feat(pipeline): Add Zimmer deployment and lineage step types

sagemaker-mlops/src/sagemaker/mlops/workflow/__init__.py

@@ -14,6 +14,7 @@
 functions, conditions, properties) and can import from sagemaker.train and sagemaker.serve
 for orchestration purposes.
 """
+
 from __future__ import absolute_import
 
 __version__ = "0.1.0"
@@ -42,12 +43,21 @@
 
 # Step implementations
 from sagemaker.mlops.workflow.automl_step import AutoMLStep
+from sagemaker.mlops.workflow.bedrock_steps import (
+    BedrockCustomModelStep,
+    BedrockCustomModelDeploymentStep,
+    BedrockModelImportStep,
+    BedrockProvisionedModelThroughputStep,
+)
 from sagemaker.mlops.workflow.callback_step import CallbackStep, CallbackOutput
 from sagemaker.mlops.workflow.clarify_check_step import ClarifyCheckStep
 from sagemaker.mlops.workflow.condition_step import ConditionStep
 from sagemaker.mlops.workflow.emr_step import EMRStep, EMRStepConfig
+from sagemaker.mlops.workflow.endpoint_step import EndpointConfigStep, EndpointStep
 from sagemaker.mlops.workflow.fail_step import FailStep
+from sagemaker.mlops.workflow.inference_component_step import InferenceComponentStep
 from sagemaker.mlops.workflow.lambda_step import LambdaStep, LambdaOutput
+from sagemaker.mlops.workflow.lineage_step import LineageStep
 from sagemaker.mlops.workflow.model_step import ModelStep
 from sagemaker.mlops.workflow.monitor_batch_transform_step import MonitorBatchTransformStep
 from sagemaker.mlops.workflow.notebook_job_step import NotebookJobStep
@@ -92,15 +102,23 @@
     "TuningStep",
     # Step implementations
     "AutoMLStep",
+    "BedrockCustomModelStep",
+    "BedrockCustomModelDeploymentStep",
+    "BedrockModelImportStep",
+    "BedrockProvisionedModelThroughputStep",
     "CallbackStep",
     "CallbackOutput",
     "ClarifyCheckStep",
     "ConditionStep",
     "EMRStep",
     "EMRStepConfig",
+    "EndpointConfigStep",
+    "EndpointStep",
     "FailStep",
+    "InferenceComponentStep",
     "LambdaStep",
     "LambdaOutput",
+    "LineageStep",
     "ModelStep",
     "MonitorBatchTransformStep",
     "NotebookJobStep",

sagemaker-mlops/src/sagemaker/mlops/workflow/_argument_validation.py

@@ -0,0 +1,134 @@
+# 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.
+"""Client-side validation for pipeline step ``arguments`` blocks.
+
+Validates the **top-level keys** of a step's ``arguments`` dict against
+the corresponding public AWS API input shape from botocore, and rejects
+fields that SageMaker Pipelines is known not to support. This fails fast
+at step construction with a clear error, instead of a server-side parse
+failure at ``CreatePipeline`` time.
+
+Values are intentionally not validated: they may be pipeline variables
+(parameter references, step property references, ``Join``/``JsonGet``
+expressions) that only resolve at pipeline compile or execution time.
+
+If the installed botocore release does not know the target operation
+(for example, a very old botocore without newer Bedrock APIs), shape
+validation is skipped and the service remains the authority.
+"""
+
+from __future__ import absolute_import
+
+import logging
+from typing import Any, Dict, FrozenSet, Optional, Sequence, Tuple
+
+import botocore.session
+from botocore.exceptions import UnknownServiceError
+from botocore.model import OperationNotFoundError
+
+logger = logging.getLogger(__name__)
+
+# Cache of (service, operation, pascal_case) -> allowed top-level keys.
+# ``None`` means botocore does not know the operation; skip shape checks.
+_SHAPE_CACHE: Dict[Tuple[str, str, bool], Optional[FrozenSet[str]]] = {}
+
+
+def _allowed_top_level_keys(
+    service_name: str, operation_name: str, pascal_case: bool
+) -> Optional[FrozenSet[str]]:
+    """Return the allowed top-level keys for an operation input shape.
+
+    Args:
+        service_name (str): botocore service name (e.g. ``sagemaker``).
+        operation_name (str): operation name (e.g. ``CreateEndpointConfig``).
+        pascal_case (bool): If True, convert member names to PascalCase
+            (used for Bedrock, whose JSON API members are camelCase but
+            whose pipeline ``Arguments`` fields are PascalCase).
+
+    Returns:
+        The allowed key set, or ``None`` if the installed botocore does
+        not know the operation (validation should then be skipped).
+    """
+    cache_key = (service_name, operation_name, pascal_case)
+    if cache_key not in _SHAPE_CACHE:
+        try:
+            session = botocore.session.get_session()
+            service_model = session.get_service_model(service_name)
+            operation_model = service_model.operation_model(operation_name)
+            members = operation_model.input_shape.members.keys()
+            if pascal_case:
+                members = [m[0].upper() + m[1:] for m in members]
+            _SHAPE_CACHE[cache_key] = frozenset(members)
+        except (UnknownServiceError, OperationNotFoundError):
+            logger.warning(
+                "Installed botocore does not know %s.%s; skipping "
+                "client-side argument shape validation for this step.",
+                service_name,
+                operation_name,
+            )
+            _SHAPE_CACHE[cache_key] = None
+    return _SHAPE_CACHE[cache_key]
+
+
+def validate_step_arguments(
+    step_class_name: str,
+    arguments: Dict[str, Any],
+    service_name: str,
+    operation_name: str,
+    unsupported_fields: Sequence[str] = (),
+    pascal_case: bool = False,
+) -> None:
+    """Validate the top-level keys of a step ``arguments`` dict.
+
+    Args:
+        step_class_name (str): Step class name, used in error messages.
+        arguments (Dict[str, Any]): The user-provided ``arguments`` dict.
+        service_name (str): botocore service name of the wrapped API.
+        operation_name (str): Operation whose input shape defines the
+            allowed top-level fields.
+        unsupported_fields (Sequence[str]): Fields that exist in the
+            public API shape but are rejected by SageMaker Pipelines.
+        pascal_case (bool): Convert botocore member names to PascalCase
+            before comparison (Bedrock APIs).
+
+    Raises:
+        ValueError: If ``arguments`` is not a non-empty dict with string
+            keys, contains an unsupported field, or contains a key that
+            is not part of the operation's input shape.
+    """
+    if arguments is None:
+        raise ValueError(f"arguments is required for {step_class_name}.")
+    if not isinstance(arguments, dict) or not arguments:
+        raise ValueError(f"{step_class_name}: arguments must be a non-empty dict.")
+    non_string_keys = [key for key in arguments if not isinstance(key, str)]
+    if non_string_keys:
+        raise ValueError(
+            f"{step_class_name}: argument keys must be strings; got {non_string_keys!r}."
+        )
+    rejected = sorted(field for field in unsupported_fields if field in arguments)
+    if rejected:
+        raise ValueError(
+            f"{step_class_name}: field(s) {rejected} are not supported by "
+            "SageMaker Pipelines and would be rejected at pipeline creation "
+            "time. Remove them from arguments."
+        )
+    allowed = _allowed_top_level_keys(service_name, operation_name, pascal_case)
+    if allowed is None:
+        return
+    unknown = sorted(set(arguments) - allowed)
+    if unknown:
+        raise ValueError(
+            f"{step_class_name}: unknown argument field(s) {unknown}. "
+            f"Allowed top-level fields (from {service_name}.{operation_name}): "
+            f"{sorted(allowed)}."
+        )