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# below are some examples of using the `anomaly-bit` option to define alerts based on anomaly
# rates as opposed to raw metric values. You can read more about the anomaly-bit and Netdata's
# native anomaly detection here:
# https://learn.netdata.cloud/docs/configure/machine-learning#anomaly-bit---100--anomalous-0--normal
# examples below are commented, you would need to uncomment and adjust as desired to enable them.
# alert per dimension example
# if anomaly rate is between 5-20% then warning (pick your own threshold that works best via tial and error).
# if anomaly rate is above 20% then critical (pick your own threshold that works best via tial and error).
# template: ml_5min_cpu_dims
# on: system.cpu
# os: linux
# hosts: *
# lookup: average -5m anomaly-bit foreach *
# calc: $this
# units: %
# every: 30s
# warn: $this > (($status >= $WARNING) ? (5) : (20))
# crit: $this > (($status == $CRITICAL) ? (20) : (100))
# info: rolling 5min anomaly rate for each system.cpu dimension
# alert per chart example
# if anomaly rate is between 5-20% then warning (pick your own threshold that works best via tial and error).
# if anomaly rate is above 20% then critical (pick your own threshold that works best via tial and error).
# template: ml_5min_cpu_chart
# on: system.cpu
# os: linux
# hosts: *
# lookup: average -5m anomaly-bit of *
# calc: $this
# units: %
# every: 30s
# warn: $this > (($status >= $WARNING) ? (5) : (20))
# crit: $this > (($status == $CRITICAL) ? (20) : (100))
# info: rolling 5min anomaly rate for system.cpu chart
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