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test_model_builder.py
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
from __future__ import absolute_import
from unittest.mock import MagicMock, patch, Mock, mock_open
import unittest
from pathlib import Path
from copy import deepcopy
from sagemaker.serve import SchemaBuilder
from sagemaker.serve.builder.model_builder import ModelBuilder
from sagemaker.serve.mode.function_pointers import Mode
from sagemaker.serve.model_format.mlflow.constants import MLFLOW_TRACKING_ARN
from sagemaker.serve.utils import task
from sagemaker.serve.utils.exceptions import TaskNotFoundException
from sagemaker.serve.utils.predictors import TensorflowServingLocalPredictor
from sagemaker.serve.utils.types import ModelServer
from tests.unit.sagemaker.serve.constants import MOCK_IMAGE_CONFIG, MOCK_VPC_CONFIG
schema_builder = MagicMock()
mock_inference_spec = Mock()
mock_fw_model = Mock()
module = "module"
class_name = "class_name"
mock_fw_model.__class__.__module__ = module
mock_fw_model.__class__.__name__ = class_name
MODEL_PATH = "test_path"
CODE_PATH = Path(MODEL_PATH + "/code")
ENV_VAR_PAIR = [("some key", "some value")]
model_data = MagicMock()
framework = "framework"
version = "version"
ENV_VARS = {"some key": "some value", "MODEL_CLASS_NAME": f"{module}.{class_name}"}
ENV_VARS_INF_SPEC = {"some key": "some value"}
INSTANCE_GPU_INFO = (2, 8)
mock_image_uri = "abcd/efghijk"
mock_1p_dlc_image_uri = "763104351884.dkr.ecr.us-east-1.amazonaws.com"
mock_role_arn = "arn:aws:iam::123456789012:role/SageMakerRole"
mock_s3_model_data_url = "sample s3 data url"
mock_secret_key = "mock_secret_key"
mock_instance_type = "mock instance type"
supported_model_servers = {
ModelServer.TORCHSERVE,
ModelServer.TRITON,
ModelServer.DJL_SERVING,
ModelServer.TENSORFLOW_SERVING,
ModelServer.MMS,
ModelServer.TGI,
ModelServer.TEI,
}
mock_session = MagicMock()
class TestModelBuilder(unittest.TestCase):
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
def test_validation_in_progress_mode_supported(self, mock_serveSettings):
builder = ModelBuilder(model_server=ModelServer.TORCHSERVE)
self.assertRaisesRegex(
Exception,
"IN_PROCESS mode is only supported for MMS/Transformers server in beta release.",
builder.build,
Mode.IN_PROCESS,
mock_role_arn,
mock_session,
)
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
def test_validation_cannot_set_both_model_and_inference_spec(self, mock_serveSettings):
builder = ModelBuilder(inference_spec="some value", model=Mock(spec=object))
self.assertRaisesRegex(
Exception,
"Can only set one of the following: model, inference_spec.",
builder.build,
Mode.SAGEMAKER_ENDPOINT,
mock_role_arn,
mock_session,
)
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
def test_validation_unsupported_model_server_type(self, mock_serveSettings):
builder = ModelBuilder(model_server="invalid_model_server")
self.assertRaisesRegex(
Exception,
"%s is not supported yet! Supported model servers: %s"
% (builder.model_server, supported_model_servers),
builder.build,
Mode.SAGEMAKER_ENDPOINT,
mock_role_arn,
mock_session,
)
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
def test_validation_model_server_not_set_with_image_uri(self, mock_serveSettings):
builder = ModelBuilder(image_uri="image_uri")
self.assertRaisesRegex(
Exception,
"Model_server must be set when non-first-party image_uri is set. "
+ "Supported model servers: %s" % supported_model_servers,
builder.build,
Mode.SAGEMAKER_ENDPOINT,
mock_role_arn,
mock_session,
)
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
def test_save_model_throw_exception_when_none_of_model_and_inference_spec_is_set(
self, mock_serveSettings
):
builder = ModelBuilder(inference_spec=None, model=None)
self.assertRaisesRegex(
Exception,
"Cannot detect required model or inference spec",
builder.build,
Mode.SAGEMAKER_ENDPOINT,
mock_role_arn,
