Working MLFlow models ¶

omega-ml provides a single, unified API and leverages the mlflow model format to form a cloud-agnostic runtime platform for both model building and serving.

Using mlflow saved models ¶

In mlflow, models are saved using a model-specific API:

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model_path = './models'
mlflow.sklearn.save_model(model, model_path)

Given such a mlflow saved model, we can store the model in omega-ml and get it back as follows:

In [1]: # use the same model_path as in mlflow's save_model
        om.models.put(model_path, 'mymodel')
        # get back the model
        mymodel = om.models.get('mymodel')
        type(mymodel)
Out[2]:
        mlflow.pyfunc.PyFuncModel

Using a MLModel file ¶

When saving a model, mlflow creates a file called MLModel. omega-ml can also store this file directly:

In [1]: # use the same model_path as in mlflow's save_model
        om.models.put('mlflow:///path/to/MLModel', 'mymodel')
        # get back the model
        mymodel = om.models.get('mymodel')
        type(mymodel)
Out[2]:
        mlflow.pyfunc.PyFuncModel

Using a mlflow Model or PythonModel ¶

omega-ml can also be used inside mlflow projects to store a model directly, without saving the model to a file first. This is useful if you like to use a mlflow model flavor that omega-ml does not support natively.

# inside a mlflow project script
model = LinearRegression()
meta = om.models.put(model, 'mymodel', kind='mlflow.model')

Serving mlflow model runs ¶

If you use the tracking features of mlflow, omegaml can directly serve models from mlflow. This is useful if you use mlflow locally or in development, and use omega-ml for easy model deployment to production, or if you need to use a model flavor that omega-ml does not support natively.

mlflow.set_tracking_uri('sqlite:///mlflow.sqlite')
with mlflow.start_run() as run:
    model = LinearRegression()
    X = pd.Series(range(0, 10))
    Y = pd.Series(X) * 2 + 3
    model.fit(reshaped(X), reshaped(Y))
    mlflow.sklearn.log_model(sk_model=model,
                             artifact_path='sklearn-model',
                             registered_model_name='sklearn-model')

om = self.om
# store with just the model path, specify the kind because paths can be other files too
meta = om.models.put('mlflow+models://sklearn-model/1', 'sklearn-model')

Model frameworks supported via mlflow ¶

The following mlflow model flavors can be stored in omega-ml. A full list of model flavors supported by mlflow can be found at mlflow_flavors

Python
Keras
MLeap
PyTorch
Scikit-learn
Spark MLLib
Tensorflow
XGBoost
LightGBM
FastAI
SpaCy
Statsmodels

Disclaimer and License ¶

mlflow is not part of, distributed by or along of omega-the. The above describes API-binding interfaces to mlflow, but does not itself constitute a derivative work of mlflow as per the mlflow_license.

Working MLFlow models¶

Using mlflow saved models¶

Using a MLModel file¶

Using a mlflow Model or PythonModel¶

Serving mlflow model runs¶

Model frameworks supported via mlflow¶

Disclaimer and License¶