Working with Python

omega-ml is easiest to use from Python. In any Python program, notebook or shell:

import omegaml as om

# try a few things
om.datasets.list()
om.models.list()

# store
df = pd.DataFrame( ... )
om.datasets.put(df, 'mydf')

# retrieve
om.datasets.get('mydf')

Using omega-ml in python

Starting up omega-ml is straight forward. In any Python program or interactive shell just import the omegaml module as follows:

import omegaml as om

The om module is readily configured to work with your local omega-ml server, or with the cloud instance configured using the om cloud login command.

Once loaded om provides several storage areas that are immediately usable:

  • om.datasets - storage area for Python and Pandas objects

  • om.models - storage area for models

  • om.scripts - storage area for custom modules (a.k.a. lambda modules)

  • om.jobs- storage area for jobs (ipython notebooks)

In addition, your cluster or cloud resources are available as

  • om.runtime - the omega-ml remote execution environment

Getting help

To get help on stored objects use the per-object .help() method. This will show the doc string of the plugin for that object. To get help on functions, use Python’s built-in help().

For example:

# per object help
om.datasets.help('mydataset')
om.models.help('mymodel')

help(om.datasets)
help(om.datasets.put)
help(om.dataests.getl('mydata'))
help(om.runtime)

Running a worker

$ om runtime celery worker