mock_session,
)
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_djl")
def test_model_server_override_djl_with_model(self, mock_build_for_djl, mock_serve_settings):
mock_setting_object = mock_serve_settings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
builder = ModelBuilder(model_server=ModelServer.DJL_SERVING, model="gpt_llm_burt")
builder.build(sagemaker_session=mock_session)
mock_build_for_djl.assert_called_once()
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
def test_model_server_override_djl_without_model_or_mlflow(self, mock_serve_settings):
builder = ModelBuilder(
model_server=ModelServer.DJL_SERVING, model=None, inference_spec=None
)
self.assertRaisesRegex(
Exception,
"Missing required parameter `model` or 'ml_flow' path",
builder.build,
Mode.SAGEMAKER_ENDPOINT,
mock_role_arn,
mock_session,
)
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_torchserve")
def test_model_server_override_torchserve_with_model(
self, mock_build_for_ts, mock_serve_settings
):
mock_setting_object = mock_serve_settings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
builder = ModelBuilder(model_server=ModelServer.TORCHSERVE, model="gpt_llm_burt")
builder.build(sagemaker_session=mock_session)
mock_build_for_ts.assert_called_once()
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
def test_model_server_override_torchserve_without_model_or_mlflow(self, mock_serve_settings):
builder = ModelBuilder(model_server=ModelServer.TORCHSERVE)
self.assertRaisesRegex(
Exception,
"Missing required parameter `model` or 'ml_flow' path",
builder.build,
Mode.SAGEMAKER_ENDPOINT,
mock_role_arn,
mock_session,
)
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_triton")
def test_model_server_override_triton_with_model(self, mock_build_for_ts, mock_serve_settings):
mock_setting_object = mock_serve_settings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
builder = ModelBuilder(model_server=ModelServer.TRITON, model="gpt_llm_burt")
builder.build(sagemaker_session=mock_session)
mock_build_for_ts.assert_called_once()
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_tensorflow_serving")
def test_model_server_override_tensor_with_model(self, mock_build_for_ts, mock_serve_settings):
mock_setting_object = mock_serve_settings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
builder = ModelBuilder(model_server=ModelServer.TENSORFLOW_SERVING, model="gpt_llm_burt")
builder.build(sagemaker_session=mock_session)
mock_build_for_ts.assert_called_once()
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_tei")
def test_model_server_override_tei_with_model(self, mock_build_for_ts, mock_serve_settings):
mock_setting_object = mock_serve_settings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
builder = ModelBuilder(model_server=ModelServer.TEI, model="gpt_llm_burt")
builder.build(sagemaker_session=mock_session)
mock_build_for_ts.assert_called_once()
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_tgi")
def test_model_server_override_tgi_with_model(self, mock_build_for_ts, mock_serve_settings):
mock_setting_object = mock_serve_settings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
builder = ModelBuilder(model_server=ModelServer.TGI, model="gpt_llm_burt")
builder.build(sagemaker_session=mock_session)
mock_build_for_ts.assert_called_once()
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_transformers")
def test_model_server_override_transformers_with_model(
self, mock_build_for_ts, mock_serve_settings
):
mock_setting_object = mock_serve_settings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
builder = ModelBuilder(model_server=ModelServer.MMS, model="gpt_llm_burt")
builder.build(sagemaker_session=mock_session)
mock_build_for_ts.assert_called_once()
@patch("os.makedirs", Mock())
@patch(
"sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id",
return_value=False,
)
