fix: bug on AutoMLInput to allow PipelineVariable

Co-authored-by: Xinlu Tu <xinltu@amazon.com>

Author: 2 people

src/sagemaker/automl/automl.py

@@ -22,6 +22,7 @@
 from sagemaker.job import _Job
 from sagemaker.session import Session
 from sagemaker.utils import name_from_base
+from sagemaker.workflow.entities import PipelineVariable
 from sagemaker.workflow.pipeline_context import runnable_by_pipeline
 
 logger = logging.getLogger("sagemaker")
@@ -44,18 +45,20 @@ def __init__(
     ):
         """Convert an S3 Uri or a list of S3 Uri to an AutoMLInput object.
 
-        :param inputs (str, list[str]): a string or a list of string that points to (a)
-            S3 location(s) where input data is stored.
-        :param target_attribute_name (str): the target attribute name for regression
-            or classification.
-        :param compression (str): if training data is compressed, the compression type.
-            The default value is None.
-        :param channel_type (str): The channel type an enum to specify
-            whether the input resource is for training or validation.
-            Valid values: training or validation.
-        :param content_type (str): The content type of the data from the input source.
-        :param s3_data_type (str): The data type for S3 data source.
-            Valid values: ManifestFile or S3Prefix.
+        Args:
+            inputs (str, list[str], PipelineVariable):
+                a string or a list of string or a PipelineVariable that points to (a)
+                S3 location(s) where input data is stored.
+            target_attribute_name (str): the target attribute name for regression
+                or classification.
+            compression (str): if training data is compressed, the compression type.
+                The default value is None.
+            channel_type (str): The channel type an enum to specify
+                whether the input resource is for training or validation.
+                Valid values: training or validation.
+            content_type (str): The content type of the data from the input source.
+            s3_data_type (str): The data type for S3 data source.
+                Valid values: ManifestFile or S3Prefix.
         """
         self.inputs = inputs
         self.target_attribute_name = target_attribute_name
@@ -70,6 +73,8 @@ def to_request_dict(self):
         auto_ml_input = []
         if isinstance(self.inputs, string_types):
             self.inputs = [self.inputs]
+        if isinstance(self.inputs, PipelineVariable):
+            self.inputs = [self.inputs]
         for entry in self.inputs:
             input_entry = {
                 "DataSource": {"S3DataSource": {"S3DataType": "S3Prefix", "S3Uri": entry}},
@@ -106,7 +111,7 @@ def __init__(
         max_candidates: Optional[int] = None,
         max_runtime_per_training_job_in_seconds: Optional[int] = None,
         total_job_runtime_in_seconds: Optional[int] = None,
-        job_objective: Optional[str] = None,
+        job_objective: Optional[Dict[str, str]] = None,
         generate_candidate_definitions_only: Optional[bool] = False,
         tags: Optional[List[Dict[str, str]]] = None,
         content_type: Optional[str] = None,
@@ -142,8 +147,9 @@ def __init__(
                 that each training job executed inside hyperparameter tuning
                 is allowed to run as part of a hyperparameter tuning job.
             total_job_runtime_in_seconds (int): the total wait time of an AutoML job.
-            job_objective (str): Defines the objective metric
+            job_objective (dict[str, str]): Defines the objective metric
                 used to measure the predictive quality of an AutoML job.
+                In the format of: {"MetricName": str}
             generate_candidate_definitions_only (bool): Whether to generates
                 possible candidates without training the models.
             tags (List[dict[str, str]]): The list of tags to attach to this
@@ -969,8 +975,10 @@ def _prepare_auto_ml_stop_condition(
 
         Returns (dict): an AutoML CompletionCriteria.
         """
-        stopping_condition = {"MaxCandidates": max_candidates}
+        stopping_condition = {}
 
+        if max_candidates is not None:
+            stopping_condition["MaxCandidates"] = max_candidates
         if max_runtime_per_training_job_in_seconds is not None:
             stopping_condition[
                 "MaxRuntimePerTrainingJobInSeconds"

src/sagemaker/workflow/automl_step.py

@@ -71,15 +71,15 @@ def __init__(
 
         root_property = Properties(step_name=name, shape_name="DescribeAutoMLJobResponse")
 
-        best_candidate_properties = Properties(step_name=name, path="bestCandidateProperties")
-        best_candidate_properties.__dict__["modelInsightsJsonReportPath"] = Properties(
-            step_name=name, path="bestCandidateProperties.modelInsightsJsonReportPath"
+        best_candidate_properties = Properties(step_name=name, path="BestCandidateProperties")
+        best_candidate_properties.__dict__["ModelInsightsJsonReportPath"] = Properties(
+            step_name=name, path="BestCandidateProperties.ModelInsightsJsonReportPath"
         )
-        best_candidate_properties.__dict__["explainabilityJsonReportPath"] = Properties(
-            step_name=name, path="bestCandidateProperties.explainabilityJsonReportPath"
+        best_candidate_properties.__dict__["ExplainabilityJsonReportPath"] = Properties(
+            step_name=name, path="BestCandidateProperties.ExplainabilityJsonReportPath"
         )
 
