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Diffstat (limited to 'collectors/python.d.plugin/anomalies/README.md')
-rw-r--r-- | collectors/python.d.plugin/anomalies/README.md | 21 |
1 files changed, 16 insertions, 5 deletions
diff --git a/collectors/python.d.plugin/anomalies/README.md b/collectors/python.d.plugin/anomalies/README.md index bcbfdbcd7..9d24e8685 100644 --- a/collectors/python.d.plugin/anomalies/README.md +++ b/collectors/python.d.plugin/anomalies/README.md @@ -35,18 +35,26 @@ Then, as the issue passes, the anomaly probabilities should settle back down int ## Requirements - This collector will only work with Python 3 and requires the packages below be installed. +- Typically you will not need to do this, but, if needed, to ensure Python 3 is used you can add the below line to the `[plugin:python.d]` section of `netdata.conf` + +```conf +[plugin:python.d] + # update every = 1 + command options = -ppython3 +``` + +Install the required python libraries. ```bash # become netdata user sudo su -s /bin/bash netdata # install required packages for the netdata user -pip3 install --user netdata-pandas==0.0.32 numba==0.50.1 scikit-learn==0.23.2 pyod==0.8.3 +pip3 install --user netdata-pandas==0.0.38 numba==0.50.1 scikit-learn==0.23.2 pyod==0.8.3 ``` ## Configuration -Install the Python requirements above, enable the collector and [restart -Netdata](/docs/configure/start-stop-restart.md). +Install the Python requirements above, enable the collector and restart Netdata. ```bash cd /etc/netdata/ @@ -69,7 +77,7 @@ sudo ./edit-config python.d/anomalies.conf The default configuration should look something like this. Here you can see each parameter (with sane defaults) and some information about each one and what it does. -```yaml +```conf # ---------------------------------------------------------------------- # JOBS (data collection sources) @@ -87,6 +95,9 @@ local: # Use http or https to pull data protocol: 'http' + # SSL verify parameter for requests.get() calls + tls_verify: true + # What charts to pull data for - A regex like 'system\..*|' or 'system\..*|apps.cpu|apps.mem' etc. charts_regex: 'system\..*' @@ -229,4 +240,4 @@ 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)]() +[![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)]()
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