@patch("sagemaker.serve.builder.model_builder._detect_framework_and_version")
@patch("sagemaker.serve.builder.model_builder.prepare_for_torchserve")
@patch("sagemaker.serve.builder.model_builder.save_pkl")
@patch("sagemaker.serve.builder.model_builder.auto_detect_container")
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.SageMakerEndpointMode")
@patch("sagemaker.serve.builder.model_builder.Model")
@patch("os.path.exists")
def test_build_happy_path_with_sagemaker_endpoint_mode_and_byoc(
self,
mock_path_exists,
mock_sdk_model,
mock_sageMakerEndpointMode,
mock_serveSettings,
mock_detect_container,
mock_save_pkl,
mock_prepare_for_torchserve,
mock_detect_fw_version,
mock_is_jumpstart_model_id,
):
# setup mocks
mock_detect_container.side_effect = lambda model, region, instance_type: (
mock_image_uri
if model == mock_fw_model
and region == mock_session.boto_region_name
and instance_type == "ml.c5.xlarge"
else None
)
mock_detect_fw_version.return_value = framework, version
mock_prepare_for_torchserve.side_effect = (
lambda model_path, shared_libs, dependencies, session, image_uri, inference_spec: (
mock_secret_key
if model_path == MODEL_PATH
and shared_libs == []
and dependencies == {"auto": False}
and session == session
and image_uri == mock_image_uri
and inference_spec is None
else None
)
)
# Mock _ServeSettings
mock_setting_object = mock_serveSettings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
mock_path_exists.side_effect = lambda path: True if path == "test_path" else False
mock_mode = Mock()
mock_sageMakerEndpointMode.side_effect = lambda inference_spec, model_server: (
mock_mode if inference_spec is None and model_server == ModelServer.TORCHSERVE else None
)
mock_mode.prepare.side_effect = lambda model_path, secret_key, s3_model_data_url, sagemaker_session, image_uri, jumpstart, **kwargs: ( # noqa E501
(
model_data,
ENV_VAR_PAIR,
)
if model_path == MODEL_PATH
and secret_key == mock_secret_key
and s3_model_data_url == mock_s3_model_data_url
and sagemaker_session == mock_session
and image_uri == mock_image_uri
else None
)
mock_model_obj = Mock()
mock_sdk_model.side_effect = lambda image_uri, image_config, vpc_config, model_data, role, env, sagemaker_session, predictor_cls, name: ( # noqa E501
mock_model_obj
if image_uri == mock_image_uri
and image_config == MOCK_IMAGE_CONFIG
and vpc_config == MOCK_VPC_CONFIG
and model_data == model_data
and role == mock_role_arn
and env == ENV_VARS
and sagemaker_session == mock_session
and "model-name-" in name
else None
)
mock_session.sagemaker_client._user_agent_creator.to_string = lambda: "sample agent"
# run
builder = ModelBuilder(
model_path=MODEL_PATH,
schema_builder=schema_builder,
model=mock_fw_model,
model_server=ModelServer.TORCHSERVE,
image_uri=mock_image_uri,
image_config=MOCK_IMAGE_CONFIG,
vpc_config=MOCK_VPC_CONFIG,
)
build_result = builder.build(sagemaker_session=mock_session)
# assert auto-detection was skipped
mock_detect_container.assert_not_called()
# assert model returned by builder is expected
self.assertEqual(mock_model_obj, build_result)
self.assertEqual(build_result.mode, Mode.SAGEMAKER_ENDPOINT)
self.assertEqual(build_result.modes, {str(Mode.SAGEMAKER_ENDPOINT): mock_mode})
self.assertEqual(build_result.serve_settings, mock_setting_object)
@patch("os.makedirs", Mock())
@patch(
"sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id",
return_value=False,
)
@patch("sagemaker.serve.builder.model_builder._detect_framework_and_version")
@patch("sagemaker.serve.builder.model_builder.prepare_for_torchserve")
@patch("sagemaker.serve.builder.model_builder.save_pkl")
@patch("sagemaker.serve.builder.model_builder.auto_detect_container")
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.SageMakerEndpointMode")
@patch("sagemaker.serve.builder.model_builder.Model")
@patch("os.path.exists")
def test_build_happy_path_with_sagemaker_endpoint_mode_and_1p_dlc_as_byoc(
self,
