hello, Thanks for your amazing job~ I followed your code to build end-to-end Hugging Face Transformers with Sagemaker Pipeline. This code use real-time inference and create an endpoint, but I want to achieve batch transform inference.

My code looks like below. ` from sagemaker.huggingface.model import HuggingFaceModel from sagemaker.s3 import S3Uploader,s3_path_join from sagemaker.inputs import TransformInput from sagemaker.workflow.steps import TransformStep

huggingfacemodel = HuggingFaceModel( model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts, # path to your trained sagemaker mod role=role, transformers_version=_transformers_version, pytorch_version=_pytorch_version, py_version=_py_version, sagemaker_session=sagemaker_session, )

output_s3_path = s3_path_join("s3://",'sagemaker-us-east-1-7348***',"**y-datalab-modela/output") transformer_instance_type=ParameterString(name="Fp16", default_value="False") transformer = huggingfacemodel.transformer( instance_count=1, instance_type="ml.m5.4xlarge", output_path=output_s3_path, strategy='SingleRecord', )

batch_data = ParameterString( name="BatchData", default_value='s3://sagemaker-us-east-1-73483***/**y-datalab-modela/test/stary_data.jsonl', ) transform_step = TransformStep( name="Batch", transformer=transformer, inputs=TransformInput( data=batch_data, content_type='application/json', split_type="Line", ) )

`

but when I run the code, it returns error: ‘AttributeError: 'Properties' object has no attribute 'decode'’.

Could you please help in this issue or could you please provide some sample code about how to use batch transform pipeline? Thank you!

AlgorithmError: ExecuteUserScriptError: Command "/opt/conda/bin/python3.8 train.py --epochs 1 --eval_batch_size 64 --fp16 True --learning_rate 3e-5 --model_id distilbert-base-uncased --train_batch_size 32" Traceback (most recent call last): File "train.py", line 46, in train_dataset = load_from_disk(args.training_dir)

I am trying to run this tutorial as it is and always run into AttributeError: 'ParameterString' object has no attribute 'startswith', when json.loads(pipeline.definition()) is executed.

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-32-d1b69f80bc6f> in <module>
      1 import json
      2 
----> 3 json.loads(pipeline.definition())

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in definition(self)
    319     def definition(self) -> str:
    320         """Converts a request structure to string representation for workflow service calls."""
--> 321         request_dict = self.to_request()
    322         self._interpolate_step_collection_name_in_depends_on(request_dict["Steps"])
    323         request_dict["PipelineExperimentConfig"] = interpolate(

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/pipeline.py in to_request(self)
    103             if self.pipeline_experiment_config is not None
    104             else None,
--> 105             "Steps": list_to_request(self.steps),
    106         }
    107 

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/utilities.py in list_to_request(entities)
     51     for entity in entities:
     52         if isinstance(entity, Entity):
---> 53             request_dicts.append(entity.to_request())
     54         elif isinstance(entity, StepCollection):
     55             request_dicts.extend(entity.request_dicts())

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/steps.py in to_request(self)
    497     def to_request(self) -> RequestType:
    498         """Updates the request dictionary with cache configuration."""
--> 499         request_dict = super().to_request()
    500         if self.cache_config:
    501             request_dict.update(self.cache_config.config)

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/steps.py in to_request(self)
    349     def to_request(self) -> RequestType:
    350         """Gets the request structure for `ConfigurableRetryStep`."""
--> 351         step_dict = super().to_request()
    352         if self.retry_policies:
    353             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)
    118             "Name": self.name,
    119             "Type": self.step_type.value,
--> 120             "Arguments": self.arguments,
    121         }
    122         if self.depends_on:

/opt/conda/lib/python3.7/site-packages/sagemaker/workflow/steps.py in arguments(self)
    478             self.estimator._prepare_for_training(self.job_name)
    479             train_args = _TrainingJob._get_train_args(
--> 480                 self.estimator, self.inputs, experiment_config=dict()
    481             )
    482             request_dict = self.estimator.sagemaker_session._get_train_request(**train_args)

/opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in _get_train_args(cls, estimator, inputs, experiment_config)
   2038         train_args = config.copy()
   2039         train_args["input_mode"] = estimator.input_mode
-> 2040         train_args["job_name"] = estimator._current_job_name
   2041         train_args["hyperparameters"] = hyperparameters
   2042         train_args["tags"] = estimator.tags

/opt/conda/lib/python3.7/site-packages/sagemaker/estimator.py in training_image_uri(self, region)
   3034         trains the model, calls this method to find the hyperparameters.
   3035 
-> 3036         Returns:
   3037             dict[str, str]: The hyperparameters.
   3038         """

/opt/conda/lib/python3.7/site-packages/sagemaker/image_uris.py in get_training_image_uri(region, framework, framework_version, py_version, image_uri, distribution, compiler_config, tensorflow_version, pytorch_version, instance_type)
    497 
    498     if tensorflow_version is not None or pytorch_version is not None:
--> 499         processor = _processor(instance_type, ["cpu", "gpu"])
    500         is_native_huggingface_gpu = processor == "gpu" and not compiler_config
    501         container_version = "cu110-ubuntu18.04" if is_native_huggingface_gpu else None

/opt/conda/lib/python3.7/site-packages/sagemaker/image_uris.py in _processor(instance_type, available_processors, serverless_inference_config)
    366         )
    367 
--> 368     if instance_type.startswith("local"):
    369         processor = "cpu" if instance_type == "local" else "gpu"
    370     elif instance_type.startswith("neuron"):

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

Converting ParameterString to str like,

pipeline = Pipeline(
    name=f"HuggingFaceDemoPipeline",
    parameters=[
        str(model_id),
        str(dataset_name),
        str(processing_instance_type),
        processing_instance_count,
        processing_script,
        training_entry_point,
        training_source_dir,
        training_instance_type,
        training_instance_count,
        evaluation_script,
        threshold_accuracy,
        epochs,
        eval_batch_size,
        train_batch_size,
        learning_rate,
        fp16
    ],
    steps=[step_process, step_train, step_eval, step_cond],
    sagemaker_session=sagemaker_session,
)

results in TypeError: Pipeline variables do not support __str__ operation. Please use.to_string()to convert it to string type in execution timeor use.exprto translate it to Json for display purpose in Python SDK.

Is there a way to solve this?

I'm trying to set up inference on AWS inf for a pretrained HF transformer. I followed this HF tutorial and ran into errors, opened an issue on aws-neuron-sdk repo, was told that env setup is wrong. I'm quite confused now whether I should follow AWS tutorials, which advise to setup env according tothis AWS doc, or HF tutorials which setup env with two pip install lines. Would appreciate the help!

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