Add telemetry support for mlflow models

src/sagemaker/serve/builder/model_builder.py

@@ -669,6 +669,9 @@ def _initialize_for_mlflow(self) -> None:
         mlflow_path = self.model_metadata.get(MLFLOW_MODEL_PATH)
         if not _mlflow_input_is_local_path(mlflow_path):
             # TODO: extend to package arn, run id and etc.
+            logger.info(
+                "Start downloading model artifacts from %s to %s", mlflow_path, self.model_path
+            )
             _download_s3_artifacts(mlflow_path, self.model_path, self.sagemaker_session)
         else:
             _copy_directory_contents(mlflow_path, self.model_path)

src/sagemaker/serve/model_format/mlflow/constants.py

@@ -19,12 +19,12 @@
     "py39": "1.13.1",
     "py310": "2.2.0",
 }
-MODEL_PACAKGE_ARN_REGEX = (
-    r"^arn:aws:sagemaker:[a-z0-9\-]+:[0-9]{12}:model-package\/[" r"a-zA-Z0-9\-_\/\.]+$"
+MODEL_PACKAGE_ARN_REGEX = (
+    r"^arn:aws:sagemaker:[a-z0-9\-]+:[0-9]{12}:model-package\/(.*?)(?:/(\d+))?$"
 )
 MLFLOW_RUN_ID_REGEX = r"^runs:/[a-zA-Z0-9]+(/[a-zA-Z0-9]+)*$"
 MLFLOW_REGISTRY_PATH_REGEX = r"^models:/[a-zA-Z0-9\-_\.]+(/[0-9]+)*$"
-S3_PATH_REGEX = r"^s3:\/\/[a-zA-Z0-9\-_\.]+\/[a-zA-Z0-9\-_\/\.]*$"
+S3_PATH_REGEX = r"^s3:\/\/[a-zA-Z0-9\-_\.]+(?:\/[a-zA-Z0-9\-_\/\.]*)?$"
 MLFLOW_MODEL_PATH = "MLFLOW_MODEL_PATH"
 MLFLOW_METADATA_FILE = "MLmodel"
 MLFLOW_PIP_DEPENDENCY_FILE = "requirements.txt"

src/sagemaker/serve/model_format/mlflow/utils.py

@@ -278,7 +278,7 @@ def _download_s3_artifacts(s3_path: str, dst_path: str, session: Session) -> Non
                 os.makedirs(local_file_dir, exist_ok=True)
 
                 # Download the file
-                print(f"Downloading {key} to {local_file_path}")
+                logger.info(f"Downloading {key} to {local_file_path}")
                 s3.download_file(s3_bucket, key, local_file_path)
 
 
@@ -356,6 +356,15 @@ def _select_container_for_mlflow_model(
     logger.info("Auto-detected framework to use is %s", framework_to_use)
     logger.info("Auto-detected framework version is %s", framework_version)
 
+    if framework_version is None:
+        raise ValueError(
+            (
+                "Unable to auto detect framework version. Please provide framework %s as part of the "
+                "requirements.txt file for deployment flavor %s"
+            )
+            % (framework_to_use, deployment_flavor)
+        )
+
     casted_versions = (
         _cast_to_compatible_version(framework_to_use, framework_version)
         if framework_version

src/sagemaker/serve/utils/lineage_utils.py

@@ -28,7 +28,7 @@
 from sagemaker.lineage.query import LineageSourceEnum
 from sagemaker.serve.model_format.mlflow.constants import (
     MLFLOW_RUN_ID_REGEX,
-    MODEL_PACAKGE_ARN_REGEX,
+    MODEL_PACKAGE_ARN_REGEX,
     S3_PATH_REGEX,
     MLFLOW_REGISTRY_PATH_REGEX,
 )
@@ -107,7 +107,7 @@ def _get_mlflow_model_path_type(mlflow_model_path: str) -> str:
     """
     mlflow_rub_id_pattern = MLFLOW_RUN_ID_REGEX
     mlflow_registry_id_pattern = MLFLOW_REGISTRY_PATH_REGEX
-    sagemaker_arn_pattern = MODEL_PACAKGE_ARN_REGEX
+    sagemaker_arn_pattern = MODEL_PACKAGE_ARN_REGEX
     s3_pattern = S3_PATH_REGEX
 
