From 112b5b91647c3dea45cc1c9bc364df526c8012f1 Mon Sep 17 00:00:00 2001 From: Daniel Baumann Date: Wed, 26 Jan 2022 19:05:15 +0100 Subject: Merging upstream version 1.33.0. Signed-off-by: Daniel Baumann --- collectors/python.d.plugin/anomalies/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) (limited to 'collectors/python.d.plugin/anomalies') 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). -[![analytics](https://www.google-analytics.com/collect?v=1&aip=1&t=pageview&_s=1&ds=github&dr=https%3A%2F%2Fgithub.com%2Fnetdata%2Fnetdata&dl=https%3A%2F%2Fmy-netdata.io%2Fgithub%2Fcollectors%2Fpython.d.plugin%2Fanomalies%2FREADME&_u=MAC~&cid=5792dfd7-8dc4-476b-af31-da2fdb9f93d2&tid=UA-64295674-3)]() \ No newline at end of file +[![analytics](https://www.google-analytics.com/collect?v=1&aip=1&t=pageview&_s=1&ds=github&dr=https%3A%2F%2Fgithub.com%2Fnetdata%2Fnetdata&dl=https%3A%2F%2Fmy-netdata.io%2Fgithub%2Fcollectors%2Fpython.d.plugin%2Fanomalies%2FREADME&_u=MAC~&cid=5792dfd7-8dc4-476b-af31-da2fdb9f93d2&tid=UA-64295674-3)]() -- cgit v1.2.3