-        root_property.__dict__["bestCandidateProperties"] = best_candidate_properties
+        root_property.__dict__["BestCandidateProperties"] = best_candidate_properties
         self._properties = root_property
 
     @property

tests/integ/sagemaker/workflow/test_automl_steps.py

@@ -14,14 +14,15 @@
 
 import os
 
+import boto3
 import pytest
 from botocore.exceptions import WaiterError
 
+from sagemaker.workflow import ParameterString
 from sagemaker.workflow.automl_step import AutoMLStep
 from sagemaker.automl.automl import AutoML, AutoMLInput
 
-from sagemaker import utils, get_execution_role
-from sagemaker.utils import unique_name_from_base
+from sagemaker import utils, get_execution_role, ModelMetrics, MetricsSource
 from sagemaker.workflow.model_step import ModelStep
 from sagemaker.workflow.pipeline import Pipeline
 
@@ -50,10 +51,8 @@ def test_automl_step(pipeline_session, role, pipeline_name):
         role=role,
         target_attribute_name=TARGET_ATTRIBUTE_NAME,
         sagemaker_session=pipeline_session,
-        max_candidates=1,
         mode=MODE,
     )
-    job_name = unique_name_from_base("auto-ml", max_length=32)
     s3_input_training = pipeline_session.upload_data(
         path=TRAINING_DATA, key_prefix=PREFIX + "/input"
     )
@@ -72,27 +71,56 @@ def test_automl_step(pipeline_session, role, pipeline_name):
     )
     inputs = [input_training, input_validation]
 
-    step_args = auto_ml.fit(inputs=inputs, job_name=job_name)
+    step_args = auto_ml.fit(inputs=inputs)
 
     automl_step = AutoMLStep(
         name="MyAutoMLStep",
         step_args=step_args,
     )
 
     automl_model = automl_step.get_best_auto_ml_model(sagemaker_session=pipeline_session, role=role)
-
     step_args_create_model = automl_model.create(
         instance_type="c4.4xlarge",
     )
-
     automl_model_step = ModelStep(
         name="MyAutoMLModelStep",
         step_args=step_args_create_model,
     )
 
+    model_package_group_name = ParameterString(
+        name="ModelPackageName", default_value="AutoMlModelPackageGroup"
+    )
+    model_approval_status = ParameterString(name="ModelApprovalStatus", default_value="Approved")
+    model_metrics = ModelMetrics(
+        model_statistics=MetricsSource(
+            s3_uri=automl_step.properties.BestCandidateProperties.ModelInsightsJsonReportPath,
+            content_type="application/json",
+        ),
+        explainability=MetricsSource(
+            s3_uri=automl_step.properties.BestCandidateProperties.ExplainabilityJsonReportPath,
+            content_type="application/json",
+        ),
+    )
+    step_args_register_model = automl_model.register(
+        content_types=["text/csv"],
+        response_types=["text/csv"],
+        inference_instances=["ml.m5.xlarge"],
+        transform_instances=["ml.m5.xlarge"],
+        model_package_group_name=model_package_group_name,
+        approval_status=model_approval_status,
+        model_metrics=model_metrics,
+    )
+    register_model_step = ModelStep(
+        name="ModelRegistrationStep", step_args=step_args_register_model
+    )
+
     pipeline = Pipeline(
         name=pipeline_name,
-        steps=[automl_step, automl_model_step],
+        parameters=[
+            model_approval_status,
+            model_package_group_name,
+        ],
+        steps=[automl_step, automl_model_step, register_model_step],
         sagemaker_session=pipeline_session,
     )
 
@@ -114,9 +142,20 @@ def test_automl_step(pipeline_session, role, pipeline_name):
                 assert step["Metadata"]["AutoMLJob"]["Arn"] is not None
 
         assert has_automl_job
-        assert len(execution_steps) == 2
+        assert len(execution_steps) == 3
     finally:
         try:
+            sagemaker_client = boto3.client("sagemaker")
+            for model_package in sagemaker_client.list_model_packages(
+                ModelPackageGroupName="AutoMlModelPackageGroup"
+            )["ModelPackageSummaryList"]:
+                sagemaker_client.delete_model_package(
+                    ModelPackageName=model_package["ModelPackageArn"]
+                )
+            sagemaker_client.delete_model_package_group(
+                ModelPackageGroupName="AutoMlModelPackageGroup"
+            )
+
             pipeline.delete()
         except Exception:
             pass

tests/integ/test_auto_ml.py

@@ -78,7 +78,6 @@ def test_auto_ml_fit_local_input(sagemaker_session):
         role=ROLE,
         target_attribute_name=TARGET_ATTRIBUTE_NAME,
         sagemaker_session=sagemaker_session,
-        max_candidates=1,
         generate_candidate_definitions_only=True,
     )
 

tests/unit/sagemaker/automl/test_auto_ml.py

@@ -18,6 +18,7 @@
 from mock import Mock, patch
 from sagemaker import AutoML, AutoMLJob, AutoMLInput, CandidateEstimator, PipelineModel
 from sagemaker.predictor import Predictor
+from sagemaker.workflow.functions import Join
 
