Pipeline AttributeError: 'ParameterString' object has no attribute 'startswith

[urirosenberg]

Describe the bug
Hi, I am using SDK version 2.169.0 on SM studio notebook.
Error
AttributeError: 'ParameterString' object has no attribute 'startswith' when using pipelines ParameterString
training_instance_type = ParameterString(name="TrainingInstanceType", default_value="ml.m5.xlarge")

To reproduce
Run the following notebook: https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/model-monitor-clarify-pipelines/sagemaker-pipeline-model-monitor-clarify-steps.ipynb

Expected behavior
No error

Screenshots or logs

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/sagemaker/utils.py in volume_size_supported(instance_type)
   1405         # local mode does not support volume size
-> 1406         if instance_type.startswith("local"):
   1407             return False

AttributeError: 'ParameterString' object has no attribute 'startswith'

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-57-5181e304d56b> in <module>
----> 1 pipeline.definition()

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in definition(self)
    364     def definition(self) -> str:
    365         """Converts a request structure to string representation for workflow service calls."""
--> 366         request_dict = self.to_request()
    367         self._interpolate_step_collection_name_in_depends_on(request_dict["Steps"])
    368         request_dict["PipelineExperimentConfig"] = interpolate(

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in to_request(self)
    106             if self.pipeline_experiment_config is not None
    107             else None,
--> 108             "Steps": build_steps(self.steps, self.name),
    109         }
    110 

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/utilities.py in build_steps(steps, pipeline_name)
     98                 pipeline_name, step.name, get_code_hash(step), get_config_hash(step)
     99             ):
--> 100                 request_dicts.append(step.to_request())
    101     return request_dicts
    102 

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/steps.py in to_request(self)
    506     def to_request(self) -> RequestType:
    507         """Updates the request dictionary with cache configuration."""
--> 508         request_dict = super().to_request()
    509         if self.cache_config:
    510             request_dict.update(self.cache_config.config)

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/steps.py in to_request(self)
    350     def to_request(self) -> RequestType:
    351         """Gets the request structure for `ConfigurableRetryStep`."""
--> 352         step_dict = super().to_request()
    353         if self.retry_policies:
    354             step_dict["RetryPolicies"] = self._resolve_retry_policy(self.retry_policies)

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/steps.py in to_request(self)
    119             "Name": self.name,
    120             "Type": self.step_type.value,
--> 121             "Arguments": self.arguments,
    122         }
    123         if self.depends_on:

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/steps.py in arguments(self)
    479             # execute fit function with saved parameters,
    480             # and store args in PipelineSession's _context
--> 481             execute_job_functions(self.step_args)
    482 
    483             # populate request dict with args

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/utilities.py in execute_job_functions(step_args)
    406     """
    407 
--> 408     chained_args = step_args.func(*step_args.func_args, **step_args.func_kwargs)
    409     if isinstance(chained_args, _StepArguments):
    410         execute_job_functions(chained_args)

/opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in fit(self, inputs, wait, logs, job_name, experiment_config)
   1267                 a default job name based on the training image name and current timestamp.
   1268             experiment_config (dict[str, str]): Experiment management configuration.
-> 1269                 Optionally, the dict can contain four keys:
   1270                 'ExperimentName', 'TrialName', 'TrialComponentDisplayName' and 'RunName'..
   1271                 The behavior of setting these keys is as follows:

/opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in start_new(cls, estimator, inputs, experiment_config)
   2213             estimator (sagemaker.estimator.EstimatorBase): Estimator object
   2214                 created by the user.
-> 2215             inputs (str): Parameters used when called
   2216                 :meth:`~sagemaker.estimator.EstimatorBase.fit`.
   2217             experiment_config (dict[str, str]): Experiment management configuration.

/opt/conda/lib/python3.7/site-packages/sagemaker/session.py in train(self, input_mode, input_config, role, job_name, output_config, resource_config, vpc_config, hyperparameters, stop_condition, tags, metric_definitions, enable_network_isolation, image_uri, training_image_config, container_entry_point, container_arguments, algorithm_arn, encrypt_inter_container_traffic, use_spot_instances, checkpoint_s3_uri, checkpoint_local_path, experiment_config, debugger_rule_configs, debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics, profiler_rule_configs, profiler_config, environment, retry_strategy)
    841             not customer_supplied_kms_key
    842             and "InstanceType" in inferred_resource_config
--> 843             and not instance_supports_kms(inferred_resource_config["InstanceType"])
    844             and "VolumeKmsKeyId" in inferred_resource_config
    845         ):

/opt/conda/lib/python3.7/site-packages/sagemaker/utils.py in instance_supports_kms(instance_type)
   1429         ValueError: If the instance type is improperly formatted.
   1430     """
-> 1431     return volume_size_supported(instance_type)

/opt/conda/lib/python3.7/site-packages/sagemaker/utils.py in volume_size_supported(instance_type)
   1420         return "d" not in family and not family.startswith("g5")
   1421     except Exception as e:
-> 1422         raise ValueError(f"Failed to parse instance type '{instance_type}': {str(e)}")
   1423 
   1424 

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/entities.py in __str__(self)
     85         """Override built-in String function for PipelineVariable"""
     86         raise TypeError(
---> 87             "Pipeline variables do not support __str__ operation. "
     88             "Please use `.to_string()` to convert it to string type in execution time"
     89             "or use `.expr` to translate it to Json for display purpose in Python SDK."

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.

System information
A description of your system. Please provide:

• SageMaker Python SDK version: 2.169.0
• Framework name (eg. PyTorch) or algorithm (eg. KMeans): Pipelines
• Framework version:
• Python version: 3
• CPU or GPU: CPU
• Custom Docker image (Y/N):N

Additional context
Add any other context about the problem here.

[joseproura]

Hi, it starts to happen to us without changing source code.
And finally we realize that rolling back sagemaker sdk to 2.160.0 it works again.

[brockwade633]

Hi @joseproura and @urirosenberg, a bug fix regarding this issue was recently merged: #3972. I've executed the provided notebook with no issues. Are you able to verify expected behavior as well, when using the latest version of sagemaker?

!{sys.executable} -m pip install 'sagemaker' --upgrade

[joseproura]

Hi @joseproura and @urirosenberg, a bug fix regarding this issue was recently merged: #3972. I've executed the provided notebook with no issues. Are you able to verify expected behavior as well, when using the latest version of sagemaker?

!{sys.executable} -m pip install 'sagemaker' --upgrade

@brockwade633 yes, with the last version everything goes perfect.
Thanks