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diff --git a/collectors/python.d.plugin/anomalies/README.md b/collectors/python.d.plugin/anomalies/README.md index aaf39ab9..7c59275f 100644 --- a/collectors/python.d.plugin/anomalies/README.md +++ b/collectors/python.d.plugin/anomalies/README.md @@ -1,13 +1,17 @@ <!-- title: "Anomaly detection with Netdata" description: "Use ML-driven anomaly detection to narrow your focus to only affected metrics and services/processes on your node to shorten root cause analysis." -custom_edit_url: https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/anomalies/README.md -sidebar_url: Anomalies +custom_edit_url: "https://github.com/netdata/netdata/edit/master/collectors/python.d.plugin/anomalies/README.md" +sidebar_url: "Anomalies" +sidebar_label: "anomalies" +learn_status: "Unpublished" +learn_topic_type: "References" +learn_rel_path: "References/Collectors references/Misc" --> # Anomaly detection with Netdata -**Note**: Check out the [Netdata Anomaly Advisor](https://learn.netdata.cloud/docs/cloud/insights/anomaly-advisor) for a more native anomaly detection experience within Netdata. +**Note**: Check out the [Netdata Anomaly Advisor](https://github.com/netdata/netdata/blob/master/docs/cloud/insights/anomaly-advisor.mdx) for a more native anomaly detection experience within Netdata. This collector uses the Python [PyOD](https://pyod.readthedocs.io/en/latest/index.html) library to perform unsupervised [anomaly detection](https://en.wikipedia.org/wiki/Anomaly_detection) on your Netdata charts and/or dimensions. @@ -70,7 +74,7 @@ The configuration for the anomalies collector defines how it will behave on your _**Note**: If you are unsure about any of the below configuration options then it's best to just ignore all this and leave the `anomalies.conf` file alone to begin with. Then you can return to it later if you would like to tune things a bit more once the collector is running for a while and you have a feeling for its performance on your node._ Edit the `python.d/anomalies.conf` configuration file using `edit-config` from the your agent's [config -directory](/docs/configure/nodes.md), which is usually at `/etc/netdata`. +directory](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md), which is usually at `/etc/netdata`. ```bash cd /etc/netdata # Replace this path with your Netdata config directory, if different @@ -216,7 +220,7 @@ If you would like to go deeper on what exactly the anomalies collector is doing ## Notes -- Python 3 is required as the [`netdata-pandas`](https://github.com/netdata/netdata-pandas) package uses Python async libraries ([asks](https://pypi.org/project/asks/) and [trio](https://pypi.org/project/trio/)) to make asynchronous calls to the [Netdata REST API](https://learn.netdata.cloud/docs/agent/web/api) to get the required data for each chart. +- Python 3 is required as the [`netdata-pandas`](https://github.com/netdata/netdata-pandas) package uses Python async libraries ([asks](https://pypi.org/project/asks/) and [trio](https://pypi.org/project/trio/)) to make asynchronous calls to the [Netdata REST API](https://github.com/netdata/netdata/blob/master/web/api/README.md) to get the required data for each chart. - Python 3 is also required for the underlying ML libraries of [numba](https://pypi.org/project/numba/), [scikit-learn](https://pypi.org/project/scikit-learn/), and [PyOD](https://pypi.org/project/pyod/). - It may take a few hours or so (depending on your choice of `train_secs_n`) for the collector to 'settle' into it's typical behaviour in terms of the trained models and probabilities you will see in the normal running of your node. - As this collector does most of the work in Python itself, with [PyOD](https://pyod.readthedocs.io/en/latest/) leveraging [numba](https://numba.pydata.org/) under the hood, you may want to try it out first on a test or development system to get a sense of its performance characteristics on a node similar to where you would like to use it. @@ -231,7 +235,7 @@ 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. +- If you would like to enable this on a Raspberry Pi, then check out [this guide](https://github.com/netdata/netdata/blob/master/docs/guides/monitor/raspberry-pi-anomaly-detection.md) which will guide you through first installing LLVM. ## Useful links and further reading |