mock_path_exists,
mock_sdk_model,
mock_sageMakerEndpointMode,
mock_serveSettings,
mock_detect_container,
mock_save_pkl,
mock_prepare_for_torchserve,
mock_detect_fw_version,
mock_is_jumpstart_model_id,
):
# setup mocks
mock_detect_container.side_effect = lambda model, region, instance_type: (
mock_1p_dlc_image_uri
if model == mock_fw_model
and region == mock_session.boto_region_name
and instance_type == "ml.c5.xlarge"
else None
)
mock_detect_fw_version.return_value = framework, version
mock_prepare_for_torchserve.side_effect = (
lambda model_path, shared_libs, dependencies, session, image_uri, inference_spec: (
mock_secret_key
if model_path == MODEL_PATH
and shared_libs == []
and dependencies == {"auto": False}
and session == session
and image_uri == mock_1p_dlc_image_uri
and inference_spec is None
else None
)
)
# Mock _ServeSettings
mock_setting_object = mock_serveSettings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
mock_path_exists.side_effect = lambda path: True if path == "test_path" else False
mock_mode = Mock()
mock_sageMakerEndpointMode.side_effect = lambda inference_spec, model_server: (
mock_mode if inference_spec is None and model_server == ModelServer.TORCHSERVE else None
)
mock_mode.prepare.side_effect = lambda model_path, secret_key, s3_model_data_url, sagemaker_session, image_uri, jumpstart, **kwargs: ( # noqa E501
(
model_data,
ENV_VAR_PAIR,
)
if model_path == MODEL_PATH
and secret_key == mock_secret_key
and s3_model_data_url == mock_s3_model_data_url
and sagemaker_session == mock_session
and image_uri == mock_1p_dlc_image_uri
else None
)
mock_model_obj = Mock()
mock_sdk_model.side_effect = lambda image_uri, image_config, vpc_config, model_data, role, env, sagemaker_session, predictor_cls, name: ( # noqa E501
mock_model_obj
if image_uri == mock_1p_dlc_image_uri
and model_data == model_data
and role == mock_role_arn
and env == ENV_VARS
and sagemaker_session == mock_session
and "model-name-" in name
else None
)
mock_session.sagemaker_client._user_agent_creator.to_string = lambda: "sample agent"
# run
builder = ModelBuilder(
model_path=MODEL_PATH,
schema_builder=schema_builder,
model=mock_fw_model,
model_server=ModelServer.TORCHSERVE,
image_uri=mock_1p_dlc_image_uri,
)
build_result = builder.build(sagemaker_session=mock_session)
# assert auto-detection was skipped
mock_detect_container.assert_not_called()
# assert model returned by builder is expected
self.assertEqual(mock_model_obj, build_result)
self.assertEqual(build_result.mode, Mode.SAGEMAKER_ENDPOINT)
self.assertEqual(build_result.modes, {str(Mode.SAGEMAKER_ENDPOINT): mock_mode})
self.assertEqual(build_result.serve_settings, mock_setting_object)
@patch("os.makedirs", Mock())
@patch("sagemaker.serve.builder.model_builder._detect_framework_and_version")
@patch("sagemaker.serve.builder.model_builder.prepare_for_torchserve")
@patch("sagemaker.serve.builder.model_builder.save_pkl")
@patch("sagemaker.serve.builder.model_builder.auto_detect_container")
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.SageMakerEndpointMode")
@patch("sagemaker.serve.builder.model_builder.Model")
@patch("os.path.exists")
def test_build_happy_path_with_sagemaker_endpoint_mode_and_inference_spec(
self,
mock_path_exists,
mock_sdk_model,
mock_sageMakerEndpointMode,
mock_serveSettings,
mock_detect_container,
mock_save_pkl,
mock_prepare_for_torchserve,
mock_detect_fw_version,
):
# setup mocks
mock_native_model = Mock()
mock_inference_spec.load = lambda model_path: (
mock_native_model if model_path == MODEL_PATH else None
)
mock_detect_fw_version.return_value = framework, version
mock_detect_container.side_effect = lambda model, region, instance_type: (
mock_image_uri
if model == mock_native_model
and region == mock_session.boto_region_name
and instance_type == "ml.c5.xlarge"
else None
)
mock_prepare_for_torchserve.side_effect = (
