feature: Enable step caching

src/sagemaker/workflow/steps.py

@@ -92,6 +92,38 @@ def ref(self) -> Dict[str, str]:
         return {"Name": self.name}
 
 
+@attr.s
+class CacheConfig:
+    """Configuration class to enable caching in pipeline workflow.
+
+    If caching is enabled, the pipeline attempts to find a previous execution of a step
+    that was called with the same arguments. Step caching only considers successful execution.
+    If a successful previous execution is found, the pipeline propagates the values
+    from previous execution rather than recomputing the step. When multiple successful executions
+    exist within the timeout period, it uses the result for the most recent successful execution.
+
+
+    Attributes:
+        enable_caching (bool): To enable step caching. Defaults to `False`.
+        expire_after (str): If step caching is enabled, a timeout also needs to defined.
+            It defines how old a previous execution can be to be considered for reuse.
+            Value should be an ISO 8601 duration string. Defaults to `None`.
+    """
+
+    enable_caching: bool = attr.ib(default=False)
+    expire_after = attr.ib(
+        default=None, validator=attr.validators.optional(attr.validators.instance_of(str))
+    )
+
+    @property
+    def config(self):
+        """Configures caching in pipeline steps."""
+        config = {"Enabled": self.enable_caching}
+        if self.expire_after is not None:
+            config["ExpireAfter"] = self.expire_after
+        return {"CacheConfig": config}
+
+
 class TrainingStep(Step):
     """Training step for workflow."""
 
@@ -100,6 +132,7 @@ def __init__(
         name: str,
         estimator: EstimatorBase,
         inputs: TrainingInput = None,
+        cache_config: CacheConfig = None,
     ):
         """Construct a TrainingStep, given an `EstimatorBase` instance.
 
@@ -110,14 +143,15 @@ def __init__(
             name (str): The name of the training step.
             estimator (EstimatorBase): A `sagemaker.estimator.EstimatorBase` instance.
             inputs (TrainingInput): A `sagemaker.inputs.TrainingInput` instance. Defaults to `None`.
+            cache_config (CacheConfig):  A `sagemaker.workflow.steps.CacheConfig` instance.
         """
         super(TrainingStep, self).__init__(name, StepTypeEnum.TRAINING)
         self.estimator = estimator
         self.inputs = inputs
-
         self._properties = Properties(
             path=f"Steps.{name}", shape_name="DescribeTrainingJobResponse"
         )
+        self.cache_config = cache_config
 
     @property
     def arguments(self) -> RequestType:
@@ -144,6 +178,14 @@ def properties(self):
         """A Properties object representing the DescribeTrainingJobResponse data model."""
         return self._properties
 
+    def to_request(self) -> RequestType:
+        """Updates the dictionary with cache configuration."""
+        request_dict = super().to_request()
+        if self.cache_config:
+            request_dict.update(self.cache_config.config)
+
+        return request_dict
+
 
 class CreateModelStep(Step):
     """CreateModel step for workflow."""
@@ -207,6 +249,7 @@ def __init__(
         name: str,
         transformer: Transformer,
         inputs: TransformInput,
+        cache_config: CacheConfig = None,
     ):
         """Constructs a TransformStep, given an `Transformer` instance.
 
@@ -217,11 +260,12 @@ def __init__(
             name (str): The name of the transform step.
             transformer (Transformer): A `sagemaker.transformer.Transformer` instance.
             inputs (TransformInput): A `sagemaker.inputs.TransformInput` instance.
+            cache_config (CacheConfig):  A `sagemaker.workflow.steps.CacheConfig` instance.
         """
         super(TransformStep, self).__init__(name, StepTypeEnum.TRANSFORM)
         self.transformer = transformer
         self.inputs = inputs
-
+        self.cache_config = cache_config
         self._properties = Properties(
             path=f"Steps.{name}", shape_name="DescribeTransformJobResponse"
         )
@@ -257,6 +301,14 @@ def properties(self):
         """A Properties object representing the DescribeTransformJobResponse data model."""
         return self._properties
 
+    def to_request(self) -> RequestType:
+        """Updates the dictionary with cache configuration."""
+        request_dict = super().to_request()
+        if self.cache_config:
+            request_dict.update(self.cache_config.config)
+
+        return request_dict
+
 
 class ProcessingStep(Step):
     """Processing step for workflow."""
@@ -270,6 +322,7 @@ def __init__(
         job_arguments: List[str] = None,
         code: str = None,
         property_files: List[PropertyFile] = None,
+        cache_config: CacheConfig = None,
     ):
         """Construct a ProcessingStep, given a `Processor` instance.
 
