def test_incomplete_verta(self, model):
msg_match = (
r"^model must finish implementing the following methods of VertaModelBase: "
)
msg_match += re.escape(str(list(sorted(model.__abstractmethods__))))
with pytest.raises(TypeError, match=msg_match):
model_validator.must_verta(model)
def create_standard_model(
self,
model_cls,
environment,
code_dependencies=None,
model_api=None,
artifacts=None,
name=None,
desc=None,
labels=None,
attrs=None,
lock_level=None,
):
"""Create a Standard Verta Model version from a Verta Model Specification.
.. versionadded:: 0.18.2
.. note::
The following artifact keys are reserved for internal use within the
Verta system:
- ``"custom_modules"``
- ``"model"``
- ``"model.pkl"``
- ``"model_api.json"``
- ``"requirements.txt"``
- ``"train_data"``
- ``"tf_saved_model"``
- ``"setup_script"``
.. note::
If using an XGBoost model from their scikit-learn API,
``"scikit-learn"`` must also be specified in `environment`
(in addition to ``"xgboost"``).
Parameters
----------
model_cls : subclass of :class:`~verta.registry.VertaModelBase`
Model class that implements ``VertaModelBase``.
environment : :class:`~verta.environment.Python`
pip and apt dependencies.
code_dependencies : list of str, optional
Paths to local Python code files that `model_cls` depends on. This
parameter has the same behavior as ``custom_modules`` in
:meth:`RegisteredModelVersion.log_model`.
model_api : :class:`~verta.utils.ModelAPI`, optional
Model API specifying the model's expected input and output
artifacts : dict of str to obj
A mapping from artifact keys to artifacts. These will be logged
and uploaded, then provided to the model when deployed.
name : str, optional
Name of the model version. If no name is provided, one will be
generated.
desc : str, optional
Description of the model version.
labels : list of str, optional
Labels of the model version.
attrs : dict of str to {None, bool, float, int, str}, optional
Attributes of the model version.
lock_level : :mod:`~verta.registry.lock`, default :class:`~verta.registry.lock.Open`
Lock level to set when creating this model version.
Returns
-------
:class:`~verta.registry.entities.RegisteredModelVersion`
Examples
--------
.. code-block:: python
from verta.environment import Python
from verta.registry import VertaModelBase
class VertaModel(VertaModelBase):
def __init__(self, artifacts):
import pickle
with open(artifacts["weights"], "rb") as f:
self.weights = pickle.load(f)
def predict(self, input):
import numpy as np
return np.matmul(self.weights, input)
model_ver = reg_model.create_standard_model(
VertaModel,
Python(["numpy"]),
artifacts={"weights": np.array(weights)},
)
endpoint.update(model_ver, wait=True)
endpoint.get_deployed_model().predict(input)
"""
model_validator.must_verta(model_cls)
return self._create_standard_model_from_spec(
model=model_cls,
environment=environment,
code_dependencies=code_dependencies,
model_api=model_api,
artifacts=artifacts,
name=name,
desc=desc,
labels=labels,
attrs=attrs,
lock_level=lock_level,
)
def test_decorated_verta(self, model):
with pytest.warns(None) as record:
model_validator.must_verta(model)
assert not record # no warning of missing decorator on predict()
def test_verta(self, model):
msg_match = "^" + re.escape(
"model predict() is not decorated with verta.registry.verify_io;")
with pytest.warns(UserWarning, match=msg_match):
assert model_validator.must_verta(model)
def test_not_verta(self, model):
msg_match = r"^model must be a subclass of verta.registry.VertaModelBase.*"
with pytest.raises(TypeError, match=msg_match):
model_validator.must_verta(model)
def test_bad_init_verta(self, model):
msg_match = "^" + re.escape(
"model __init__() parameters must be named ('self', 'artifacts'), not "
)
with pytest.raises(TypeError, match=msg_match):
model_validator.must_verta(model)