     if re.match(mlflow_rub_id_pattern, mlflow_model_path):

src/sagemaker/serve/utils/telemetry_logger.py

@@ -19,7 +19,16 @@
 
 from sagemaker import Session, exceptions
 from sagemaker.serve.mode.function_pointers import Mode
+from sagemaker.serve.model_format.mlflow.constants import MLFLOW_MODEL_PATH
 from sagemaker.serve.utils.exceptions import ModelBuilderException
+from sagemaker.serve.utils.lineage_constants import (
+    MLFLOW_LOCAL_PATH,
+    MLFLOW_S3_PATH,
+    MLFLOW_MODEL_PACKAGE_PATH,
+    MLFLOW_RUN_ID,
+    MLFLOW_REGISTRY_PATH,
+)
+from sagemaker.serve.utils.lineage_utils import _get_mlflow_model_path_type
 from sagemaker.serve.utils.types import ModelServer, ImageUriOption
 from sagemaker.serve.validations.check_image_uri import is_1p_image_uri
 from sagemaker.user_agent import SDK_VERSION
@@ -51,6 +60,14 @@
     str(ModelServer.TGI): 6,
 }
 
+MLFLOW_MODEL_PATH_CODE = {
+    MLFLOW_LOCAL_PATH: 1,
+    MLFLOW_S3_PATH: 2,
+    MLFLOW_MODEL_PACKAGE_PATH: 3,
+    MLFLOW_RUN_ID: 4,
+    MLFLOW_REGISTRY_PATH: 5,
+}
+
 
 def _capture_telemetry(func_name: str):
     """Placeholder docstring"""
@@ -78,6 +95,11 @@ def wrapper(self, *args, **kwargs):
             if self.sagemaker_session and self.sagemaker_session.endpoint_arn:
                 extra += f"&x-endpointArn={self.sagemaker_session.endpoint_arn}"
 
+            if getattr(self, "_is_mlflow_model", False):
+                mlflow_model_path = self.model_metadata[MLFLOW_MODEL_PATH]
+                mlflow_model_path_type = _get_mlflow_model_path_type(mlflow_model_path)
+                extra += f"&x-mlflowModelPathType={MLFLOW_MODEL_PATH_CODE[mlflow_model_path_type]}"
+
             start_timer = perf_counter()
             try:
                 response = func(self, *args, **kwargs)

tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py

@@ -418,6 +418,61 @@ def test_select_container_for_mlflow_model_no_dlc_detected(
         )
 
 
+@patch("sagemaker.image_uris.retrieve")
+@patch("sagemaker.serve.model_format.mlflow.utils._cast_to_compatible_version")
+@patch("sagemaker.serve.model_format.mlflow.utils._get_framework_version_from_requirements")
+@patch(
+    "sagemaker.serve.model_format.mlflow.utils._get_python_version_from_parsed_mlflow_model_file"
+)
+@patch("sagemaker.serve.model_format.mlflow.utils._get_all_flavor_metadata")
+@patch("sagemaker.serve.model_format.mlflow.utils._generate_mlflow_artifact_path")
+def test_select_container_for_mlflow_model_no_framework_version_detected(
+    mock_generate_mlflow_artifact_path,
+    mock_get_all_flavor_metadata,
+    mock_get_python_version_from_parsed_mlflow_model_file,
+    mock_get_framework_version_from_requirements,
+    mock_cast_to_compatible_version,
+    mock_image_uris_retrieve,
+):
+    mlflow_model_src_path = "/path/to/mlflow_model"
+    deployment_flavor = "pytorch"
+    region = "us-west-2"
+    instance_type = "ml.m5.xlarge"
+
+    mock_requirements_path = "/path/to/requirements.txt"
+    mock_metadata_path = "/path/to/mlmodel"
+    mock_flavor_metadata = {"pytorch": {"some_key": "some_value"}}
+    mock_python_version = "3.8.6"
+
+    mock_generate_mlflow_artifact_path.side_effect = lambda path, artifact: (
+        mock_requirements_path if artifact == "requirements.txt" else mock_metadata_path
+    )
+    mock_get_all_flavor_metadata.return_value = mock_flavor_metadata
+    mock_get_python_version_from_parsed_mlflow_model_file.return_value = mock_python_version
+    mock_get_framework_version_from_requirements.return_value = None
+
+    with pytest.raises(
+        ValueError,
+        match="Unable to auto detect framework version. Please provide framework "
+        "pytorch as part of the requirements.txt file for deployment flavor "
+        "pytorch",
+    ):
+        _select_container_for_mlflow_model(
+            mlflow_model_src_path, deployment_flavor, region, instance_type
+        )
+
+        mock_generate_mlflow_artifact_path.assert_any_call(
+            mlflow_model_src_path, "requirements.txt"
+        )
+        mock_generate_mlflow_artifact_path.assert_any_call(mlflow_model_src_path, "MLmodel")
+        mock_get_all_flavor_metadata.assert_called_once_with(mock_metadata_path)
+        mock_get_framework_version_from_requirements.assert_called_once_with(
+            deployment_flavor, mock_requirements_path
+        )
+        mock_cast_to_compatible_version.assert_not_called()
+        mock_image_uris_retrieve.assert_not_called()
+
+
 def test_validate_input_for_mlflow():
     _validate_input_for_mlflow(ModelServer.TORCHSERVE, "pytorch")
 

