeframe
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<table class="params">
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<thead>
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<tr>
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<th>Format</th>
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<th>Parameter</th>
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<th>Type</th>
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<th>Example</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>ISO 8601</td>
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<td>
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`infer_start_iso`
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</td>
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<td>str</td>
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<td>
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`"2022-04-11T00:00:00Z", "2022-04-11T00:00:00+01:00", "2022-04-11T00:00:00+0100", "2022-04-11T00:00:00+01"`
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</td>
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<td rowspan=2>Start datetime to use for a model inference. ISO string or UNIX time in seconds.</td>
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</tr>
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<tr>
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<td>UNIX time</td>
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<td>
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`infer_start_s`
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</td>
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<td>float</td>
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<td>1649635200</td>
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</tr>
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<tr>
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<td>ISO 8601</td>
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<td>
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`infer_end_iso`
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</td>
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<td>str</td>
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<td>
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`"2022-04-14T00:00:00Z", "2022-04-14T00:00:00+01:00", "2022-04-14T00:00:00+0100", "2022-04-14T00:00:00+01"`
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</td>
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<td rowspan=2>End datetime to use for a model inference. Must be greater than
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`infer_start_*`
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. ISO string or UNIX time in seconds.</td>
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</tr>
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<tr>
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<td>UNIX time</td>
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<td>
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`infer_end_s`
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</td>
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<td>float</td>
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<td>1649894400</td>
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</tr>
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</tbody>
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</table>
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### ISO format scheduler config example
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```yaml
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schedulers:
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oneoff_scheduler_alias:
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class: "oneoff"
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# (or class: "scheduler.oneoff.OneoffScheduler" until v1.13.0 with class alias support)
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fit_start_iso: "2022-04-01T00:00:00Z"
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fit_end_iso: "2022-04-10T00:00:00Z"
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infer_start_iso: "2022-04-11T00:00:00Z"
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infer_end_iso: "2022-04-14T00:00:00Z"
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```
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### UNIX time format scheduler config example
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```yaml
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schedulers:
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oneoff_scheduler_alias:
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class: "oneoff"
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# (or class: "scheduler.oneoff.OneoffScheduler" until v1.13.0 with class alias support)
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fit_start_s: 1648771200
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fit_end_s: 1649548800
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infer_start_s: 1649635200
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infer_end_s: 1649894400
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```
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## Backtesting scheduler
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### Parameters
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As for [Oneoff scheduler](#oneoff-scheduler), timeframes can be defined in Unix time in seconds or ISO 8601 string format.
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ISO format supported time zone offset formats are:
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* Z (UTC)
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* ±HH:MM
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* ±HHMM
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* ±HH
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If a time zone is omitted, a timezone-naive datetime is used.
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### Parallelization
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<table class="params">
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<thead>
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<tr>
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<th>Parameter</th>
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<th>Type</th>
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<th>Example</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>
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`n_jobs`
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</td>
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<td>int</td>
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<td>
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`1`
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</td>
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<td>
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Allows *proportionally faster (yet more resource-intensive)* evaluations of a config on historical data. Default value is 1, that implies *sequential* execution. Introduced in [v1.13.0](https://docs.victoriametrics.com/anomaly-detection/changelog/#v1130)
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</td>
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</tr>
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</tbody>
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</table>
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### Defining overall timeframe
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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.
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<table class="params">
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<thead>
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<tr>
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<th>Format</th>
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<th>Parameter</th>
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<th>Type</th>
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<th>Example</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>ISO 8601</td>
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<td>
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`from_iso`
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</td>
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<td>str</td>
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<td>
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`"2022-04-01T00:00:00Z", "2022-04-01T00:00:00+01:00", "2022-04-01T00:00:00+0100", "2022-04-01T00:00:00+01"`
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</td>
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<td rowspan=2>Start datetime to use for backtesting.</td>
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</tr>
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<tr>
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<td>UNIX time</td>
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<td>
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`from_s`
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</td>
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<td>float</td>
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<td>1648771200</td>
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</tr>
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<tr>
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<td>ISO 8601</td>
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<td>
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`to_iso`
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</td>
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<td>str</td>
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<td>
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`"2022-04-10T00:00:00Z", "2022-04-10T00:00:00+01:00", "2022-04-10T00:00:00+0100", "2022-04-10T00:00:00+01"`
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</td>
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<td rowspan=2>End datetime to use for backtesting. Must be greater than
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`from_start_*`
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</td>
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</tr>
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<tr>
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<td>UNIX time</td>
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<td>
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`to_s`
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</td>
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<td>float</td>
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<td>1649548800</td>
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</tr>
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</tbody>
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</table>
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### Defining training timeframe
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The same *explicit* logic as in [Periodic scheduler](#periodic-scheduler)
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<table class="params">
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<thead>
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<tr>
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<th>Format</th>
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<th>Parameter</th>
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<th>Type</th>
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<th>Example</th>
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<th>Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>ISO 8601</td>
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<td rowspan=2>
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`fit_window`
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</td>
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<td rowspan=2>str</td>
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<td>
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`"PT1M", "P1H"`
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</td>
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<td rowspan=2>What time range to use for training the models. Must be at least 1 second.</td>
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</tr>
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<tr>
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<td>Prometheus-compatible</td>
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<td>
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`"1m", "1h"`
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</td>
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</tr>
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</tbody>
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</table>
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|
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### Defining inference timeframe
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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`.
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<table class="params">
|
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<thead>
|
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<tr>
|
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<th>Format</th>
|
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<th>Parameter</th>
|
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<th>Type</th>
|
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<th>Example</th>
|
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<th>Description</th>
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</tr>
|
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</thead>
|
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<tbody>
|
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<tr>
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<td>ISO 8601</td>
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<td rowspan=2>
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`fit_every`
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</td>
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<td rowspan=2>str</td>
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<td>
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`"PT1M", "P1H"`
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</td>
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<td rowspan=2>What time range to use previously trained model to infer on new data until next retrain happens.</td>
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</tr>
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<tr>
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<td>Prometheus-compatible</td>
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<td>
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`"1m", "1h"`
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</td>
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</tr>
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</tbody>
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</table>
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|
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### ISO format scheduler config example
|
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```yaml
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schedulers:
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backtesting_scheduler_alias:
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class: "backtesting"
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# (or class: "scheduler.backtesting.BacktestingScheduler" until v1.13.0 with class alias support)
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from_iso: '2021-01-01T00:00:00Z'
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to_iso: '2021-01-14T00:00:00Z'
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fit_window: 'P14D'
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fit_every: 'PT1H'
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n_jobs: 1 # default = 1 (sequential), set it up to # of CPUs for parallel execution
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```
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|
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### UNIX time format scheduler config example
|
||
```yaml
|
||
schedulers:
|
||
backtesting_scheduler_alias:
|
||
class: "backtesting"
|
||
# (or class: "scheduler.backtesting.BacktestingScheduler" until v1.13.0 with class alias support)
|
||
from_s: 167253120
|
||
to_s: 167443200
|
||
fit_window: '14d'
|
||
fit_every: '1h'
|
||
n_jobs: 1 # default = 1 (sequential), set it up to # of CPUs for parallel execution
|
||
```
|