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Diffstat (limited to 'collectors/python.d.plugin/anomalies/README.md')
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diff --git a/collectors/python.d.plugin/anomalies/README.md b/collectors/python.d.plugin/anomalies/README.md index c58c858bf..3552053ee 100644 --- a/collectors/python.d.plugin/anomalies/README.md +++ b/collectors/python.d.plugin/anomalies/README.md @@ -229,6 +229,7 @@ If you would like to go deeper on what exactly the anomalies collector is doing - If you activate this collector on a fresh node, it might take a little while to build up enough data to calculate a realistic and useful model. - Some models like `iforest` can be comparatively expensive (on same n1-standard-2 system above ~2s runtime during predict, ~40s training time, ~50% cpu on both train and predict) so if you would like to use it you might be advised to set a relatively high `update_every` maybe 10, 15 or 30 in `anomalies.conf`. - Setting a higher `train_every_n` and `update_every` is an easy way to devote less resources on the node to anomaly detection. Specifying less charts and a lower `train_n_secs` will also help reduce resources at the expense of covering less charts and maybe a more noisy model if you set `train_n_secs` to be too small for how your node tends to behave. +- If you would like to enable this on a Rasberry Pi, then check out [this guide](https://learn.netdata.cloud/guides/monitor/raspberry-pi-anomaly-detection) which will guide you through first installing LLVM. ## Useful links and further reading @@ -240,4 +241,4 @@ If you would like to go deeper on what exactly the anomalies collector is doing - Good [blog post](https://www.anodot.com/blog/what-is-anomaly-detection/) from Anodot on time series anomaly detection. Anodot also have some great whitepapers in this space too that some may find useful. - Novelty and outlier detection in the [scikit-learn documentation](https://scikit-learn.org/stable/modules/outlier_detection.html). -[]()
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