Add telemetry support for mlflow models#4674

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Merged
mufaddal-rohawala merged 4 commits into from
May 13, 2024
Merged

Add telemetry support for mlflow models #4674

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8f50183
Initial commit for telemetry support
jiapinw May 10, 2024
e9f5181
Fix style issues and add more logger messages
jiapinw May 10, 2024
e7d803e
fix value error messages in ut
jiapinw May 10, 2024
4938ab5
Merge branch 'master' into aloy-telemetry-support
jiapinw May 12, 2024
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3 changes: 3 additions & 0 deletions src/sagemaker/serve/builder/model_builder.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)
Expand Down
6 changes: 3 additions & 3 deletions src/sagemaker/serve/model_format/mlflow/constants.py
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Expand Up@@ -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"
Expand Down
11 changes: 10 additions & 1 deletion src/sagemaker/serve/model_format/mlflow/utils.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)


Expand DownExpand Up@@ -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
Expand Down
4 changes: 2 additions & 2 deletions src/sagemaker/serve/utils/lineage_utils.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
)
Expand DownExpand Up@@ -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):
Expand Down
22 changes: 22 additions & 0 deletions src/sagemaker/serve/utils/telemetry_logger.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand DownExpand Up@@ -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"""
Expand DownExpand Up@@ -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)
Expand Down
55 changes: 55 additions & 0 deletions tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")

Expand Down
36 changes: 36 additions & 0 deletions tests/unit/sagemaker/serve/utils/test_telemetry_logger.py
View file
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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand All@@ -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:
Expand DownExpand Up@@ -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
)
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Add telemetry support for mlflow models#4674

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 Merged

mufaddal-rohawala
merged 4 commits into

from

May 13, 2024
 Merged


Add telemetry support for mlflow models
#4674
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8f50183

Initial commit for telemetry support


jiapinw May 10, 2024
e9f5181

Fix style issues and add more logger messages


jiapinw May 10, 2024
e7d803e

fix value error messages in ut


jiapinw May 10, 2024
4938ab5

Merge branch 'master' into aloy-telemetry-support


jiapinw May 12, 2024

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3 changes: 3 additions & 0 deletions
src/sagemaker/serve/builder/model_builder.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)
Expand Down

6 changes: 3 additions & 3 deletions
src/sagemaker/serve/model_format/mlflow/constants.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand Down

11 changes: 10 additions & 1 deletion
src/sagemaker/serve/model_format/mlflow/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)


Expand DownExpand Up@@ -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
Expand Down

4 changes: 2 additions & 2 deletions
src/sagemaker/serve/utils/lineage_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
)
Expand DownExpand Up@@ -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):
Expand Down

22 changes: 22 additions & 0 deletions
src/sagemaker/serve/utils/telemetry_logger.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand DownExpand Up@@ -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"""
Expand DownExpand Up@@ -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)
Expand Down

55 changes: 55 additions & 0 deletions
tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")

Expand Down

36 changes: 36 additions & 0 deletions
tests/unit/sagemaker/serve/utils/test_telemetry_logger.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand All@@ -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:
Expand DownExpand Up@@ -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
 )

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Add telemetry support for mlflow models#4674

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 Merged

mufaddal-rohawala
merged 4 commits into

from

May 13, 2024
 Merged


Add telemetry support for mlflow models
#4674
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8f50183

Initial commit for telemetry support


jiapinw May 10, 2024
e9f5181

Fix style issues and add more logger messages


jiapinw May 10, 2024
e7d803e

fix value error messages in ut


jiapinw May 10, 2024
4938ab5

Merge branch 'master' into aloy-telemetry-support


jiapinw May 12, 2024

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3 changes: 3 additions & 0 deletions
src/sagemaker/serve/builder/model_builder.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)
Expand Down

6 changes: 3 additions & 3 deletions
src/sagemaker/serve/model_format/mlflow/constants.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand Down

11 changes: 10 additions & 1 deletion
src/sagemaker/serve/model_format/mlflow/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)


Expand DownExpand Up@@ -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
Expand Down

4 changes: 2 additions & 2 deletions
src/sagemaker/serve/utils/lineage_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
)
Expand DownExpand Up@@ -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):
Expand Down

