diff options
Diffstat (limited to 'docs/guides/monitor/raspberry-pi-anomaly-detection.md')
| -rw-r--r-- | docs/guides/monitor/raspberry-pi-anomaly-detection.md | 96 |
1 files changed, 0 insertions, 96 deletions
diff --git a/docs/guides/monitor/raspberry-pi-anomaly-detection.md b/docs/guides/monitor/raspberry-pi-anomaly-detection.md deleted file mode 100644 index 935d0f6cf..000000000 --- a/docs/guides/monitor/raspberry-pi-anomaly-detection.md +++ /dev/null @@ -1,96 +0,0 @@ -# Anomaly detection for RPi monitoring - -Learn how to use a low-overhead machine learning algorithm alongside Netdata to detect anomalous metrics on a Raspberry Pi. - -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](https://github.com/netdata/netdata/blob/master/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](https://github.com/netdata/netdata) monitoring agent. If you don't have it installed on your - node yet, [get started now](https://github.com/netdata/netdata/blob/master/packaging/installer/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`](https://github.com/netdata/netdata/blob/master/docs/configure/nodes.md#use-edit-config-to-edit-configuration-files) -from within the [Netdata config directory](https://github.com/netdata/netdata/blob/master/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==0.0.38 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](https://github.com/netdata/netdata/blob/master/docs/configure/start-stop-restart.md). - -```bash -sudo ./edit-config python.d.conf - -# 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. - - - -## 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](https://github.com/netdata/netdata/blob/master/collectors/python.d.plugin/anomalies/README.md#configuration) to happen less often if you wish. - - - -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. - - - -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. - - - - |