The omega|ml runtime

om.runtime provides access to cloud resources:

Training a model

Training a model using a cloud cluster is straight forward:

# store some data and an unfitted model
pd = pd.DataFrame({'x': range(100))
reg = LinearRegression()
om.models.put(reg, 'mymodel')

# train the model using the cloud
om.runtime.model('mymodel').fit('sample[x]', 'sample[y]')

The same also works from the command line:

$ om datasets put sample.csv sample
$ om models put mymodel.create_model mymodel
$ om runtime model mymodel fit sample[x] sample[y]

Running grid search

In a similar way we can run a gridsearch:

$ om runtime model mymodel gridsearch