22 changes: 22 additions & 0 deletions
src/sagemaker/serve/utils/telemetry_logger.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand DownExpand Up@@ -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"""
Expand DownExpand Up@@ -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)
Expand Down

55 changes: 55 additions & 0 deletions
tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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")

Expand Down

36 changes: 36 additions & 0 deletions
tests/unit/sagemaker/serve/utils/test_telemetry_logger.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand All@@ -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:
Expand DownExpand Up@@ -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
 )

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Add telemetry support for mlflow models#4674

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 Merged

mufaddal-rohawala
merged 4 commits into

from

May 13, 2024
 Merged


Add telemetry support for mlflow models
#4674
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8f50183

Initial commit for telemetry support


jiapinw May 10, 2024
e9f5181

Fix style issues and add more logger messages


jiapinw May 10, 2024
e7d803e

fix value error messages in ut


jiapinw May 10, 2024
4938ab5

Merge branch 'master' into aloy-telemetry-support


jiapinw May 12, 2024

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3 changes: 3 additions & 0 deletions
src/sagemaker/serve/builder/model_builder.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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)
Expand Down

6 changes: 3 additions & 3 deletions
src/sagemaker/serve/model_format/mlflow/constants.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand Down

11 changes: 10 additions & 1 deletion
src/sagemaker/serve/model_format/mlflow/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)


Expand DownExpand Up@@ -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
Expand Down

4 changes: 2 additions & 2 deletions
src/sagemaker/serve/utils/lineage_utils.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
)
Expand DownExpand Up@@ -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):
Expand Down

22 changes: 22 additions & 0 deletions
src/sagemaker/serve/utils/telemetry_logger.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand DownExpand Up@@ -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"""
Expand DownExpand Up@@ -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)
Expand Down

55 changes: 55 additions & 0 deletions
tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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")

Expand Down

36 changes: 36 additions & 0 deletions
tests/unit/sagemaker/serve/utils/test_telemetry_logger.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand All@@ -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:
Expand DownExpand Up@@ -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
 )

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Add telemetry support for mlflow models#4674

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 Merged

mufaddal-rohawala
merged 4 commits into

from

May 13, 2024
 Merged


Add telemetry support for mlflow models
#4674
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8f50183

Initial commit for telemetry support


jiapinw May 10, 2024
e9f5181

Fix style issues and add more logger messages


jiapinw May 10, 2024
e7d803e

fix value error messages in ut


jiapinw May 10, 2024
4938ab5

Merge branch 'master' into aloy-telemetry-support


jiapinw May 12, 2024

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3 changes: 3 additions & 0 deletions
src/sagemaker/serve/builder/model_builder.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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)
Expand Down

6 changes: 3 additions & 3 deletions
src/sagemaker/serve/model_format/mlflow/constants.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand Down

11 changes: 10 additions & 1 deletion
src/sagemaker/serve/model_format/mlflow/utils.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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)


Expand DownExpand Up@@ -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
Expand Down

4 changes: 2 additions & 2 deletions
src/sagemaker/serve/utils/lineage_utils.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
)
Expand DownExpand Up@@ -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):
Expand Down

22 changes: 22 additions & 0 deletions
src/sagemaker/serve/utils/telemetry_logger.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand DownExpand Up@@ -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"""
Expand DownExpand Up@@ -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)
Expand Down

55 changes: 55 additions & 0 deletions
tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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")

Expand Down

36 changes: 36 additions & 0 deletions
tests/unit/sagemaker/serve/utils/test_telemetry_logger.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand All@@ -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:
Expand DownExpand Up@@ -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
 )

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Add telemetry support for mlflow models#4674

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 Merged

mufaddal-rohawala
merged 4 commits into

from

May 13, 2024
 Merged


Add telemetry support for mlflow models
#4674
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8f50183

Initial commit for telemetry support


jiapinw May 10, 2024
e9f5181

Fix style issues and add more logger messages


jiapinw May 10, 2024
e7d803e

fix value error messages in ut


jiapinw May 10, 2024
4938ab5

Merge branch 'master' into aloy-telemetry-support


jiapinw May 12, 2024

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3 changes: 3 additions & 0 deletions
src/sagemaker/serve/builder/model_builder.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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)
Expand Down

