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Revert "feature: Decouple model.right_size() from model registry" #3700

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Revert "feature: Decouple model.right_size() from model registry" #3700

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38 changes: 5 additions & 33 deletions src/sagemaker/inference_recommender/inference_recommender_mixin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@ class Phase:
"""

def __init__(self, duration_in_seconds: int, initial_number_of_users: int, spawn_rate: int):
"""Initialize a `Phase`"""
"""Initialze a `Phase`"""
self.to_json = {
"DurationInSeconds": duration_in_seconds,
"InitialNumberOfUsers": initial_number_of_users,
Expand All @@ -53,7 +53,7 @@ class ModelLatencyThreshold:
"""

def __init__(self, percentile: str, value_in_milliseconds: int):
"""Initialize a `ModelLatencyThreshold`"""
"""Initialze a `ModelLatencyThreshold`"""
self.to_json = {"Percentile": percentile, "ValueInMilliseconds": value_in_milliseconds}


Expand All @@ -79,12 +79,6 @@ def right_size(
):
"""Recommends an instance type for a SageMaker or BYOC model.

Create a SageMaker ``Model`` or use a registered ``ModelPackage``,
to start an Inference Recommender job.

The name of the created model is accessible in the ``name`` field of
this ``Model`` after right_size returns.

Args:
sample_payload_url (str): The S3 path where the sample payload is stored.
supported_content_types: (list[str]): The supported MIME types for the input data.
Expand Down Expand Up @@ -125,6 +119,8 @@ def right_size(
sagemaker.model.Model: A SageMaker ``Model`` object. See
:func:`~sagemaker.model.Model` for full details.
"""
if not isinstance(self, sagemaker.model.ModelPackage):
raise ValueError("right_size() is currently only supported with a registered model")

if not framework and self._framework():
framework = INFERENCE_RECOMMENDER_FRAMEWORK_MAPPING.get(self._framework(), framework)
Expand Down Expand Up @@ -153,36 +149,12 @@ def right_size(

self._init_sagemaker_session_if_does_not_exist()

if isinstance(self, sagemaker.model.Model) and not isinstance(
self, sagemaker.model.ModelPackage
):
primary_container_def = self.prepare_container_def()
if not self.name:
self._ensure_base_name_if_needed(
image_uri=primary_container_def["Image"],
script_uri=self.source_dir,
model_uri=self.model_data,
)
self._set_model_name_if_needed()

create_model_args = dict(
name=self.name,
role=self.role,
container_defs=None,
primary_container=primary_container_def,
vpc_config=self.vpc_config,
enable_network_isolation=self.enable_network_isolation(),
)
LOGGER.warning("Attempting to create new model with name %s", self.name)
self.sagemaker_session.create_model(**create_model_args)

ret_name = self.sagemaker_session.create_inference_recommendations_job(
role=self.role,
job_name=job_name,
job_type=job_type,
job_duration_in_seconds=job_duration_in_seconds,
model_name=self.name,
model_package_version_arn=getattr(self, "model_package_arn", None),
model_package_version_arn=self.model_package_arn,
framework=framework,
framework_version=framework_version,
sample_payload_url=sample_payload_url,
Expand Down
18 changes: 1 addition & 17 deletions src/sagemaker/session.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4820,7 +4820,6 @@ def _create_inference_recommendations_job_request(
framework: str,
sample_payload_url: str,
supported_content_types: List[str],
model_name: str = None,
model_package_version_arn: str = None,
job_duration_in_seconds: int = None,
job_type: str = "Default",
Expand All @@ -4844,7 +4843,6 @@ def _create_inference_recommendations_job_request(
framework (str): The machine learning framework of the Image URI.
sample_payload_url (str): The S3 path where the sample payload is stored.
supported_content_types (List[str]): The supported MIME types for the input data.
model_name (str): Name of the Amazon SageMaker ``Model`` to be used.
model_package_version_arn (str): The Amazon Resource Name (ARN) of a
versioned model package.
job_duration_in_seconds (int): The maximum job duration that a job
Expand Down Expand Up @@ -4892,15 +4890,10 @@ def _create_inference_recommendations_job_request(
"RoleArn": role,
"InputConfig": {
"ContainerConfig": containerConfig,
"ModelPackageVersionArn": model_package_version_arn,
},
}

request.get("InputConfig").update(
{"ModelPackageVersionArn": model_package_version_arn}
if model_package_version_arn
else {"ModelName": model_name}
)

