Create a Model

Creating a Model

A Model represents a machine learning model. Each Model has a type. Currently Merlin supports both standard model types (PyTorch, SKLearn, Tensorflow, and XGBoost) and user-defined models (PyFunc model).

Merlin also supports custom models. More info can be found here:

Custom Model

Conceptually, a Model in Merlin is similar to a class in programming languages. To instantiate a Model, you’ll have to create a Model Version.

merlin.set_model(<model_name>, <model_type>) will set the active model to the name given by parameter. If the Model with given name is not found, a new Model will be created.

model_creation.py

import merlin
from merlin.model import ModelType

merlin.set_model("tensorflow-sample", ModelType.TENSORFLOW)

Creating a Model Version

A Model Version represents a snapshot of A particular Model iteration. A Model Version might contain artifacts which are deployable to Merlin. You'll also be able to attach information such as metrics and tags to a given Model Version.

model_version_creation.py

with merlin.new_model_version() as v:
    merlin.log_metric("metric", 0.1)
    merlin.log_param("param", "value")
    merlin.set_tag("tag", "value")

    merlin.log_model(model_dir='tensorflow-sample')