You can find the latest docs (there aren't enough!) at ReadTheDocs.

NYMMS is a monitoring framework that takes inspiration from a lot of different places.

It's goals are:

• Independently scalable components
• Fault tolerant
• Easily useable in a cloud environment
• Easy to add new monitors

There are many other goals, but that's a good start.

Here's a somewhat hard to understand diagram (at least without some explanation):

Currently the main requirements are:

• Python (2.7 - may work on older versions, haven't tested)
• boto
• PyYAML (used in a few backends, will eventually not be a requirement unless you need to use those backends)
• Jinja2 (needed for templating)
• Validictory (0.9.1 https://pypi.python.org/pypi/validictory/0.9.1)

Optionally:

• pagerduty (0.2.1 https://pypi.python.org/pypi/pagerduty/0.2.1) if you use the pagerduty reactor handler

A docker image is provided that can be used to run any of the daemons used in NYMMS. It can be pulled from phobologic/nymms. To run the daemons, you can launch them with the following command:

docker run -e "AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>" -e "AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>" --rm -it phobologic/nymms:latest /[schedulerreactor] <OPTIONAL_ARGS>

For example, to run the scheduler (with verbose logging, the -v) you can run:

docker run --rm -it phobologic/nymms:latest /scheduler -v

You can also set the AWS_ACCESS_KEY_ID & AWS_SECRET_ACCESS_KEY in a file, and then use --env-file rather than specifying the variables on the command line. Optionally, if you are running on a host in EC2 that has an IAM profile with all the necessary permissions, you do not need to specify the keys at all.