@@ -289,6 +342,7 @@ def __init__(
                 script to run. Defaults to `None`.
             property_files (List[PropertyFile]): A list of property files that workflow looks
                 for and resolves from the configured processing output list.
+            cache_config (CacheConfig):  A `sagemaker.workflow.steps.CacheConfig` instance.
         """
         super(ProcessingStep, self).__init__(name, StepTypeEnum.PROCESSING)
         self.processor = processor
@@ -305,6 +359,7 @@ def __init__(
         self._properties = Properties(
             path=f"Steps.{name}", shape_name="DescribeProcessingJobResponse"
         )
+        self.cache_config = cache_config
 
     @property
     def arguments(self) -> RequestType:
@@ -335,6 +390,8 @@ def properties(self):
     def to_request(self) -> RequestType:
         """Get the request structure for workflow service calls."""
         request_dict = super(ProcessingStep, self).to_request()
+        if self.cache_config:
+            request_dict.update(self.cache_config.config)
         if self.property_files:
             request_dict["PropertyFiles"] = [
                 property_file.expr for property_file in self.property_files

tests/integ/test_workflow.py

@@ -46,6 +46,7 @@
     CreateModelStep,
     ProcessingStep,
     TrainingStep,
+    CacheConfig,
 )
 from sagemaker.workflow.step_collections import RegisterModel
 from sagemaker.workflow.pipeline import Pipeline
@@ -274,6 +275,8 @@ def test_one_step_sklearn_processing_pipeline(
         ProcessingInput(dataset_definition=athena_dataset_definition),
     ]
 
+    cache_config = CacheConfig(enable_caching=True, expire_after="T30m")
+
     sklearn_processor = SKLearnProcessor(
         framework_version=sklearn_latest_version,
         role=role,
@@ -289,6 +292,7 @@ def test_one_step_sklearn_processing_pipeline(
         processor=sklearn_processor,
         inputs=inputs,
         code=script_path,
+        cache_config=cache_config,
     )
     pipeline = Pipeline(
         name=pipeline_name,
@@ -328,6 +332,11 @@ def test_one_step_sklearn_processing_pipeline(
         response = execution.describe()
         assert response["PipelineArn"] == create_arn
 
+        # Check CacheConfig
+        response = json.loads(pipeline.describe()["PipelineDefinition"])["Steps"][0]["CacheConfig"]
+        assert response["Enabled"] == cache_config.enable_caching
+        assert response["ExpireAfter"] == cache_config.expire_after
+
         try:
             execution.wait(delay=30, max_attempts=3)
         except WaiterError:

tests/unit/sagemaker/workflow/test_steps.py

@@ -37,6 +37,7 @@
     TrainingStep,
     TransformStep,
     CreateModelStep,
+    CacheConfig,
 )
 
 REGION = "us-west-2"
@@ -114,10 +115,9 @@ def test_training_step(sagemaker_session):
         sagemaker_session=sagemaker_session,
     )
     inputs = TrainingInput(f"s3://{BUCKET}/train_manifest")
+    cache_config = CacheConfig(enable_caching=True, expire_after="PT1H")
     step = TrainingStep(
-        name="MyTrainingStep",
-        estimator=estimator,
-        inputs=inputs,
+        name="MyTrainingStep", estimator=estimator, inputs=inputs, cache_config=cache_config
     )
     assert step.to_request() == {
         "Name": "MyTrainingStep",
@@ -145,6 +145,7 @@ def test_training_step(sagemaker_session):
             "RoleArn": ROLE,
             "StoppingCondition": {"MaxRuntimeInSeconds": 86400},
         },
+        "CacheConfig": {"Enabled": True, "ExpireAfter": "PT1H"},
     }
     assert step.properties.TrainingJobName.expr == {"Get": "Steps.MyTrainingStep.TrainingJobName"}
 
@@ -163,11 +164,13 @@ def test_processing_step(sagemaker_session):
             destination="processing_manifest",
         )
     ]
+    cache_config = CacheConfig(enable_caching=True, expire_after="PT1H")
     step = ProcessingStep(
         name="MyProcessingStep",
         processor=processor,
         inputs=inputs,
         outputs=[],
+        cache_config=cache_config,
     )
     assert step.to_request() == {
         "Name": "MyProcessingStep",
@@ -197,6 +200,7 @@ def test_processing_step(sagemaker_session):
             },
             "RoleArn": "DummyRole",
         },
+        "CacheConfig": {"Enabled": True, "ExpireAfter": "PT1H"},
     }
     assert step.properties.ProcessingJobName.expr == {
         "Get": "Steps.MyProcessingStep.ProcessingJobName"
@@ -238,10 +242,9 @@ def test_transform_step(sagemaker_session):
         sagemaker_session=sagemaker_session,
     )
     inputs = TransformInput(data=f"s3://{BUCKET}/transform_manifest")
+    cache_config = CacheConfig(enable_caching=True, expire_after="PT1H")
     step = TransformStep(
-        name="MyTransformStep",
-        transformer=transformer,
-        inputs=inputs,
+        name="MyTransformStep", transformer=transformer, inputs=inputs, cache_config=cache_config
     )
     assert step.to_request() == {
         "Name": "MyTransformStep",
@@ -262,6 +265,7 @@ def test_transform_step(sagemaker_session):
                 "InstanceType": "c4.4xlarge",
             },
         },
+        "CacheConfig": {"Enabled": True, "ExpireAfter": "PT1H"},
     }
     assert step.properties.TransformJobName.expr == {
         "Get": "Steps.MyTransformStep.TransformJobName"