tests/unit/sagemaker/serve/utils/test_telemetry_logger.py

@@ -14,6 +14,7 @@
 import unittest
 from unittest.mock import Mock, patch
 from sagemaker.serve import Mode, ModelServer
+from sagemaker.serve.model_format.mlflow.constants import MLFLOW_MODEL_PATH
 from sagemaker.serve.utils.telemetry_logger import (
     _send_telemetry,
     _capture_telemetry,
@@ -32,9 +33,13 @@
     "763104351884.dkr.ecr.us-east-1.amazonaws.com/"
     "huggingface-pytorch-inference:2.0.0-transformers4.28.1-cpu-py310-ubuntu20.04"
 )
+MOCK_PYTORCH_CONTAINER = (
+    "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:2.0.1-cpu-py310"
+)
 MOCK_HUGGINGFACE_ID = "meta-llama/Llama-2-7b-hf"
 MOCK_EXCEPTION = LocalModelOutOfMemoryException("mock raise ex")
 MOCK_ENDPOINT_ARN = "arn:aws:sagemaker:us-west-2:123456789012:endpoint/test"
+MOCK_MODEL_METADATA_FOR_MLFLOW = {MLFLOW_MODEL_PATH: "s3://some_path"}
 
 
 class ModelBuilderMock:
@@ -239,3 +244,34 @@ def test_construct_url_with_failure_reason_and_extra_info(self):
             f"&x-extra={mock_extra_info}"
         )
         self.assertEquals(ret_url, expected_base_url)
+
+    @patch("sagemaker.serve.utils.telemetry_logger._send_telemetry")
+    def test_capture_telemetry_decorator_mlflow_success(self, mock_send_telemetry):
+        mock_model_builder = ModelBuilderMock()
+        mock_model_builder.serve_settings.telemetry_opt_out = False
+        mock_model_builder.image_uri = MOCK_PYTORCH_CONTAINER
+        mock_model_builder._is_mlflow_model = True
+        mock_model_builder.model_metadata = MOCK_MODEL_METADATA_FOR_MLFLOW
+        mock_model_builder._is_custom_image_uri = False
+        mock_model_builder.mode = Mode.SAGEMAKER_ENDPOINT
+        mock_model_builder.model_server = ModelServer.TORCHSERVE
+        mock_model_builder.sagemaker_session.endpoint_arn = MOCK_ENDPOINT_ARN
+
+        mock_model_builder.mock_deploy()
+
+        args = mock_send_telemetry.call_args.args
+        latency = str(args[5]).split("latency=")[1]
+        expected_extra_str = (
+            f"{MOCK_FUNC_NAME}"
+            "&x-modelServer=1"
+            "&x-imageTag=pytorch-inference:2.0.1-cpu-py310"
+            f"&x-sdkVersion={SDK_VERSION}"
+            f"&x-defaultImageUsage={ImageUriOption.DEFAULT_IMAGE.value}"
+            f"&x-endpointArn={MOCK_ENDPOINT_ARN}"
+            f"&x-mlflowModelPathType=2"
+            f"&x-latency={latency}"
+        )
+
+        mock_send_telemetry.assert_called_once_with(
+            "1", 3, MOCK_SESSION, None, None, expected_extra_str
+        )