Local setup using Vagrant¶

OmegaML comes with a Vagrant resourcefile that can be used to spin off a local machine that has spark, omegaml and hadoop all configured, integrated and ready to use.

Pre-requisites¶

Before you spin up the Vagrant machine, please make sure that you have a working rabbitmq and mongodb installations and map the relative omegaml variables to the same, which can be easily done using.

$ export OMEGA_MONGO_URL=mongodb://<host ip>:27017/omega
$ export OMEGA_BROKER=amqp://guest@<host ip>:5672//

Start up vagrant machine¶

To start the vagrant machine, go to the installation directory of omegaml, which would generally be <libdir>/omegaml/omegaml/resources/spark_vagrant where libdir would be the location where you installed omegaml.

# cd <libdir>/omegaml/omegaml/resources/spark_vagrant
# vagrant up

This will take a while and spin off a virtualbox with spark & omegaml on it.

Using the vagrant machine¶

Open a web browser and browse http://localhost:18888 . This is the ipython notebook server.
Start a python notebook.
Run your code snippets.
To have omegaml run periodic jobs, create a notebook prefixing job_ to its name and the first cell with job parameters. Which should look something like below
```
# omegaml.script:
#   run-at: "*/1 * * * *"
#   results-store: gridfs
#   author: exsys@nixify.com
#   name: Gaurav Ghimire
```
In the above configuration, below points are to be noted :
- all job configurations should be valid yaml files.
- all lines must start with # .
- the first line should always be omegaml.script.
- run-at: “*/1 * * * *” defines the time interval in ‘cron’ like schedule, this is scheduled to run every minute
- results-store: gridfs , results-store can either be s3 or gridfs
  - s3 would store the results in pre-configured S3 bucket/path.
  - gridfs would store the results as result_.ipynb on same collection.
  - author and name are author identifiers, not used for now, but may later be used for say email results.

Debugging¶

To see omegaml and ipython logs

@host$ vagrant ssh
@vm$ tail -f /tmp/celeryd.log
@vm$ tail -f /tmp/ipython.log