def Do(self, spec: SageMakerDeploySpec):
# Manually invoke AWS client configuration so we can use it before
# starting the reconciliation loop
self._configure_aws_clients(spec.inputs)
name_suffix = SageMakerComponent._generate_unique_timestamped_id()
self._endpoint_name = (spec.inputs.endpoint_name
if spec.inputs.endpoint_name else
f"Endpoint{name_suffix}")
self._should_update_existing = (spec.inputs.update_endpoint
and self._endpoint_name_exists(
spec.inputs.endpoint_name))
# Fetch existing config to delete after creation
if self._should_update_existing:
self._existing_endpoint_config_name = self._get_endpoint_config(
spec.inputs.endpoint_name)
self._endpoint_config_name = (
spec.inputs.endpoint_config_name
# Only use the predefined name if we are not updating, otherwise could conflict
if (spec.inputs.endpoint_config_name
and not self._should_update_existing) else
f"EndpointConfig{name_suffix}")
super().Do(spec.inputs, spec.outputs, spec.output_paths)
def Do(self, spec: SageMakerTrainingSpec):
self._training_job_name = (
spec.inputs.job_name if spec.inputs.job_name else
SageMakerComponent._generate_unique_timestamped_id(
prefix="TrainingJob"))
super().Do(spec.inputs, spec.outputs, spec.output_paths)
def Do(self, spec: SageMakerRLEstimatorSpec):
self._rlestimator_job_name = (
spec.inputs.job_name if spec.inputs.job_name else
SageMakerComponent._generate_unique_timestamped_id(
prefix="RLEstimatorJob"))
super().Do(spec.inputs, spec.outputs, spec.output_paths)