ParameterString' object has no attribute 'decode'

[vmunagal]

---

AttributeError Traceback (most recent call last)

in ----> 1 pipeline.upsert(role_arn=role) /opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in upsert(self, role_arn, description, tags, parallelism_config) 275 raise ValueError("An AWS IAM role is required to create or update a Pipeline.") 276 try: --> 277 response = self.create(role_arn, description, tags, parallelism_config) 278 except ClientError as ce: 279 error_code = ce.response["Error"]["Code"] /opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in create(self, role_arn, description, tags, parallelism_config) 144 tags = _append_project_tags(tags) 145 tags = self.sagemaker_session._append_sagemaker_config_tags(tags, PIPELINE_TAGS_PATH) --> 146 kwargs = self._create_args(role_arn, description, parallelism_config) 147 update_args( 148 kwargs, /opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in _create_args(self, role_arn, description, parallelism_config) 166 A keyword argument dict for calling create_pipeline. 167 """ --> 168 pipeline_definition = self.definition() 169 kwargs = dict( 170 PipelineName=self.name, /opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in definition(self) 352 def definition(self) -> str: 353 """Converts a request structure to string representation for workflow service calls.""" --> 354 request_dict = self.to_request() 355 self._interpolate_step_collection_name_in_depends_on(request_dict["Steps"]) 356 request_dict["PipelineExperimentConfig"] = interpolate( /opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in to_request(self) 105 if self.pipeline_experiment_config is not None 106 else None, --> 107 "Steps": build_steps(self.steps, self.name), 108 } 109 /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) 485 request_dict = estimator.sagemaker_session.context.args 486 else: --> 487 self.estimator._prepare_for_training(self.job_name) 488 train_args = _TrainingJob._get_train_args( 489 self.estimator, self.inputs, experiment_config=dict() /opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in _prepare_for_training(self, job_name) 3178 constructor if applicable. 3179 """ -> 3180 super(Framework, self)._prepare_for_training(job_name=job_name) 3181 3182 self._validate_and_set_debugger_configs() /opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in _prepare_for_training(self, job_name) 767 constructor if applicable. 768 """ --> 769 self._current_job_name = self._get_or_create_name(job_name) 770 771 # if output_path was specified we use it otherwise initialize here. /opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in _get_or_create_name(self, name) 741 return name 742 --> 743 self._ensure_base_job_name() 744 return name_from_base(self.base_job_name) 745 /opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in _ensure_base_job_name(self) 722 self.base_job_name = ( 723 self.base_job_name --> 724 or get_jumpstart_base_name_if_jumpstart_model(self.source_dir, self.model_uri) 725 or base_name_from_image( 726 self.training_image_uri(), default_base_name=EstimatorBase.JOB_CLASS_NAME /opt/conda/lib/python3.7/site-packages/sagemaker/jumpstart/utils.py in get_jumpstart_base_name_if_jumpstart_model(*uris) 255 """ 256 for uri in uris: --> 257 if is_jumpstart_model_uri(uri): 258 return constants.JUMPSTART_RESOURCE_BASE_NAME 259 return None /opt/conda/lib/python3.7/site-packages/sagemaker/jumpstart/utils.py in is_jumpstart_model_uri(uri) 181 182 bucket = None --> 183 if urlparse(uri).scheme == "s3": 184 bucket, _ = parse_s3_url(uri) 185 /opt/conda/lib/python3.7/urllib/parse.py in urlparse(url, scheme, allow_fragments) 365 Note that we don't break the components up in smaller bits 366 (e.g. netloc is a single string) and we don't expand % escapes.""" --> 367 url, scheme, _coerce_result = _coerce_args(url, scheme) 368 splitresult = urlsplit(url, scheme, allow_fragments) 369 scheme, netloc, url, query, fragment = splitresult /opt/conda/lib/python3.7/urllib/parse.py in _coerce_args(*args) 121 if str_input: 122 return args + (_noop,) --> 123 return _decode_args(args) + (_encode_result,) 124 125 # Result objects are more helpful than simple tuples /opt/conda/lib/python3.7/urllib/parse.py in _decode_args(args, encoding, errors) 105 def _decode_args(args, encoding=_implicit_encoding, 106 errors=_implicit_errors): --> 107 return tuple(x.decode(encoding, errors) if x else '' for x in args) 108 109 def _coerce_args(*args): /opt/conda/lib/python3.7/urllib/parse.py in (.0) 105 def _decode_args(args, encoding=_implicit_encoding, 106 errors=_implicit_errors): --> 107 return tuple(x.decode(encoding, errors) if x else '' for x in args) 108 109 def _coerce_args(*args): AttributeError: 'ParameterString' object has no attribute 'decode'

[philschmid]

Did you run the exact same code?