lambda model_path, shared_libs, dependencies, session, image_uri, inference_spec: (
mock_secret_key
if model_path == MODEL_PATH
and shared_libs == []
and dependencies == {"auto": False}
and session == mock_session
and image_uri == mock_image_uri
and inference_spec == mock_inference_spec
else None
)
)
# Mock _ServeSettings
mock_setting_object = mock_serveSettings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
mock_path_exists.side_effect = lambda path: True if path == "test_path" else False
mock_mode = Mock()
mock_sageMakerEndpointMode.side_effect = lambda inference_spec, model_server: (
mock_mode
if inference_spec == mock_inference_spec and model_server == ModelServer.TORCHSERVE
else None
)
mock_mode.prepare.side_effect = lambda model_path, secret_key, s3_model_data_url, sagemaker_session, image_uri, jumpstart, **kwargs: ( # noqa E501
(
model_data,
ENV_VAR_PAIR,
)
if model_path == MODEL_PATH
and secret_key == mock_secret_key
and s3_model_data_url == mock_s3_model_data_url
and sagemaker_session == mock_session
and image_uri == mock_image_uri
else None
)
mock_model_obj = Mock()
mock_sdk_model.side_effect = lambda image_uri, image_config, vpc_config, model_data, role, env, sagemaker_session, predictor_cls, name: ( # noqa E501
mock_model_obj
if image_uri == mock_image_uri
and model_data == model_data
and role == mock_role_arn
and env == ENV_VARS_INF_SPEC
and sagemaker_session == mock_session
and "model-name-" in name
else None
)
# run
builder = ModelBuilder(
model_path=MODEL_PATH,
schema_builder=schema_builder,
inference_spec=mock_inference_spec,
)
build_result = builder.build(sagemaker_session=mock_session)
# assert pkl file was saved
mock_save_pkl.assert_called_once_with(CODE_PATH, (mock_inference_spec, schema_builder))
# assert model returned by builder is expected
self.assertEqual(mock_model_obj, build_result)
self.assertEqual(build_result.mode, Mode.SAGEMAKER_ENDPOINT)
self.assertEqual(build_result.modes, {str(Mode.SAGEMAKER_ENDPOINT): mock_mode})
self.assertEqual(build_result.serve_settings, mock_setting_object)
@patch("os.makedirs", Mock())
@patch(
"sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id",
return_value=False,
)
@patch("sagemaker.serve.builder.model_builder._detect_framework_and_version")
@patch("sagemaker.serve.builder.model_builder.prepare_for_torchserve")
@patch("sagemaker.serve.builder.model_builder.save_pkl")
@patch("sagemaker.serve.builder.model_builder.auto_detect_container")
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.SageMakerEndpointMode")
@patch("sagemaker.serve.builder.model_builder.Model")
@patch("os.path.exists")
def test_build_happy_path_with_sagemakerEndpoint_mode_and_model(
self,
mock_path_exists,
mock_sdk_model,
mock_sageMakerEndpointMode,
mock_serveSettings,
mock_detect_container,
mock_save_pkl,
mock_prepare_for_torchserve,
mock_detect_fw_version,
mock_is_jumpstart_model_id,
):
# setup mocks
mock_detect_container.side_effect = lambda model, region, instance_type: (
mock_image_uri
if model == mock_fw_model
and region == mock_session.boto_region_name
and instance_type == "ml.c5.xlarge"
else None
)
mock_detect_fw_version.return_value = framework, version
mock_prepare_for_torchserve.side_effect = (
lambda model_path, shared_libs, dependencies, session, image_uri, inference_spec: (
mock_secret_key
if model_path == MODEL_PATH
and shared_libs == []
and dependencies == {"auto": False}
and session == session
and image_uri == mock_image_uri
and inference_spec is None
else None
)
)
# Mock _ServeSettings
mock_setting_object = mock_serveSettings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
mock_path_exists.side_effect = lambda path: True if path == "test_path" else False
mock_mode = Mock()
mock_sageMakerEndpointMode.side_effect = lambda inference_spec, model_server: (
mock_mode if inference_spec is None and model_server == ModelServer.TORCHSERVE else None
)
