feature: support JsonGet/Join parameterization in tuning step Hyperparameters

src/sagemaker/parameter.py

@@ -15,6 +15,8 @@
 
 import json
 from sagemaker.workflow.parameters import Parameter as PipelineParameter
+from sagemaker.workflow.functions import JsonGet as PipelineJsonGet
+from sagemaker.workflow.functions import Join as PipelineJoin
 
 
 class ParameterRange(object):
@@ -71,10 +73,10 @@ def as_tuning_range(self, name):
         return {
             "Name": name,
             "MinValue": str(self.min_value)
-            if not isinstance(self.min_value, PipelineParameter)
+            if not isinstance(self.min_value, (PipelineParameter, PipelineJsonGet, PipelineJoin))
             else self.min_value,
             "MaxValue": str(self.max_value)
-            if not isinstance(self.max_value, PipelineParameter)
+            if not isinstance(self.max_value, (PipelineParameter, PipelineJsonGet, PipelineJoin))
             else self.max_value,
             "ScalingType": self.scaling_type,
         }
@@ -108,10 +110,11 @@ def __init__(self, values):  # pylint: disable=super-init-not-called
             values (list or object): The possible values for the hyperparameter.
                 This input will be converted into a list of strings.
         """
-        if isinstance(values, list):
-            self.values = [str(v) if not isinstance(v, PipelineParameter) else v for v in values]
-        else:
-            self.values = [str(values) if not isinstance(values, PipelineParameter) else values]
+        values = values if isinstance(values, list) else [values]
+        self.values = [
+            str(v) if not isinstance(v, (PipelineParameter, PipelineJsonGet, PipelineJoin)) else v
+            for v in values
+        ]
 
     def as_tuning_range(self, name):
         """Represent the parameter range as a dictionary.

src/sagemaker/tuner.py

@@ -38,6 +38,10 @@
     IntegerParameter,
     ParameterRange,
 )
+from sagemaker.workflow.parameters import Parameter as PipelineParameter
+from sagemaker.workflow.functions import JsonGet as PipelineJsonGet
+from sagemaker.workflow.functions import Join as PipelineJoin
+
 from sagemaker.session import Session
 from sagemaker.utils import base_from_name, base_name_from_image, name_from_base
 
@@ -59,6 +63,18 @@
 logger = logging.getLogger(__name__)
 
 
+def is_pipeline_parameters(value):
+    """Determine if a value is a pipeline parameter or function representation
+
+    Args:
+        value (float or int): The value to be verified.
+
+    Returns:
+        bool: True if it is, False otherwise.
+    """
+    return isinstance(value, (PipelineParameter, PipelineJsonGet, PipelineJoin))
+
+
 class WarmStartTypes(Enum):
     """Warm Start Configuration type.
 
@@ -359,7 +375,12 @@ def _prepare_static_hyperparameters(
     ):
         """Prepare static hyperparameters for one estimator before tuning."""
         # Remove any hyperparameter that will be tuned
-        static_hyperparameters = {str(k): str(v) for (k, v) in estimator.hyperparameters().items()}
+        static_hyperparameters = {
+            str(k): str(v)
+            if not isinstance(v, (PipelineParameter, PipelineJsonGet, PipelineJoin))
+            else v
+            for (k, v) in estimator.hyperparameters().items()
+        }
         for hyperparameter_name in hyperparameter_ranges.keys():
             static_hyperparameters.pop(hyperparameter_name, None)
 

tests/integ/test_workflow.py

@@ -66,14 +66,13 @@
     ConditionIn,
     ConditionLessThanOrEqualTo,
 )
-from sagemaker.workflow.condition_step import ConditionStep, JsonGet
+from sagemaker.workflow.condition_step import ConditionStep
 from sagemaker.workflow.callback_step import CallbackStep, CallbackOutput, CallbackOutputTypeEnum
 from sagemaker.workflow.lambda_step import LambdaStep, LambdaOutput, LambdaOutputTypeEnum
-from sagemaker.workflow.properties import PropertyFile
 from sagemaker.wrangler.processing import DataWranglerProcessor
 from sagemaker.dataset_definition.inputs import DatasetDefinition, AthenaDatasetDefinition
 from sagemaker.workflow.execution_variables import ExecutionVariables
-from sagemaker.workflow.functions import Join
+from sagemaker.workflow.functions import Join, JsonGet
 from sagemaker.wrangler.ingestion import generate_data_ingestion_flow_from_s3_input
 from sagemaker.workflow.parameters import (
     ParameterInteger,
@@ -87,6 +86,7 @@
     TuningStep,
     TransformStep,
     TransformInput,
+    PropertyFile,
 )
 from sagemaker.workflow.step_collections import RegisterModel
 from sagemaker.workflow.pipeline import Pipeline
@@ -137,7 +137,7 @@ def feature_store_session(sagemaker_session):
 
 @pytest.fixture
 def pipeline_name():
-    return f"my-pipeline-{int(time.time() * 10**7)}"
+    return f"my-pipeline-{int(time.time() * 10 ** 7)}"
 
 
 @pytest.fixture
@@ -1371,6 +1371,8 @@ def test_tuning_multi_algos(
     cpu_instance_type,
     pipeline_name,
     region_name,
+    script_dir,
+    athena_dataset_definition,
 ):
     base_dir = os.path.join(DATA_DIR, "pytorch_mnist")
     entry_point = os.path.join(base_dir, "mnist.py")
@@ -1382,6 +1384,42 @@ def test_tuning_multi_algos(
     instance_count = ParameterInteger(name="InstanceCount", default_value=1)
     instance_type = ParameterString(name="InstanceType", default_value="ml.m5.xlarge")
 
