def case_custom_model(custom_model: Model) -> ModelSettings:
save(custom_model, save_env=False)
model_uri = custom_model.details.local_folder
return ModelSettings(
name="custom-model",
parameters=ModelParameters(uri=model_uri),
)
def case_wrapped_class_instance(inference_pipeline_class) -> ModelSettings:
save(inference_pipeline_class, save_env=False)
model_uri = inference_pipeline_class.pipeline.details.local_folder
return ModelSettings(
name="wrapped-class-instance",
parameters=ModelParameters(uri=model_uri),
)
def case_wrapped_class(inference_pipeline_class) -> ModelSettings:
MyClass = inference_pipeline_class.__class__
save(MyClass, save_env=False)
model_uri = MyClass.pipeline.details.local_folder
return ModelSettings(
name="wrapped-class",
parameters=ModelParameters(uri=model_uri),
)
async def test_save(inference_pipeline):
save(inference_pipeline, save_env=False)
loaded_pipeline = load(inference_pipeline.details.local_folder)
# Ensure models are exported as async
assert len(inference_pipeline.models.__dict__) == len(
loaded_pipeline.models.__dict__)
for model in loaded_pipeline.models.values():
assert isinstance(model, Model)
def inference_pipeline_deployed(
inference_pipeline: Pipeline,
runtime: SeldonDockerRuntime,
) -> Generator[RemoteModel, None, None]:
# NOTE: Need to re-save the pipeline so that it knows about the runtime
save(inference_pipeline, save_env=True)
rm = deploy(inference_pipeline)
# TODO: Fix wait_ready for pipelines
time.sleep(8)
yield rm
try:
rm.undeploy()
except docker.errors.NotFound:
# TODO: Should undeploy be idempotent as well?
# Ignore if the model has already been undeployed
pass