Model verification and testing

Model Verification and Testing
For faster and safer iteration of the model catalog -> deployment cycle, Verta offers functionality to provide more responsive, targeted feedback if the model doesn't behave as expected. Additionally, tools are provided for validation of the dependencies included in the model's environment.
VertaModelBase.model_test()
The VertaModelBase interface supports a model_test() method for model verification, which can be implemented with any desired calls and checks.
This method is automatically called
when a deployed Verta endpoint is initializing; any raised exceptions will cause the endpoint update to fail, returning the error and preventing any predictions from being made.
Verification during endpoint initialization requires the 2023_03 release of the Verta platform.
Here is an example model that will fail its model test. It calls predict() and checks the expected values of its output and data logs:
from verta.registry import VertaModelBase, verify_io
from verta import runtime
​
class LoudEcho(VertaModelBase):
    """ Takes a string and makes it LOUD!!! """
    def __init__(self, artifacts=None):
        pass
            
    @verify_io
    def predict(self, input: str) -> str:
        runtime.log('model_input', input)
        echo: str = input + '!!!'
        runtime.log('model_output', echo)
        return echo
        
    def model_test(self):
        # call predict(), capturing model data logs
        input = 'roar'
        with runtime.context() as ctx:
            output = self.predict(input)
        logs = ctx.logs()
​
        # check predict() output
        expected_output = 'ROAR!!!'
        if output != expected_output:
            raise ValueError(f"expected output {expected_output}, got {output}")
        
        # check model data logs
        expected_logs = {'model_input': 'roar', 'model_output': 'ROAR!!!'}
        if logs != expected_logs:
            raise ValueError(f"expected logs {expected_logs}, got {logs}")
When this model is cataloged and deployed, it will encounter an exception—propagating the full error message from model_test():
endpoint.update(model_ver, wait=True)
raises
RuntimeError: endpoint update failed;
Error in model container: Using predict as predictor method
Traceback (most recent call last):
  File "/root/.pyenv/versions/3.10.9/bin/pythonmodelservice", line 33, in <module>
    sys.exit(load_entry_point('pythonmodelservice==0.1.0', 'console_scripts', 'pythonmodelservice')())
  File "/root/.pyenv/versions/3.10.9/lib/python3.10/site-packages/app/__main__.py", line 21, in main
    runtime = _init_runtime()
  File "/root/.pyenv/versions/3.10.9/lib/python3.10/site-packages/app/__main__.py", line 13, in _init_runtime
    runtime = new_runtime()
  File "/root/.pyenv/versions/3.10.9/lib/python3.10/site-packages/app/runtime/runtime.py", line 22, in new_runtime
    return Runtime()
  File "/root/.pyenv/versions/3.10.9/lib/python3.10/site-packages/app/runtime/cherrypy/runtime.py", line 27, in __init__
    self.model_wrapper.model_test()  # test on init
  File "/root/.pyenv/versions/3.10.9/lib/python3.10/site-packages/app/wrappers/model/abc_model_wrapper.py", line 109, in model_test
    self.model.model_test()
  File "/Users/verta/Documents/model_test.py", line 29, in model_test
ValueError: expected output ROAR!!!, got roar!!!
Critically, predict() is missing a call to input.upper():
    @verify_io
    def predict(self, input: str) -> str:
        runtime.log('model_input', input)
        echo: str = input.upper() + '!!!'
        runtime.log('model_output', echo)
        return echo
Making this change will allow the endpoint update to pass its model test and become available for predictions.
Validate a Model's Dependencies Locally
Verta provides convenience functions for scanning a provided model class for dependencies and identifying any missing packages in the provided environment. This can help catch problems early in the development process, and avoid time wasted debugging deployment issues.
Requires python client version: verta>=0.22.2
Option 1.
Simply set the check_model_dependencies argument to True when creating a model version with registered_model.create_standard_model()
model_version = registered_model.create_standard_model(
    model_cls=MyModel,
    environment=Python([]),  # missing model dependency `tensorflow` here
    check_model_dependencies=True
)
If the tensorflow package is used within the model class, the above code will raise an exception, including a list of missing packages:
RuntimeError: the following packages are required by the model but missing from the environment:
tensorflow (installed via ['tensorflow'])
Option 2.
Validate the model's dependencies directly by passing the model class and the environment to verta.registry.check_model_dependencies()
from verta.registry import check_model_dependencies
from verta.environment import Python
​
check_model_dependencies(
    model_cls=MyModel,
    environment=Python([]),  # missing model dependency `tensorflow` here
    raise_for_missing=False  # defaults to False; if True, raises RuntimeError documented above
)
If the tensorflow package is used within the model class, the above code will raise a warning, including a list of missing packages:
RuntimeWarning: the following packages are required by the model but missing from the environment:
tensorflow (installed via ['tensorflow'])
Model version stages
Deployment
Last modified 2d ago