Change: Use the output path to store the Clarify config file

src/sagemaker/clarify.py

@@ -21,7 +21,7 @@
 import tempfile
 
 from sagemaker.processing import ProcessingInput, ProcessingOutput, Processor
-from sagemaker import image_uris, utils
+from sagemaker import image_uris, s3, utils
 
 
 class DataConfig:
@@ -405,9 +405,15 @@ def _run(
             analysis_config_file = os.path.join(tmpdirname, "analysis_config.json")
             with open(analysis_config_file, "w") as f:
                 json.dump(analysis_config, f)
+            s3_analysis_config_file = _upload_analysis_config(
+                analysis_config_file,
+                data_config.s3_output_path,
+                self.sagemaker_session,
+                kms_key,
+            )
             config_input = ProcessingInput(
                 input_name="analysis_config",
-                source=analysis_config_file,
+                source=s3_analysis_config_file,
                 destination=self._CLARIFY_CONFIG_INPUT,
                 s3_data_type="S3Prefix",
                 s3_input_mode="File",
@@ -638,6 +644,30 @@ def run_explainability(
         self._run(data_config, analysis_config, wait, logs, job_name, kms_key)
 
 
+def _upload_analysis_config(analysis_config_file, s3_output_path, sagemaker_session, kms_key):
+    """Uploads the local analysis_config_file to the s3_output_path.
+
+    Args:
+        analysis_config_file (str): File path to the local analysis config file.
+        s3_output_path (str): S3 prefix to store the analysis config file.
+        sagemaker_session (:class:`~sagemaker.session.Session`):
+            Session object which manages interactions with Amazon SageMaker and
+            any other AWS services needed. If not specified, the processor creates
+            one using the default AWS configuration chain.
+        kms_key (str): The ARN of the KMS key that is used to encrypt the
+            user code file (default: None).
+
+    Returns:
+        The S3 uri of the uploaded file.
+    """
+    return s3.S3Uploader.upload(
+        local_path=analysis_config_file,
+        desired_s3_uri=s3_output_path,
+        sagemaker_session=sagemaker_session,
+        kms_key=kms_key,
+    )
+
+
 def _set(value, key, dictionary):
     """Sets dictionary[key] = value if value is not None."""
     if value is not None:

tests/integ/test_clarify.py

@@ -112,10 +112,11 @@ def clarify_processor(sagemaker_session, cpu_instance_type):
     return processor
 
 
-@pytest.fixture(scope="module")
+@pytest.fixture
 def data_config(sagemaker_session, data_path, headers):
-    output_path = "s3://{}/{}".format(
-        sagemaker_session.default_bucket(), "linear_learner_analysis_result"
+    test_run = utils.unique_name_from_base("test_run")
+    output_path = "s3://{}/{}/{}".format(
+        sagemaker_session.default_bucket(), "linear_learner_analysis_result", test_run
     )
     return DataConfig(
         s3_data_input_path=data_path,
@@ -195,6 +196,7 @@ def test_pre_training_bias(clarify_processor, data_config, data_bias_config, sag
             )
             <= 1.0
         )
+        check_analysis_config(data_config, sagemaker_session, "pre_training_bias")
 
 
 def test_post_training_bias(
@@ -227,6 +229,7 @@ def test_post_training_bias(
             )
             <= 1.0
         )
+        check_analysis_config(data_config, sagemaker_session, "post_training_bias")
 
 
 def test_shap(clarify_processor, data_config, model_config, shap_config, sagemaker_session):
@@ -250,3 +253,13 @@ def test_shap(clarify_processor, data_config, model_config, shap_config, sagemak
             )
             <= 1
         )
+        check_analysis_config(data_config, sagemaker_session, "shap")
+
+
+def check_analysis_config(data_config, sagemaker_session, method):
+    analysis_config_json = s3.S3Downloader.read_file(
+        data_config.s3_output_path + "/analysis_config.json",
+        sagemaker_session,
+    )
+    analysis_config = json.loads(analysis_config_json)
+    assert method in analysis_config["methods"]