Merge branch 'master' into fix/tgi-image-uri-unit-tests

Author: benieric

requirements/extras/test_requirements.txt

@@ -50,3 +50,4 @@ fastapi==0.115.4
 nest-asyncio
 sagemaker-mlflow>=0.1.0
 deepdiff>=8.0.0
+orderly-set<5.4.0

src/sagemaker/processing.py

@@ -314,14 +314,15 @@ def _normalize_args(
                 "code argument has to be a valid S3 URI or local file path "
                 + "rather than a pipeline variable"
             )
+
         if arguments is not None:
             processed_arguments = []
             for arg in arguments:
                 if isinstance(arg, PipelineVariable):
                     processed_value = json.dumps(arg.expr)
                     processed_arguments.append(processed_value)
                 else:
-                    processed_arguments.append(str(arg))
+                    processed_arguments.append(arg)
             arguments = processed_arguments
 
         self._current_job_name = self._generate_current_job_name(job_name=job_name)

tests/unit/sagemaker/workflow/test_processing_step.py

@@ -824,12 +824,14 @@ def test_spark_processor(spark_processor, processing_input, pipeline_session):
     processor, run_inputs = spark_processor
     processor.sagemaker_session = pipeline_session
     processor.role = ROLE
+
     arguments_output = [
         "--input",
         "input-data-uri",
         "--output",
         '{"Get": "Parameters.MyArgOutput"}',
     ]
+
     run_inputs["inputs"] = processing_input
 
     step_args = processor.run(**run_inputs)
@@ -1024,6 +1026,7 @@ def test_spark_processor_local_code(spark_processor, processing_input, pipeline_
     processor, run_inputs = spark_processor
     processor.sagemaker_session = pipeline_session
     processor.role = ROLE
+
     arguments_output = [
         "--input",
         "input-data-uri",