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#
# 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
import pytest
from mock import Mock, patch
from sagemaker.model import FrameworkModel
from sagemaker.pipeline import PipelineModel
from sagemaker.predictor import Predictor
from sagemaker.sparkml import SparkMLModel
ENTRY_POINT = "blah.py"
MODEL_DATA_1 = "s3://bucket/model_1.tar.gz"
MODEL_DATA_2 = "s3://bucket/model_2.tar.gz"
MODEL_IMAGE_1 = "mi-1"
MODEL_IMAGE_2 = "mi-2"
INSTANCE_TYPE = "ml.m4.xlarge"
ROLE = "some-role"
ENV_1 = {"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "application/json"}
ENV_2 = {"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "text/csv"}
ENDPOINT = "some-ep"
TIMESTAMP = "2017-10-10-14-14-15"
BUCKET_NAME = "mybucket"
INSTANCE_COUNT = 1
IMAGE_NAME = "fakeimage"
REGION = "us-west-2"
class DummyFrameworkModel(FrameworkModel):
def __init__(self, sagemaker_session, **kwargs):
super(DummyFrameworkModel, self).__init__(
MODEL_DATA_1,
MODEL_IMAGE_1,
ROLE,
ENTRY_POINT,
sagemaker_session=sagemaker_session,
**kwargs,
)
def create_predictor(self, endpoint_name):
return Predictor(endpoint_name, self.sagemaker_session)
@pytest.fixture()
def sagemaker_session():
boto_mock = Mock(name="boto_session", region_name=REGION)
sms = Mock(
name="sagemaker_session",
boto_session=boto_mock,
boto_region_name=REGION,
config=None,
local_mode=False,
s3_client=None,
s3_resource=None,
)
sms.default_bucket = Mock(name="default_bucket", return_value=BUCKET_NAME)
return sms
@patch("tarfile.open")
@patch("time.strftime", return_value=TIMESTAMP)
def test_prepare_container_def(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(
model_data=MODEL_DATA_2,
role=ROLE,
sagemaker_session=sagemaker_session,
env={"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "text/csv"},
)
model = PipelineModel(
models=[framework_model, sparkml_model], role=ROLE, sagemaker_session=sagemaker_session
)
assert model.pipeline_container_def(INSTANCE_TYPE) == [
{
"Environment": {
"SAGEMAKER_PROGRAM": "blah.py",
"SAGEMAKER_SUBMIT_DIRECTORY": "s3://mybucket/mi-1-2017-10-10-14-14-15/sourcedir.tar.gz",
"SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
"SAGEMAKER_REGION": "us-west-2",
},
"Image": "mi-1",
"ModelDataUrl": "s3://bucket/model_1.tar.gz",
},
{
"Environment": {"SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT": "text/csv"},
"Image": "246618743249.dkr.ecr.us-west-2.amazonaws.com"
+ "/sagemaker-sparkml-serving:2.4",
"ModelDataUrl": "s3://bucket/model_2.tar.gz",
},
]
@patch("tarfile.open")
@patch("time.strftime", return_value=TIMESTAMP)
def test_deploy(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(
model_data=MODEL_DATA_2, role=ROLE, sagemaker_session=sagemaker_session
)
model = PipelineModel(
models=[framework_model, sparkml_model], role=ROLE, sagemaker_session=sagemaker_session
)
kms_key = "pipeline-model-deploy-kms-key"
model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1, kms_key=kms_key)
sagemaker_session.endpoint_from_production_variants.assert_called_with(
name="mi-1-2017-10-10-14-14-15",
production_variants=[
{
"InitialVariantWeight": 1,
"ModelName": "mi-1-2017-10-10-14-14-15",
"InstanceType": INSTANCE_TYPE,
"InitialInstanceCount": 1,
"VariantName": "AllTraffic",
}
],
tags=None,
kms_key=kms_key,
wait=True,
data_capture_config_dict=None,
)
@patch("tarfile.open")
@patch("time.strftime", return_value=TIMESTAMP)
def test_deploy_endpoint_name(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(
model_data=MODEL_DATA_2, role=ROLE, sagemaker_session=sagemaker_session
)
model = PipelineModel(
models=[framework_model, sparkml_model], role=ROLE, sagemaker_session=sagemaker_session
)
model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1)
sagemaker_session.endpoint_from_production_variants.assert_called_with(
