Add guide: Unsupervised anomaly detection for Raspberry Pi monitoring (#10713)

* Init guide

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docs/guides/monitor/raspberry-pi-anomaly-detection.md

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+<!--
+title: "Unsupervised anomaly detection for Raspberry Pi monitoring"
+description: "Use a low-overhead machine learning algorithm and an open-source monitoring tool to detect anomalous metrics on a Raspberry Pi."
+image: /img/seo/guides/monitor/raspberry-pi-anomaly-detection.png
+author: "Andy Maguire"
+author_title: "Senior Machine Learning Engineer"
+author_img: "/img/authors/andy-maguire.jpg"
+custom_edit_url: https://github.com/netdata/netdata/edit/master/docs/guides/monitor/raspberry-pi-anomaly-detection.md
+-->
+
+# Unsupervised anomaly detection for Raspberry Pi monitoring
+
+We love IoT and edge at Netdata, we also love machine learning. Even better if we can combine the two to ease the pain
+of monitoring increasingly complex systems.
+
+We recently explored what might be involved in enabling our Python-based [anomalies
+collector](/collectors/python.d.plugin/anomalies/README.md) on a Raspberry Pi. To our delight, it's actually quite
+straightforward! 
+
+Read on to learn all the steps and enable unsupervised anomaly detection on your on Raspberry Pi(s).
+
+> Spoiler: It's just a couple of extra commands that will make you feel like a pro.
+
+## What you need to get started
+
+- A Raspberry Pi running Raspbian, which we'll call a _node_.
+- The [open-source Netdata Agent](https://github.com/netdata/netdata). If you don't have it installed on your node yet,
+  [get it now](/docs/get/README.md).
+
+## Install dependencies
+
+First make sure Netdata is using Python 3 when it runs Python-based data collectors. 
+
+Next, open `netdata.conf` using [`edit-config`](/docs/configure/nodes.md#use-edit-config-to-edit-configuration-files)
+from within the [Netdata config directory](/docs/configure/nodes.md#the-netdata-config-directory). Scroll down to the
+`[plugin:python.d]` section to pass in the `-ppython3` command option. 
+
+```conf
+[plugin:python.d]
+    # update every = 1
+    command options = -ppython3
+```
+
+Next, install some of the underlying libraries used by the Python packages the collector depends upon.
+
+```bash
+sudo apt install llvm-9 libatlas3-base libgfortran5 libatlas-base-dev
+```
+
+Now you're ready to install the Python packages used by the collector itself. First, become the `netdata` user.
+
+```bash
+sudo su -s /bin/bash netdata
+```
+
+Then pass in the location to find `llvm` as an environment variable for `pip3`.
+
+```bash
+LLVM_CONFIG=llvm-config-9 pip3 install --user llvmlite numpy==1.20.1 netdata-pandas==0.0.32 numba==0.50.1 scikit-learn==0.23.2 pyod==0.8.3
+```
+
+## Enable the anomalies collector
+
+Now you're ready to enable the collector and [restart Netdata](/docs/configure/start-stop-restart.md).
+
+```bash
+sudo ./edit-config python.d.conf
+# set `anomalies: no` to `anomalies: yes`
+
+# restart netdata
+sudo systemctl restart netdata
+```
+
+And that should be it! Wait a minute or two, refresh your Netdata dashboard, you should see the default anomalies
+charts under the **Anomalies** section in the dashboard's menu.
+
+![Anomaly detection on the Raspberry
+Pi](https://user-images.githubusercontent.com/1153921/110149717-9d749c00-7d9b-11eb-853c-e041a36f0a41.png)
+
+## Overhead on system
+
+Of course one of the most important considerations when trying to do anomaly detection at the edge (as opposed to in a
+centralized cloud somewhere) is the resource utilization impact of running a monitoring tool.
+
+With the default configuration, the anomalies collector uses about 6.5% of CPU at each run. During the retraining step,
+CPU utilization jumps to between 20-30% for a few seconds, but you can [configure
+retraining](/collectors/python.d.plugin/anomalies/README.md#configuration) to happen less often if you wish.
+
+![CPU utilization of anomaly detection on the Raspberry
+Pi](https://user-images.githubusercontent.com/1153921/110149718-9d749c00-7d9b-11eb-9af8-46e2032cd1d0.png)
+
+In terms of the runtime of the collector, it was averaging around 250ms during each prediction step, jumping to about
+8-10 seconds during a retraining step. This jump equates only to a small gap in the anomaly charts for a few seconds.
+
+![Execution time of anomaly detection on the Raspberry
+Pi](https://user-images.githubusercontent.com/1153921/110149715-9cdc0580-7d9b-11eb-826d-faf6f620621a.png)
+
+The last consideration then is the amount of RAM the collector needs to store both the models and some of the data
+during training. By default, the anomalies collector, along with all other running Python-based collectors, uses about
+100MB of system memory.
+
+![RAM utilization of anomaly detection on the Raspberry
+Pi](https://user-images.githubusercontent.com/1153921/110149720-9e0d3280-7d9b-11eb-883d-b1d4d9b9b5e1.png)
+
+## What's next?
+
+So, all in all, with a small little bit of extra set up and a small overhead on the Pi itself, the anomalies collector
+looks like a potentially useful addition to enable unsupervised anomaly detection on your Pi.
+
+See our two-part guide series for a more complete picture of configuring the anomalies collector, plus some best
+practices on using the charts it automatically generates:
+
+- [_Detect anomalies in systems and applications_](/docs/guides/monitor/anomaly-detection.md)
+- [_Monitor and visualize anomalies with Netdata_](/docs/guides/monitor/visualize-monitor-anomalies.md)
+
+If you're using your Raspberry Pi for other purposes, like blocking ads/trackers with Pi-hole, check out our companions
+Pi guide: [_Monitor Pi-hole (and a Raspberry Pi) with Netdata_](/docs/guides/monitor/pi-hole-raspberry-pi.md).
+
+Once you've had a chance to give unsupervised anomaly detection a go, share your use cases and let us know of any
+feedback on our [community forum](https://community.netdata.cloud/t/anomalies-collector-feedback-megathread/767).  
+
+### Related reference documentation
+
+- [Netdata Agent · Get Netdata](/docs/get/README.md)
+- [Netdata Agent · Anomalies collector](/collectors/python.d.plugin/anomalies/README.md)
+
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