feature: added monitor batch transform step (pipeline)

src/sagemaker/workflow/monitor_batch_transform_step.py

@@ -0,0 +1,159 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""The `MonitorBatchTransform` definition for SageMaker Pipelines Workflows"""
+from __future__ import absolute_import
+import logging
+from typing import Union, Optional, List
+
+from sagemaker.session import Session
+from sagemaker.workflow.pipeline_context import _JobStepArguments
+from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.workflow.step_collections import StepCollection
+from sagemaker.workflow.quality_check_step import (
+    QualityCheckStep,
+    QualityCheckConfig,
+    DataQualityCheckConfig,
+)
+from sagemaker.workflow.clarify_check_step import (
+    ClarifyCheckStep,
+    ClarifyCheckConfig,
+    ModelExplainabilityCheckConfig,
+)
+from sagemaker.workflow.steps import Step
+from sagemaker.workflow.check_job_config import CheckJobConfig
+from sagemaker.workflow.steps import TransformStep
+from sagemaker.workflow.utilities import validate_step_args_input
+
+
+class MonitorBatchTransformStep(StepCollection):
+    """Creates a Transformer step with Quality or Clarify check step
+
+    Used to monitor the inputs and outputs of the batch transform job.
+    """
+
+    def __init__(
+        self,
+        name: str,
+        transform_step_args: _JobStepArguments,
+        monitor_configuration: Union[QualityCheckConfig, ClarifyCheckConfig],
+        check_job_configuration: CheckJobConfig,
+        monitor_before_transform: bool = False,
+        fail_on_violation: Union[bool, PipelineVariable] = True,
+        supplied_baseline_statistics: Union[str, PipelineVariable] = None,
+        supplied_baseline_constraints: Union[str, PipelineVariable] = None,
+        display_name: Optional[str] = None,
+        description: Optional[str] = None,
+    ):
+        """Construct a step collection of `TransformStep`, `QualityCheckStep` or `ClarifyCheckStep`
+
+        Args:
+            name (str): The name of the `MonitorBatchTransformStep`.
+                The corresponding transform step will be named `{name}-transform`;
+                and the corresponding check step will be named `{name}-monitoring`
+            transform_step_args (_JobStepArguments): the transform step transform arguments.
+            monitor_configuration (Union[
+                `sagemaker.workflow.quality_check_step.QualityCheckConfig`,
+                `sagemaker.workflow.quality_check_step.ClarifyCheckConfig`
+            ]): the monitoring configuration used for run model monitoring.
+            check_job_configuration (`sagemaker.workflow.check_job_config.CheckJobConfig`):
+                the check job (processing job) cluster resource configuration.
+            monitor_before_transform (bool): If to run data quality or model explainability
+                monitoring type, a true value of this flag indicates
+                running the check step before the transform job.
+            fail_on_violation (Union[bool, PipelineVariable]): A opt-out flag to not to fail the
+                check step when a violation is detected.
+            supplied_baseline_statistics (Union[str, PipelineVariable]): The S3 path
+                to the supplied statistics object representing the statistics JSON file
+                which will be used for drift to check (default: None).
+            supplied_baseline_constraints (Union[str, PipelineVariable]): The S3 path
+                to the supplied constraints object representing the constraints JSON file
+                which will be used for drift to check (default: None).
+            display_name (str): The display name of the `MonitorBatchTransformStep`.
+                The display name provides better UI readability.
+                The corresponding transform step will be
+                named `{display_name}-transform`;  and the corresponding check step
+                will be named `{display_name}-monitoring` (default: None).
+            description (str): The description of the `MonitorBatchTransformStep` (default: None).
+        """
+        self.name: str = name
+        self.steps: List[Step] = []
+
+        validate_step_args_input(
+            step_args=transform_step_args,
+            expected_caller={
+                Session.transform.__name__,
+            },
+            error_message="The transform_step_args of MonitorBatchTransformStep"
+            "must be obtained from transformer.transform()",
+        )
+        transform_step = TransformStep(
+            name=f"{name}-transform",
+            display_name=f"{display_name}-transform" if display_name else None,
+            description=description,
+            step_args=transform_step_args,
+        )
+
+        self.steps.append(transform_step)
+
+        monitoring_step_name = f"{name}-monitoring"
+        monitoring_step_display_name = f"{display_name}-monitoring" if display_name else None
+        if isinstance(monitor_configuration, QualityCheckConfig):
+            monitoring_step = QualityCheckStep(
+                name=monitoring_step_name,
+                display_name=monitoring_step_display_name,
+                description=description,
+                quality_check_config=monitor_configuration,
+                check_job_config=check_job_configuration,
+                skip_check=False,
+                supplied_baseline_statistics=supplied_baseline_statistics,
+                supplied_baseline_constraints=supplied_baseline_constraints,
+                fail_on_violation=fail_on_violation,
+            )
+        elif isinstance(monitor_configuration, ClarifyCheckConfig):
+            if supplied_baseline_statistics:
+                logging.warning(
+                    "supplied_baseline_statistics will be ignored if monitor_configuration "
+                    "is a ClarifyCheckConfig"
+                )
+            monitoring_step = ClarifyCheckStep(
+                name=monitoring_step_name,
+                display_name=monitoring_step_display_name,
+                description=description,
+                clarify_check_config=monitor_configuration,
+                check_job_config=check_job_configuration,
+                skip_check=False,
+                supplied_baseline_constraints=supplied_baseline_constraints,
+                fail_on_violation=fail_on_violation,
+            )
+        else:
+            raise ValueError(
+                f"Unrecognized monitoring configuration: {monitor_configuration}"
+                f"Should be an instance of either QualityCheckConfig or ClarifyCheckConfig"
+            )
+
+        self.steps.append(monitoring_step)
+
+        if monitor_before_transform and not (
+            isinstance(
+                monitor_configuration, (DataQualityCheckConfig, ModelExplainabilityCheckConfig)
+            )
+        ):
+            raise ValueError(
+                "monitor_before_transform only take effect when the monitor_configuration "
+                "is one of [DataQualityCheckConfig, ModelExplainabilityCheckConfig]"
+            )
+
+        if monitor_before_transform:
+            transform_step.add_depends_on([monitoring_step])
+        else:
+            monitoring_step.add_depends_on([transform_step])

tests/data/xgboost_abalone/inference.py

@@ -54,7 +54,7 @@ def output_fn(predictions, content_type):
     """
     After invoking predict_fn, the model server invokes `output_fn`.
     """
-    if content_type == "text/csv":
+    if content_type == "text/csv" or content_type == "application/json":
         return ",".join(str(x) for x in predictions[0])
     else:
         raise ValueError("Content type {} is not supported.".format(content_type))