if job_description:
request["JobDescription"] = job_description
if job_duration_in_seconds:
Expand All @@ -4925,7 +4918,6 @@ def create_inference_recommendations_job(
supported_content_types: List[str],
job_name: str = None,
job_type: str = "Default",
model_name: str = None,
model_package_version_arn: str = None,
job_duration_in_seconds: int = None,
nearest_model_name: str = None,
Expand All @@ -4946,7 +4938,6 @@ def create_inference_recommendations_job(
You must grant sufficient permissions to this role.
sample_payload_url (str): The S3 path where the sample payload is stored.
supported_content_types (List[str]): The supported MIME types for the input data.
model_name (str): Name of the Amazon SageMaker ``Model`` to be used.
model_package_version_arn (str): The Amazon Resource Name (ARN) of a
versioned model package.
job_name (str): The name of the job being run.
Expand All @@ -4973,12 +4964,6 @@ def create_inference_recommendations_job(
str: The name of the job created. In the form of `SMPYTHONSDK-<timestamp>`
"""

if model_name is None and model_package_version_arn is None:
raise ValueError("Please provide either model_name or model_package_version_arn.")

if model_name is not None and model_package_version_arn is not None:
raise ValueError("Please provide either model_name or model_package_version_arn.")

if not job_name:
unique_tail = uuid.uuid4()
job_name = "SMPYTHONSDK-" + str(unique_tail)
Expand All @@ -4987,7 +4972,6 @@ def create_inference_recommendations_job(
create_inference_recommendations_job_request = (
self._create_inference_recommendations_job_request(
role=role,
model_name=model_name,
model_package_version_arn=model_package_version_arn,
job_name=job_name,
job_type=job_type,
Expand Down
181 changes: 0 additions & 181 deletions tests/integ/test_inference_recommender.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,6 @@

import pytest

from sagemaker.model import Model
from sagemaker.sklearn.model import SKLearnModel, SKLearnPredictor
from sagemaker.utils import unique_name_from_base
from tests.integ import DATA_DIR
Expand Down Expand Up @@ -155,120 +154,6 @@ def advanced_right_sized_model(sagemaker_session, cpu_instance_type):
)


@pytest.fixture(scope="module")
def default_right_sized_unregistered_model(sagemaker_session, cpu_instance_type):
with timeout(minutes=45):
try:
ir_job_name = unique_name_from_base("test-ir-right-size-job-name")
model_data = sagemaker_session.upload_data(path=IR_SKLEARN_MODEL)
payload_data = sagemaker_session.upload_data(path=IR_SKLEARN_PAYLOAD)

iam_client = sagemaker_session.boto_session.client("iam")
role_arn = iam_client.get_role(RoleName="SageMakerRole")["Role"]["Arn"]

sklearn_model = SKLearnModel(
model_data=model_data,
role=role_arn,
entry_point=IR_SKLEARN_ENTRY_POINT,
framework_version=IR_SKLEARN_FRAMEWORK_VERSION,
)

return (
sklearn_model.right_size(
job_name=ir_job_name,
sample_payload_url=payload_data,
supported_content_types=IR_SKLEARN_CONTENT_TYPE,
supported_instance_types=[cpu_instance_type],
framework=IR_SKLEARN_FRAMEWORK,
log_level="Quiet",
),
ir_job_name,
)
except Exception:
sagemaker_session.delete_model(ModelName=sklearn_model.name)


@pytest.fixture(scope="module")
def advanced_right_sized_unregistered_model(sagemaker_session, cpu_instance_type):
with timeout(minutes=45):
try:
model_data = sagemaker_session.upload_data(path=IR_SKLEARN_MODEL)
payload_data = sagemaker_session.upload_data(path=IR_SKLEARN_PAYLOAD)

iam_client = sagemaker_session.boto_session.client("iam")
role_arn = iam_client.get_role(RoleName="SageMakerRole")["Role"]["Arn"]

sklearn_model = SKLearnModel(
model_data=model_data,
role=role_arn,
entry_point=IR_SKLEARN_ENTRY_POINT,
framework_version=IR_SKLEARN_FRAMEWORK_VERSION,
)

hyperparameter_ranges = [
{
"instance_types": CategoricalParameter([cpu_instance_type]),
"TEST_PARAM": CategoricalParameter(
["TEST_PARAM_VALUE_1", "TEST_PARAM_VALUE_2"]
),
}
]

phases = [
Phase(duration_in_seconds=300, initial_number_of_users=2, spawn_rate=2),
Phase(duration_in_seconds=300, initial_number_of_users=14, spawn_rate=2),
]