 MODEL_DATA = "s3://bucket/model.tar.gz"
 MODEL_IMAGE = "mi"
@@ -52,7 +53,7 @@
 MAX_RUNTIME_PER_TRAINING_JOB = 3600
 TOTAL_JOB_RUNTIME = 36000
 TARGET_OBJECTIVE = "0.01"
-JOB_OBJECTIVE = {"fake job objective"}
+JOB_OBJECTIVE = {"MetricName": "F1"}
 TAGS = [{"Name": "some-tag", "Value": "value-for-tag"}]
 CONTENT_TYPE = "x-application/vnd.amazon+parquet"
 S3_DATA_TYPE = "ManifestFile"
@@ -503,7 +504,46 @@ def test_auto_ml_default_fit(strftime, sagemaker_session):
         ],
         "output_config": {"S3OutputPath": DEFAULT_OUTPUT_PATH},
         "auto_ml_job_config": {
-            "CompletionCriteria": {"MaxCandidates": DEFAULT_MAX_CANDIDATES},
+            "CompletionCriteria": {},
+            "SecurityConfig": {
+                "EnableInterContainerTrafficEncryption": ENCRYPT_INTER_CONTAINER_TRAFFIC
+            },
+        },
+        "role": ROLE,
+        "job_name": DEFAULT_JOB_NAME,
+        "problem_type": None,
+        "job_objective": None,
+        "generate_candidate_definitions_only": GENERATE_CANDIDATE_DEFINITIONS_ONLY,
+        "tags": None,
+    }
+
+
+@patch("time.strftime", return_value=TIMESTAMP)
+def test_auto_ml_default_fit_with_pipeline_variable(strftime, sagemaker_session):
+    auto_ml = AutoML(
+        role=ROLE,
+        target_attribute_name=TARGET_ATTRIBUTE_NAME,
+        sagemaker_session=sagemaker_session,
+    )
+    inputs = Join(on="/", values=[DEFAULT_S3_INPUT_DATA, "ProcessingJobName"])
+    auto_ml.fit(inputs=AutoMLInput(inputs=inputs, target_attribute_name=TARGET_ATTRIBUTE_NAME))
+    sagemaker_session.auto_ml.assert_called_once()
+    _, args = sagemaker_session.auto_ml.call_args
+    assert args == {
+        "input_config": [
+            {
+                "DataSource": {
+                    "S3DataSource": {
+                        "S3DataType": "S3Prefix",
+                        "S3Uri": Join(on="/", values=["s3://mybucket/data", "ProcessingJobName"]),
+                    }
+                },
+                "TargetAttributeName": TARGET_ATTRIBUTE_NAME,
+            }
+        ],
+        "output_config": {"S3OutputPath": DEFAULT_OUTPUT_PATH},
+        "auto_ml_job_config": {
+            "CompletionCriteria": {},
             "SecurityConfig": {
                 "EnableInterContainerTrafficEncryption": ENCRYPT_INTER_CONTAINER_TRAFFIC
             },

tests/unit/sagemaker/workflow/test_automl_step.py

@@ -231,11 +231,11 @@ def test_single_automl_step_with_parameter(pipeline_session):
         step_args=step_args,
     )
 
-    assert automl_step.properties.bestCandidateProperties.modelInsightsJsonReportPath.expr == {
-        "Get": "Steps.MyAutoMLStep.bestCandidateProperties.modelInsightsJsonReportPath"
+    assert automl_step.properties.BestCandidateProperties.ModelInsightsJsonReportPath.expr == {
+        "Get": "Steps.MyAutoMLStep.BestCandidateProperties.ModelInsightsJsonReportPath"
     }
-    assert automl_step.properties.bestCandidateProperties.explainabilityJsonReportPath.expr == {
-        "Get": "Steps.MyAutoMLStep.bestCandidateProperties.explainabilityJsonReportPath"
+    assert automl_step.properties.BestCandidateProperties.ExplainabilityJsonReportPath.expr == {
+        "Get": "Steps.MyAutoMLStep.BestCandidateProperties.ExplainabilityJsonReportPath"
     }
 
     pipeline = Pipeline(