mock_mode.prepare.side_effect = lambda model_path, secret_key, s3_model_data_url, sagemaker_session, image_uri, jumpstart, **kwargs: ( # noqa E501
(
model_data,
ENV_VAR_PAIR,
)
if model_path == MODEL_PATH
and secret_key == mock_secret_key
and s3_model_data_url == mock_s3_model_data_url
and sagemaker_session == mock_session
and image_uri == mock_image_uri
else None
)
mock_model_obj = Mock()
mock_sdk_model.side_effect = lambda image_uri, image_config, vpc_config, model_data, role, env, sagemaker_session, predictor_cls, name: ( # noqa E501
mock_model_obj
if image_uri == mock_image_uri
and model_data == model_data
and role == mock_role_arn
and env == ENV_VARS
and sagemaker_session == mock_session
and "model-name-" in name
else None
)
mock_session.sagemaker_client._user_agent_creator.to_string = lambda: "sample agent"
# run
builder = ModelBuilder(
model_path=MODEL_PATH,
schema_builder=schema_builder,
model=mock_fw_model,
)
build_result = builder.build(sagemaker_session=mock_session)
# assert pkl file was saved
mock_save_pkl.assert_called_once_with(CODE_PATH, (mock_fw_model, schema_builder))
# assert model returned by builder is expected
self.assertEqual(mock_model_obj, build_result)
self.assertEqual(build_result.mode, Mode.SAGEMAKER_ENDPOINT)
self.assertEqual(build_result.modes, {str(Mode.SAGEMAKER_ENDPOINT): mock_mode})
self.assertEqual(build_result.serve_settings, mock_setting_object)
# assert user agent
user_agent = builder.sagemaker_session.sagemaker_client._user_agent_creator.to_string()
self.assertEqual("sample agent ModelBuilder", user_agent)
@patch("os.makedirs", Mock())
@patch(
"sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id",
return_value=False,
)
@patch("sagemaker.serve.builder.model_builder.save_xgboost")
@patch("sagemaker.serve.builder.model_builder._detect_framework_and_version")
@patch("sagemaker.serve.builder.model_builder.prepare_for_torchserve")
@patch("sagemaker.serve.builder.model_builder.save_pkl")
@patch("sagemaker.serve.builder.model_builder.auto_detect_container")
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.SageMakerEndpointMode")
@patch("sagemaker.serve.builder.model_builder.Model")
@patch("os.path.exists")
def test_build_happy_path_with_sagemakerEndpoint_mode_and_xgboost_model(
self,
mock_path_exists,
mock_sdk_model,
mock_sageMakerEndpointMode,
mock_serveSettings,
mock_detect_container,
mock_save_pkl,
mock_prepare_for_torchserve,
mock_detect_fw_version,
mock_save_xgb,
mock_is_jumpstart_model_id,
):
# setup mocks
mock_detect_container.side_effect = lambda model, region, instance_type: (
mock_image_uri
if model == mock_fw_model
and region == mock_session.boto_region_name
and instance_type == "ml.c5.xlarge"
else None
)
mock_detect_fw_version.return_value = "xgboost", version
mock_prepare_for_torchserve.side_effect = (
lambda model_path, shared_libs, dependencies, session, image_uri, inference_spec: (
mock_secret_key
if model_path == MODEL_PATH
and shared_libs == []
and dependencies == {"auto": False}
and session == session
and image_uri == mock_image_uri
and inference_spec is None
else None
)
)
# Mock _ServeSettings
mock_setting_object = mock_serveSettings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
mock_path_exists.side_effect = lambda path: True if path == "test_path" else False
mock_mode = Mock()
mock_sageMakerEndpointMode.side_effect = lambda inference_spec, model_server: (
mock_mode if inference_spec is None and model_server == ModelServer.TORCHSERVE else None
)
mock_mode.prepare.side_effect = lambda model_path, secret_key, s3_model_data_url, sagemaker_session, image_uri, jumpstart, **kwargs: ( # noqa E501
(
model_data,
ENV_VAR_PAIR,
)
if model_path == MODEL_PATH
and secret_key == mock_secret_key
and s3_model_data_url == mock_s3_model_data_url
and sagemaker_session == mock_session
and image_uri == mock_image_uri
else None
)
mock_model_obj = Mock()