model_latency_thresholds = [
ModelLatencyThreshold(percentile="P95", value_in_milliseconds=100)
]

return sklearn_model.right_size(
sample_payload_url=payload_data,
supported_content_types=IR_SKLEARN_CONTENT_TYPE,
framework=IR_SKLEARN_FRAMEWORK,
job_duration_in_seconds=3600,
hyperparameter_ranges=hyperparameter_ranges,
phases=phases,
model_latency_thresholds=model_latency_thresholds,
max_invocations=100,
max_tests=5,
max_parallel_tests=5,
log_level="Quiet",
)

except Exception:
sagemaker_session.delete_model(ModelName=sklearn_model.name)


@pytest.fixture(scope="module")
def default_right_sized_unregistered_base_model(sagemaker_session, cpu_instance_type):
with timeout(minutes=45):
try:
ir_job_name = unique_name_from_base("test-ir-right-size-job-name")
model_data = sagemaker_session.upload_data(path=IR_SKLEARN_MODEL)
payload_data = sagemaker_session.upload_data(path=IR_SKLEARN_PAYLOAD)

iam_client = sagemaker_session.boto_session.client("iam")
role_arn = iam_client.get_role(RoleName="SageMakerRole")["Role"]["Arn"]

model = Model(model_data=model_data, role=role_arn, entry_point=IR_SKLEARN_ENTRY_POINT)

return (
model.right_size(
job_name=ir_job_name,
sample_payload_url=payload_data,
supported_content_types=IR_SKLEARN_CONTENT_TYPE,
supported_instance_types=[cpu_instance_type],
framework=IR_SKLEARN_FRAMEWORK,
log_level="Quiet",
),
ir_job_name,
)
except Exception:
sagemaker_session.delete_model(ModelName=model.name)


@pytest.mark.slow_test
def test_default_right_size_and_deploy_registered_model_sklearn(
default_right_sized_model, sagemaker_session
Expand All @@ -291,72 +176,6 @@ def test_default_right_size_and_deploy_registered_model_sklearn(
predictor.delete_endpoint()


@pytest.mark.slow_test
def test_default_right_size_and_deploy_unregistered_model_sklearn(
default_right_sized_unregistered_model, sagemaker_session
):
endpoint_name = unique_name_from_base("test-ir-right-size-default-unregistered-sklearn")

right_size_model, ir_job_name = default_right_sized_unregistered_model
with timeout(minutes=45):
try:
right_size_model.predictor_cls = SKLearnPredictor
predictor = right_size_model.deploy(endpoint_name=endpoint_name)

payload = pd.read_csv(IR_SKLEARN_DATA, header=None)

inference = predictor.predict(payload)
assert inference is not None
assert 26 == len(inference)
finally:
predictor.delete_model()
predictor.delete_endpoint()


@pytest.mark.slow_test
def test_default_right_size_and_deploy_unregistered_base_model(
default_right_sized_unregistered_base_model, sagemaker_session
):
endpoint_name = unique_name_from_base("test-ir-right-size-default-unregistered-base")

right_size_model, ir_job_name = default_right_sized_unregistered_base_model
with timeout(minutes=45):
try:
right_size_model.predictor_cls = SKLearnPredictor
predictor = right_size_model.deploy(endpoint_name=endpoint_name)

payload = pd.read_csv(IR_SKLEARN_DATA, header=None)

inference = predictor.predict(payload)
assert inference is not None
assert 26 == len(inference)
finally:
predictor.delete_model()
predictor.delete_endpoint()


@pytest.mark.slow_test
def test_advanced_right_size_and_deploy_unregistered_model_sklearn(
advanced_right_sized_unregistered_model, sagemaker_session
):
endpoint_name = unique_name_from_base("test-ir-right-size-advanced-sklearn")

right_size_model = advanced_right_sized_unregistered_model
with timeout(minutes=45):
try:
right_size_model.predictor_cls = SKLearnPredictor
predictor = right_size_model.deploy(endpoint_name=endpoint_name)

payload = pd.read_csv(IR_SKLEARN_DATA, header=None)

inference = predictor.predict(payload)
assert inference is not None
assert 26 == len(inference)
finally:
predictor.delete_model()
predictor.delete_endpoint()


@pytest.mark.slow_test
def test_advanced_right_size_and_deploy_registered_model_sklearn(
advanced_right_sized_model, sagemaker_session
Expand Down
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