Using the runtime for MLOps

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]

Using a model for prediction

X = pd.Series(...)
result = om.runtime.model('mymodel').predict(X)
yhat = result.get()

Scoring a model

X = pd.DataFrame(...)
Y = pd.Series(...)
result = om.runtime.model('mymodel').score(X, Y)
score = result.get()

Running gridsearch

gridsearch is supported by some ML frameworks only (e.g. scikit-learn)

X = pd.DataFrame(...)
Y = pd.Series(...)
result = om.runtime.model('mymodel').gridsearch(X, Y)
score = result.get()

Tracking experiments

Since experiments are a feature of the runtime, we can store a model and link it to an experiment. In this case the runtime will create an experiment context prior to performing the requested model action.

lr = LogisticRegression()
om.models.put(lr, 'mymodel', attributes={
    'tracking': {
        'default': 'myexp',
om.runtime.model('mymodel').score(X, Y)

Thus the runtime worker will run the following code equivalent. This is true for all calls of the runtime (programmatic, cli or REST API).

# run time worker, in response to om.runtime.score('mymodel', X, Y)
def omega_score(X, Y):
    model = om.models.get('mymodel')
    meta = om.models.metadata('mymodel')
    exp_name = meta.attributes['tracking']['default']
    with om.runtime.experiment(exp_name) as exp:
        exp.log_event('task_call', 'mymodel')
        result = model.score(X, Y)
        exp.log_metric('score', result)
        exp.log_artifcat(meta, 'related')
        exp.log_event('task_success', 'mymodel')