mock_sdk_model.side_effect = lambda image_uri, image_config, vpc_config, model_data, role, env, sagemaker_session, predictor_cls, name: ( # noqa E501
mock_model_obj
if image_uri == mock_image_uri
and model_data == model_data
and role == mock_role_arn
and env == ENV_VARS
and sagemaker_session == mock_session
and "model-name-" in name
else None
)
mock_session.sagemaker_client._user_agent_creator.to_string = lambda: "sample agent"
# run
builder = ModelBuilder(
model_path=MODEL_PATH,
schema_builder=schema_builder,
model=mock_fw_model,
)
build_result = builder.build(sagemaker_session=mock_session)
# assert xgboost model is saved
mock_save_xgb.assert_called_once_with(CODE_PATH, mock_fw_model)
# assert pkl file was saved
mock_save_pkl.assert_called_once_with(CODE_PATH, ("xgboost", schema_builder))
# assert model returned by builder is expected
self.assertEqual(mock_model_obj, build_result)
self.assertEqual(build_result.mode, Mode.SAGEMAKER_ENDPOINT)
self.assertEqual(build_result.modes, {str(Mode.SAGEMAKER_ENDPOINT): mock_mode})
self.assertEqual(build_result.serve_settings, mock_setting_object)
# assert user agent
user_agent = builder.sagemaker_session.sagemaker_client._user_agent_creator.to_string()
self.assertEqual("sample agent ModelBuilder", user_agent)
@patch("os.makedirs", Mock())
@patch("sagemaker.serve.builder.model_builder.prepare_for_torchserve")
@patch("sagemaker.serve.builder.model_builder.save_pkl")
@patch("sagemaker.serve.builder.model_builder.auto_detect_container")
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.LocalContainerMode")
@patch("sagemaker.serve.builder.model_builder.Model")
@patch("os.path.exists")
def test_build_happy_path_with_local_container_mode(
self,
mock_path_exists,
mock_sdk_model,
mock_localContainerMode,
mock_serveSettings,
mock_detect_container,
mock_save_pkl,
mock_prepare_for_torchserve,
):
# setup mocks
mock_native_model = Mock()
mock_inference_spec.load = lambda model_path: (
mock_native_model if model_path == MODEL_PATH else None
)
mock_detect_container.side_effect = lambda model, region, instance_type: (
mock_image_uri
if model == mock_native_model
and region == mock_session.boto_region_name
and instance_type == "ml.c5.xlarge"
else None
)
mock_prepare_for_torchserve.side_effect = (
lambda model_path, shared_libs, dependencies, session, image_uri, inference_spec: (
mock_secret_key
if model_path == MODEL_PATH
and shared_libs == []
and dependencies == {"auto": False}
and session == mock_session
and image_uri == mock_image_uri
and inference_spec == mock_inference_spec
else None
)
)
# Mock _ServeSettings
mock_setting_object = mock_serveSettings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
mock_path_exists.side_effect = lambda path: True if path == "test_path" else False
mock_mode = Mock()
mock_localContainerMode.side_effect = (
lambda inference_spec, schema_builder, session, model_path, env_vars, model_server: (
mock_mode
if inference_spec == mock_inference_spec
and schema_builder == schema_builder
and model_server == ModelServer.TORCHSERVE
and session == mock_session
and model_path == MODEL_PATH
and env_vars == {}
and model_server == ModelServer.TORCHSERVE
else None
)
)
mock_mode.prepare.side_effect = lambda: None
mock_model_obj = Mock()
mock_sdk_model.side_effect = lambda image_uri, image_config, vpc_config, model_data, role, env, sagemaker_session, predictor_cls, name: ( # noqa E501
mock_model_obj
if image_uri == mock_image_uri
and model_data is None
and role == mock_role_arn
and env == {}
and sagemaker_session == mock_session
and "model-name-" in name
else None
)
# run
builder = ModelBuilder(
model_path=MODEL_PATH,
schema_builder=schema_builder,
inference_spec=mock_inference_spec,
mode=Mode.LOCAL_CONTAINER,
)
build_result = builder.build(sagemaker_session=mock_session)
# assert pkl file was saved
mock_save_pkl.assert_called_once_with(CODE_PATH, (mock_inference_spec, schema_builder))