6 changes: 3 additions & 3 deletions
src/sagemaker/serve/model_format/mlflow/constants.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand Down

11 changes: 10 additions & 1 deletion
src/sagemaker/serve/model_format/mlflow/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)


Expand DownExpand Up@@ -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
Expand Down

4 changes: 2 additions & 2 deletions
src/sagemaker/serve/utils/lineage_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
)
Expand DownExpand Up@@ -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):
Expand Down

22 changes: 22 additions & 0 deletions
src/sagemaker/serve/utils/telemetry_logger.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand DownExpand Up@@ -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"""
Expand DownExpand Up@@ -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)
Expand Down

55 changes: 55 additions & 0 deletions
tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")

Expand Down

36 changes: 36 additions & 0 deletions
tests/unit/sagemaker/serve/utils/test_telemetry_logger.py

View file


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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand All@@ -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:
Expand DownExpand Up@@ -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
 )

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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 Merged

mufaddal-rohawala
merged 4 commits into

from

May 13, 2024
 Merged


Add telemetry support for mlflow models
#4674
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8f50183

Initial commit for telemetry support


jiapinw May 10, 2024
e9f5181

Fix style issues and add more logger messages


jiapinw May 10, 2024
e7d803e

fix value error messages in ut


jiapinw May 10, 2024
4938ab5

Merge branch 'master' into aloy-telemetry-support


jiapinw May 12, 2024

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3 changes: 3 additions & 0 deletions
src/sagemaker/serve/builder/model_builder.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)
Expand Down

6 changes: 3 additions & 3 deletions
src/sagemaker/serve/model_format/mlflow/constants.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand Down

11 changes: 10 additions & 1 deletion
src/sagemaker/serve/model_format/mlflow/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)


Expand DownExpand Up@@ -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
Expand Down

4 changes: 2 additions & 2 deletions
src/sagemaker/serve/utils/lineage_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
)
Expand DownExpand Up@@ -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):
Expand Down

22 changes: 22 additions & 0 deletions
src/sagemaker/serve/utils/telemetry_logger.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand DownExpand Up@@ -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"""
Expand DownExpand Up@@ -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)
Expand Down

55 changes: 55 additions & 0 deletions
tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")

Expand Down

36 changes: 36 additions & 0 deletions
tests/unit/sagemaker/serve/utils/test_telemetry_logger.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand All@@ -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:
Expand DownExpand Up@@ -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
 )

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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 Merged

mufaddal-rohawala
merged 4 commits into

from

May 13, 2024
 Merged


Add telemetry support for mlflow models
#4674
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8f50183

Initial commit for telemetry support


jiapinw May 10, 2024
e9f5181

Fix style issues and add more logger messages


jiapinw May 10, 2024
e7d803e

fix value error messages in ut


jiapinw May 10, 2024
4938ab5

Merge branch 'master' into aloy-telemetry-support


jiapinw May 12, 2024

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3 changes: 3 additions & 0 deletions
src/sagemaker/serve/builder/model_builder.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)
Expand Down

6 changes: 3 additions & 3 deletions
src/sagemaker/serve/model_format/mlflow/constants.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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"
Expand Down

11 changes: 10 additions & 1 deletion
src/sagemaker/serve/model_format/mlflow/utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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)


Expand DownExpand Up@@ -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
Expand Down

4 changes: 2 additions & 2 deletions
src/sagemaker/serve/utils/lineage_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
)
Expand DownExpand Up@@ -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):
Expand Down

22 changes: 22 additions & 0 deletions
src/sagemaker/serve/utils/telemetry_logger.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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
Expand DownExpand Up@@ -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"""
Expand DownExpand Up@@ -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)
Expand Down

55 changes: 55 additions & 0 deletions
tests/unit/sagemaker/serve/model_format/mlflow/test_mlflow_utils.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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")

Expand Down

36 changes: 36 additions & 0 deletions
tests/unit/sagemaker/serve/utils/test_telemetry_logger.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -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,
Expand All@@ -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:
Expand DownExpand Up@@ -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
 )