+    input_data = f"s3://sagemaker-sample-data-{region_name}/processing/census/census-income.csv"
+
+    sklearn_processor = SKLearnProcessor(
+        framework_version="0.20.0",
+        instance_type=instance_type,
+        instance_count=instance_count,
+        base_job_name="test-sklearn",
+        sagemaker_session=sagemaker_session,
+        role=role,
+    )
+
+    property_file = PropertyFile(
+        name="DataAttributes", output_name="attributes", path="attributes.json"
+    )
+
+    step_process = ProcessingStep(
+        name="my-process",
+        display_name="ProcessingStep",
+        description="description for Processing step",
+        processor=sklearn_processor,
+        inputs=[
+            ProcessingInput(source=input_data, destination="/opt/ml/processing/input"),
+            ProcessingInput(dataset_definition=athena_dataset_definition),
+        ],
+        outputs=[
+            ProcessingOutput(output_name="train_data", source="/opt/ml/processing/train"),
+            ProcessingOutput(output_name="attributes", source="/opt/ml/processing/attributes.json"),
+        ],
+        property_files=[property_file],
+        code=os.path.join(script_dir, "preprocessing.py"),
+    )
+
+    static_hp_1 = ParameterString(name="InstanceType", default_value="ml.m5.xlarge")
+    json_get_hp = JsonGet(
+        step_name=step_process.name, property_file=property_file, json_path="train_size"
+    )
     pytorch_estimator = PyTorch(
         entry_point=entry_point,
         role=role,
@@ -1392,10 +1430,11 @@ def test_tuning_multi_algos(
         sagemaker_session=sagemaker_session,
         enable_sagemaker_metrics=True,
         max_retry_attempts=3,
+        hyperparameters={"static-hp": static_hp_1, "train_size": json_get_hp},
     )
 
     min_batch_size = ParameterString(name="MinBatchSize", default_value="64")
-    max_batch_size = ParameterString(name="MaxBatchSize", default_value="128")
+    max_batch_size = json_get_hp
 
     tuner = HyperparameterTuner.create(
         estimator_dict={
@@ -1415,6 +1454,7 @@ def test_tuning_multi_algos(
             "estimator-2": [{"Name": "test:acc", "Regex": "Overall test accuracy: (.*?);"}],
         },
     )
+
     inputs = {
         "estimator-1": TrainingInput(s3_data=input_path),
         "estimator-2": TrainingInput(s3_data=input_path),
@@ -1429,7 +1469,7 @@ def test_tuning_multi_algos(
     pipeline = Pipeline(
         name=pipeline_name,
         parameters=[instance_count, instance_type, min_batch_size, max_batch_size],
-        steps=[step_tune],
+        steps=[step_process, step_tune],
         sagemaker_session=sagemaker_session,
     )
 

tests/unit/test_tuner.py

@@ -30,6 +30,8 @@
     create_transfer_learning_tuner,
     HyperparameterTuner,
 )
+from sagemaker.workflow.functions import JsonGet, Join
+from sagemaker.workflow.parameters import ParameterString, ParameterInteger
 
 from .tuner_test_utils import *  # noqa: F403
 
@@ -68,14 +70,24 @@ def tuner(estimator):
 
 
 def test_prepare_for_training(tuner):
-    static_hyperparameters = {"validated": 1, "another_one": 0}
+    hp1 = JsonGet(step_name="stepname", property_file="pf", json_path="jp")
+    hp2 = Join(on="/", values=["1", "2", ParameterString(name="ps", default_value="3")])
+
+    static_hyperparameters = {
+        "validated": 1,
+        "another_one": 0,
+        "hp1": hp1,
+        "hp2": hp2,
+    }
+
     tuner.estimator.set_hyperparameters(**static_hyperparameters)
     tuner._prepare_for_tuning()
 
     assert tuner._current_job_name.startswith(IMAGE_NAME)
-
-    assert len(tuner.static_hyperparameters) == 1
+    assert len(tuner.static_hyperparameters) == 3
     assert tuner.static_hyperparameters["another_one"] == "0"
+    assert tuner.static_hyperparameters["hp1"] == hp1
+    assert tuner.static_hyperparameters["hp2"] == hp2
 
 
 def test_prepare_for_tuning_with_amazon_estimator(tuner, sagemaker_session):
@@ -1156,6 +1168,20 @@ def test_integer_parameter_ranges():
     assert ranges["ScalingType"] == "Auto"
 
 
+def test_integer_parameter_ranges_with_pipeline_parameter():
+    min = ParameterInteger(name="p", default_value=2)
+    max = JsonGet(step_name="sn", property_file="pf", json_path="jp")
+    scale = ParameterString(name="scale", default_value="Auto")
+    int_param = IntegerParameter(min, max)
+    ranges = int_param.as_tuning_range("some")
+
+    assert len(ranges.keys()) == 4
+    assert ranges["Name"] == "some"
+    assert ranges["MinValue"] == min
+    assert ranges["MaxValue"] == max
+    assert ranges["ScalingType"] == scale
+
+
 def test_integer_parameter_scaling_type():
     int_param = IntegerParameter(2, 3, scaling_type="Linear")
     int_range = int_param.as_tuning_range("range")