Configuring models

As models become more complicated they are attached to different assets or other models. We will need to instanciate them through the ModelLibrary object which will take care of all this for us.

To do so, we have to configure our model: give it a name, and possibly assets, dependencies, adding test cases, etc.

Models are made available to clients using modelkit by specifying them using the CONFIGURATIONS class attribute:

class SimpleModel(Model):
    CONFIGURATIONS = {
        "simple": {}
    }
    def _predict(self, item):
        return "something"

Right now, we have only given it a name "simple" which makes the model available to other models via the ModelLibrary.

The rest of the configuration is empty but we will add to it at the next section.

Assuming that SimpleModel is defined in my_module.my_models, it is now accessible via:

from modelkit.core import ModelLibrary
import my_module.my_models

p = ModelLibrary(models=my_module.my_models)
m = p.get("simple")

See Organization for more information on how to organize your models.

Model settings

The simplest configuration options are model_settings:

from modelkit.core.model import Model

class SimpleModel(Model):
    CONFIGURATIONS = {
        "simple": {"model_settings": {"value" : "something"}},
        "simple2": {"model_settings": {"value" : "something2"}}
    }
    def _predict(self, item):
        return self.model_settings["value"]

Now, there are two versions of the model available, simple and simple2:

from modelkit.core import ModelLibrary

p = ModelLibrary(models=SimpleModel)
m = p.get("simple")
print(m({}))
m2 = p.get("simple2")
print(m2({}))

It will print "something" and "something2".

Model attributes

In general, Model have several attributes set by the ModelLibrary when they are loaded:

• asset_path the path to the asset set in the Model's configuration
• configuration_key the key of the model's configuration
• model_dependencies a dictionary of Model dependencies
• model_settings the model_settings as passed at initialization
• service_settings the settings of the ModelLibrary that created the model
• batch_size the batch size for the model: if _predict_batch is implemented it will default to getting batches of this size. It defaults to None, which means "no batching"

Asset class

It is sometimes useful for a given asset in memory to serve many different Model objects. It is possibly by using the model_dependencies to point to a parent Model that is the only one to load the asset via _load.

In this case, we may not want the parent asset-bearing Model object to implement predict at all.

This is what an modelkit.core.model.Asset is.

Note

In fact, it is defined the other way around: Models are Assets with a predict function, and thus Model inherits from Asset.

Note

There are two ways to use a data asset in a Model: either load it directly via its configuration and the _load, or package it in an Asset and use the deserialized object via model dependencies.