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AttributeError Traceback (most recent call last)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/utils.py:1384, in volume_size_supported(instance_type)
1381 try:
1382
1383 # local mode does not support volume size
-> 1384 if instance_type.startswith("local"):
1385 return False
AttributeError: 'ParameterString' object has no attribute 'startswith'
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
Cell In[28], line 4
1 import json
----> 4 definition = json.loads(pipeline.definition())
5 definition
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/pipeline.py:354, in Pipeline.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(
357 request_dict["PipelineExperimentConfig"], {}, {}
358 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/pipeline.py:107, in Pipeline.to_request(self)
98 def to_request(self) -> RequestType:
99 """Gets the request structure for workflow service calls."""
100 return {
101 "Version": self._version,
102 "Metadata": self._metadata,
103 "Parameters": list_to_request(self.parameters),
104 "PipelineExperimentConfig": self.pipeline_experiment_config.to_request()
105 if self.pipeline_experiment_config is not None
106 else None,
--> 107 "Steps": build_steps(self.steps, self.name),
108 }
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/utilities.py:100, in build_steps(steps, pipeline_name)
96 else:
97 with _pipeline_config_manager(
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
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/steps.py:508, in TrainingStep.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)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/steps.py:352, in ConfigurableRetryStep.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)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/steps.py:121, in Step.to_request(self)
116 def to_request(self) -> RequestType:
117 """Gets the request structure for workflow service calls."""
118 request_dict = {
119 "Name": self.name,
120 "Type": self.step_type.value,
--> 121 "Arguments": self.arguments,
122 }
123 if self.depends_on:
124 request_dict["DependsOn"] = self._resolve_depends_on(self.depends_on)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/steps.py:481, in TrainingStep.arguments(self)
476 from sagemaker.workflow.utilities import execute_job_functions
478 if self.step_args:
479 # execute fit function with saved parameters,
480 # and store args in PipelineSession's _context
--> 481 execute_job_functions(self.step_args)
483 # populate request dict with args
484 estimator = self.step_args.func_args[0]
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/utilities.py:408, in execute_job_functions(step_args)
393 def execute_job_functions(step_args: _StepArguments):
394 """Execute the job class functions during pipeline definition construction
395
396 Executes the job functions such as run(), fit(), or transform() that have been
(...)
405 a pipeline step, contains the necessary function information
406 """
--> 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)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/estimator.py:1243, in EstimatorBase.fit(self, inputs, wait, logs, job_name, experiment_config)
1240 self._prepare_for_training(job_name=job_name)
1242 experiment_config = check_and_get_run_experiment_config(experiment_config)
-> 1243 self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config)
1244 self.jobs.append(self.latest_training_job)
1245 if wait:
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/estimator.py:2179, in _TrainingJob.start_new(cls, estimator, inputs, experiment_config)
2154 """Create a new Amazon SageMaker training job from the estimator.
2155
2156 Args:
(...)
2175 all information about the started training job.
2176 """
2177 train_args = cls._get_train_args(estimator, inputs, experiment_config)
-> 2179 estimator.sagemaker_session.train(**train_args)
2181 return cls(estimator.sagemaker_session, estimator._current_job_name)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/session.py:822, in Session.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, 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)
815 customer_supplied_kms_key = "VolumeKmsKeyId" in resource_config
816 inferred_resource_config = update_nested_dictionary_with_values_from_config(
817 resource_config, TRAINING_JOB_RESOURCE_CONFIG_PATH, sagemaker_session=self
818 )
819 if (
820 not customer_supplied_kms_key
821 and "InstanceType" in inferred_resource_config
--> 822 and not instance_supports_kms(inferred_resource_config["InstanceType"])
823 and "VolumeKmsKeyId" in inferred_resource_config
824 ):
825 del inferred_resource_config["VolumeKmsKeyId"]
826 train_request = self._get_train_request(
827 input_mode=input_mode,
828 input_config=input_config,
(...)
854 retry_strategy=retry_strategy,
855 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/utils.py:1409, in instance_supports_kms(instance_type)
1403 def instance_supports_kms(instance_type: str) -> bool:
1404 """Returns True if SageMaker allows KMS keys to be attached to the instance.
1405
1406 Raises:
1407 ValueError: If the instance type is improperly formatted.
1408 """
-> 1409 return volume_size_supported(instance_type)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/utils.py:1400, in volume_size_supported(instance_type)
1398 return "d" not in family and not family.startswith("g5")
1399 except Exception as e:
-> 1400 raise ValueError(f"Failed to parse instance type '{instance_type}': {str(e)}")
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/sagemaker/workflow/entities.py:86, in PipelineVariable.__str__(self)
84 def __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."
90 )
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.
Will be skipping the notebook test for now!
Need to RCA/fix the notebook/sdk code and re-enable this notebook test.
Testing done:
Merge Checklist
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Issue #, if available:
Description of changes:
This notebook is currently failing at: https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/abalone_build_train_deploy/sagemaker-pipelines-preprocess-train-evaluate-batch-transform.ipynb?short_path=bd99ba4#L1306
Traceback:
Will be skipping the notebook test for now!
Need to RCA/fix the notebook/sdk code and re-enable this notebook test.
Testing done:
Merge Checklist
Put an
xin the boxes that apply. You can also fill these out after creating the PR. If you're unsure about any of them, don't hesitate to ask. We're here to help! This is simply a reminder of what we are going to look for before merging your pull request.General
Tests
unique_name_from_baseto create resource names in integ tests (if appropriate)By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.