# assert model returned by builder is expected
self.assertEqual(mock_model_obj, build_result)
self.assertEqual(build_result.mode, Mode.LOCAL_CONTAINER)
self.assertEqual(build_result.serve_settings, mock_setting_object)
@patch("os.makedirs", Mock())
@patch("sagemaker.serve.builder.model_builder._detect_framework_and_version")
@patch("sagemaker.serve.builder.model_builder.prepare_for_torchserve")
@patch("sagemaker.serve.builder.model_builder.save_pkl")
@patch("sagemaker.serve.builder.model_builder.auto_detect_container")
@patch("sagemaker.serve.builder.model_builder._ServeSettings")
@patch("sagemaker.serve.builder.model_builder.SageMakerEndpointMode")
@patch("sagemaker.serve.builder.model_builder.LocalContainerMode")
@patch("sagemaker.serve.builder.model_builder.Model")
@patch("os.path.exists")
def test_build_happy_path_with_localContainer_mode_overwritten_with_sagemaker_mode(
self,
mock_path_exists,
mock_sdk_model,
mock_localContainerMode,
mock_sageMakerEndpointMode,
mock_serveSettings,
mock_detect_container,
mock_save_pkl,
mock_prepare_for_torchserve,
mock_detect_fw_version,
):
# setup mocks
mock_native_model = Mock()
mock_inference_spec.load = lambda model_path: (
mock_native_model if model_path == MODEL_PATH else None
)
mock_detect_fw_version.return_value = framework, version
mock_detect_container.side_effect = lambda model, region, instance_type: (
mock_image_uri
if model == mock_native_model
and region == mock_session.boto_region_name
and instance_type == "ml.c5.xlarge"
else None
)
mock_prepare_for_torchserve.side_effect = (
lambda model_path, shared_libs, dependencies, session, image_uri, inference_spec: (
mock_secret_key
if model_path == MODEL_PATH
and shared_libs == []
and dependencies == {"auto": False}
and session == mock_session
and image_uri == mock_image_uri
and inference_spec == mock_inference_spec
else None
)
)
# Mock _ServeSettings
mock_setting_object = mock_serveSettings.return_value
mock_setting_object.role_arn = mock_role_arn
mock_setting_object.s3_model_data_url = mock_s3_model_data_url
mock_path_exists.side_effect = lambda path: True if path == "test_path" else False
mock_lc_mode = Mock()
mock_localContainerMode.side_effect = (
lambda inference_spec, schema_builder, session, model_path, env_vars, model_server: (
mock_lc_mode
if inference_spec == mock_inference_spec
and schema_builder == schema_builder
and model_server == ModelServer.TORCHSERVE
and session == mock_session
and model_path == MODEL_PATH
and env_vars == {}
and model_server == ModelServer.TORCHSERVE
else None
)
)
mock_lc_mode.prepare.side_effect = lambda: None
mock_sagemaker_endpoint_mode = Mock()
mock_sageMakerEndpointMode.side_effect = lambda inference_spec, model_server: (
mock_sagemaker_endpoint_mode
if inference_spec == mock_inference_spec and model_server == ModelServer.TORCHSERVE
else None
)
mock_sagemaker_endpoint_mode.prepare.side_effect = lambda model_path, secret_key, s3_model_data_url, sagemaker_session, image_uri, jumpstart, **kwargs: ( # noqa E501
(
model_data,
ENV_VAR_PAIR,
)
if model_path == MODEL_PATH
and secret_key == mock_secret_key
and s3_model_data_url == mock_s3_model_data_url
and sagemaker_session == mock_session
and image_uri == mock_image_uri
else None
)
mock_model_obj = Mock()
mock_sdk_model.side_effect = lambda image_uri, image_config, vpc_config, model_data, role, env, sagemaker_session, predictor_cls, name: ( # noqa E501
mock_model_obj
if image_uri == mock_image_uri
and model_data is None
and role == mock_role_arn
and env == {}
and sagemaker_session == mock_session
and "model-name-" in name
else None
)
# run
builder = ModelBuilder(
model_path=MODEL_PATH,
schema_builder=schema_builder,
inference_spec=mock_inference_spec,
mode=Mode.LOCAL_CONTAINER,
)
build_result = builder.build(sagemaker_session=mock_session)
# assert pkl file was saved
mock_save_pkl.assert_called_once_with(CODE_PATH, (mock_inference_spec, schema_builder))