feature: support endpoint_name_prefix, seed and version for Clarify (#2421)

* feat: support endpoint_name_prefix and seed

* feat: support version

* fix: fix doc string

* fix: change default version to None

* fix: pass parameter

Co-authored-by: icywang86rui <[email protected]>

Author: xiaoyi-cheng

src/sagemaker/clarify.py

@@ -19,7 +19,7 @@
 import json
 import os
 import tempfile
-
+import re
 from sagemaker.processing import ProcessingInput, ProcessingOutput, Processor
 from sagemaker import image_uris, s3, utils
 
@@ -124,8 +124,9 @@ def __init__(
         content_template=None,
         custom_attributes=None,
         accelerator_type=None,
+        endpoint_name_prefix=None,
     ):
-        """Initializes a configuration of a model and the endpoint to be created for it.
+        r"""Initializes a configuration of a model and the endpoint to be created for it.
 
         Args:
             model_name (str): Model name (as created by 'CreateModel').
@@ -155,12 +156,21 @@ def __init__(
             accelerator_type (str): The Elastic Inference accelerator type to deploy to the model
                 endpoint instance for making inferences to the model, see
                 https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html.
+            endpoint_name_prefix (str): The endpoint name prefix of a new endpoint. Must follow
+                pattern "^[a-zA-Z0-9](-\*[a-zA-Z0-9]".
         """
         self.predictor_config = {
             "model_name": model_name,
             "instance_type": instance_type,
             "initial_instance_count": instance_count,
         }
+        if endpoint_name_prefix is not None:
+            if re.search("^[a-zA-Z0-9](-*[a-zA-Z0-9])", endpoint_name_prefix) is None:
+                raise ValueError(
+                    "Invalid endpoint_name_prefix."
+                    " Please follow pattern ^[a-zA-Z0-9](-*[a-zA-Z0-9])."
+                )
+            self.predictor_config["endpoint_name_prefix"] = endpoint_name_prefix
         if accept_type is not None:
             if accept_type not in ["text/csv", "application/jsonlines"]:
                 raise ValueError(
@@ -277,6 +287,7 @@ def __init__(
         agg_method,
         use_logit=False,
         save_local_shap_values=True,
+        seed=None,
     ):
         """Initializes config for SHAP.
 
@@ -297,6 +308,7 @@ def __init__(
                 have log-odds units.
             save_local_shap_values (bool): Indicator of whether to save the local SHAP values
                 in the output location. Default is True.
+            seed (int): seed value to get deterministic SHAP values. Default is None.
         """
         if agg_method not in ["mean_abs", "median", "mean_sq"]:
             raise ValueError(
@@ -310,6 +322,8 @@ def __init__(
             "use_logit": use_logit,
             "save_local_shap_values": save_local_shap_values,
         }
+        if seed is not None:
+            self.shap_config["seed"] = seed
 
     def get_explainability_config(self):
         """Returns config."""
@@ -336,6 +350,7 @@ def __init__(
         env=None,
         tags=None,
         network_config=None,
+        version=None,
     ):
         """Initializes a ``Processor`` instance, computing bias metrics and model explanations.
 
@@ -369,8 +384,9 @@ def __init__(
                 A :class:`~sagemaker.network.NetworkConfig`
                 object that configures network isolation, encryption of
                 inter-container traffic, security group IDs, and subnets.
+            version (str): Clarify version want to be used.
         """
-        container_uri = image_uris.retrieve("clarify", sagemaker_session.boto_region_name)
+        container_uri = image_uris.retrieve("clarify", sagemaker_session.boto_region_name, version)
         super(SageMakerClarifyProcessor, self).__init__(
             role,
             container_uri,

tests/integ/test_clarify.py

@@ -144,6 +144,7 @@ def model_config(model_name):
         instance_type="ml.c5.xlarge",
         instance_count=1,
         accept_type="application/jsonlines",
+        endpoint_name_prefix="myprefix",
     )
 
 
@@ -172,6 +173,7 @@ def shap_config():
         ],
         num_samples=2,
         agg_method="mean_sq",
+        seed=123,
     )
 
 

tests/unit/test_clarify.py

@@ -128,6 +128,21 @@ def test_invalid_model_config():
     )
 
 
+def test_invalid_model_config_with_bad_endpoint_name_prefix():
+    with pytest.raises(ValueError) as error:
+        ModelConfig(
+            model_name="xgboost-model",
+            instance_type="ml.c5.xlarge",
+            instance_count=1,
+            accept_type="invalid_accept_type",
+            endpoint_name_prefix="~invalid_endpoint_prefix",
+        )
+    assert (
+        "Invalid endpoint_name_prefix. Please follow pattern ^[a-zA-Z0-9](-*[a-zA-Z0-9])."
+        in str(error.value)
+    )
+
+
 def test_model_predicted_label_config():
     label = "label"
     probability = "pr"
@@ -171,11 +186,13 @@ def test_shap_config():
     num_samples = 100
     agg_method = "mean_sq"
     use_logit = True
+    seed = 123
     shap_config = SHAPConfig(
         baseline=baseline,
         num_samples=num_samples,
         agg_method=agg_method,
         use_logit=use_logit,
+        seed=seed,
     )
     expected_config = {
         "shap": {
@@ -184,6 +201,7 @@ def test_shap_config():
             "agg_method": agg_method,
             "use_logit": use_logit,
             "save_local_shap_values": True,
+            "seed": seed,
         }
     }
     assert expected_config == shap_config.get_explainability_config()