AWS SageMaker Pipeline Issue - Pipeline variables do not support __str__ operation - amazon-sagemaker

Question

I'm trying to build SageMaker Pipeline based on Tensorflow framework. I have only Training, Evaluating steps, and Register model. On the evaluation step I declared MetricsSource for ModelMetrics and received an error.
Code is below:
pipeline_model = PipelineModel(
models=[tf_model],
role=role,
sagemaker_session=sagemaker_session
)
eval_res = step_evaluate_model.arguments['ProcessingOutputConfig']['Outputs'][0]['S3Output']['S3Uri']
evaluation_s3_uri = f'{eval_res}/evaluation.json'
model_statistics=MetricsSource(
s3_uri=evaluation_s3_uri,
content_type='application/json')
model_metrics = ModelMetrics(model_statistics=model_statistics)
step_register_pipeline_model = pipeline_model.register(
content_types=['application/json'],
response_types=['application/json'],
inference_instances=['ml.m4.xlarge','ml.c5.2xlarge'],
transform_instances=['ml.c5.2xlarge'],
model_package_group_name=model_package_group_name,
model_metrics=model_metrics,
approval_status=model_approval_status.default_value,
)
Error:
TypeError Traceback (most recent call last)
Input In [17], in <cell line: 17>()
14 model_metrics = ModelMetrics(model_statistics=model_statistics)
15 # print('\n',pipeline_model)
---> 17 step_register_pipeline_model = pipeline_model.register(
18 content_types=['application/json'],
19 response_types=['application/json'],
20 inference_instances=['ml.m4.xlarge','ml.c5.2xlarge'],
21 transform_instances=['ml.c5.2xlarge'],
22 model_package_group_name=model_package_group_name,
23 model_metrics=model_metrics,
24 approval_status=model_approval_status.default_value,
25 )
TypeError: Pipeline variables do not support __str__ operation. Please use `.to_string()` to convert it to string type in execution timeor use `.expr` to translate it to Json for display purpose in Python SDK.
Could you please help me to solve it? I'd appreciate for any idea. Thanks

Answer 1

They way to create model and register model on Pipelines has changed slightly with the introduction of ModelStep, also the instantiation of session_pipeline is needed. Similarly, ModelStep will be used for registering the model .
Reference: https://github.com/aws/sagemaker-python-sdk/pull/3076
Examples : https://sagemaker.readthedocs.io/en/stable/workflows/pipelines/sagemaker.workflow.pipelines.html?highlight=ModelStep#sagemaker.workflow.model_step.ModelStep