name="mi-1-2017-10-10-14-14-15",
production_variants=[
{
"InitialVariantWeight": 1,
"ModelName": "mi-1-2017-10-10-14-14-15",
"InstanceType": INSTANCE_TYPE,
"InitialInstanceCount": 1,
"VariantName": "AllTraffic",
}
],
tags=None,
kms_key=None,
wait=True,
data_capture_config_dict=None,
)
@patch("tarfile.open")
@patch("time.strftime", return_value=TIMESTAMP)
def test_deploy_update_endpoint(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
endpoint_name = "endpoint-name"
sparkml_model = SparkMLModel(
model_data=MODEL_DATA_2, role=ROLE, sagemaker_session=sagemaker_session
)
model = PipelineModel(
models=[framework_model, sparkml_model], role=ROLE, sagemaker_session=sagemaker_session
)
model.deploy(
instance_type=INSTANCE_TYPE,
initial_instance_count=1,
endpoint_name=endpoint_name,
update_endpoint=True,
)
sagemaker_session.create_endpoint_config.assert_called_with(
name=model.name,
model_name=model.name,
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
tags=None,
kms_key=None,
data_capture_config_dict=None,
)
config_name = sagemaker_session.create_endpoint_config(
name=model.name,
model_name=model.name,
initial_instance_count=INSTANCE_COUNT,
instance_type=INSTANCE_TYPE,
)
sagemaker_session.update_endpoint.assert_called_with(endpoint_name, config_name, wait=True)
sagemaker_session.create_endpoint.assert_not_called()
@patch("tarfile.open")
@patch("time.strftime", return_value=TIMESTAMP)
def test_transformer(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(
model_data=MODEL_DATA_2, role=ROLE, sagemaker_session=sagemaker_session
)
model_name = "ModelName"
model = PipelineModel(
models=[framework_model, sparkml_model],
role=ROLE,
sagemaker_session=sagemaker_session,
name=model_name,
)
instance_count = 55
strategy = "MultiRecord"
assemble_with = "Line"
output_path = "s3://output/path"
output_kms_key = "output:kms:key"
accept = "application/jsonlines"
env = {"my_key": "my_value"}
max_concurrent_transforms = 20
max_payload = 5
tags = [{"my_tag": "my_value"}]
volume_kms_key = "volume:kms:key"
transformer = model.transformer(
instance_type=INSTANCE_TYPE,
instance_count=instance_count,
strategy=strategy,
assemble_with=assemble_with,
output_path=output_path,
output_kms_key=output_kms_key,
accept=accept,
env=env,
max_concurrent_transforms=max_concurrent_transforms,
max_payload=max_payload,
tags=tags,
volume_kms_key=volume_kms_key,
)
assert transformer.instance_type == INSTANCE_TYPE
assert transformer.instance_count == instance_count
assert transformer.strategy == strategy
assert transformer.assemble_with == assemble_with
assert transformer.output_path == output_path
assert transformer.output_kms_key == output_kms_key
assert transformer.accept == accept
assert transformer.env == env
assert transformer.max_concurrent_transforms == max_concurrent_transforms
assert transformer.max_payload == max_payload
assert transformer.tags == tags
assert transformer.volume_kms_key == volume_kms_key
assert transformer.model_name == model_name
@patch("tarfile.open")
@patch("time.strftime", return_value=TIMESTAMP)
def test_deploy_tags(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(
model_data=MODEL_DATA_2, role=ROLE, sagemaker_session=sagemaker_session
)
model = PipelineModel(
models=[framework_model, sparkml_model], role=ROLE, sagemaker_session=sagemaker_session
)
tags = [{"ModelName": "TestModel"}]
model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1, tags=tags)
sagemaker_session.endpoint_from_production_variants.assert_called_with(
name="mi-1-2017-10-10-14-14-15",
production_variants=[
{
"InitialVariantWeight": 1,
"ModelName": "mi-1-2017-10-10-14-14-15",
"InstanceType": INSTANCE_TYPE,
"InitialInstanceCount": 1,
"VariantName": "AllTraffic",
}
],
tags=tags,
wait=True,
kms_key=None,
data_capture_config_dict=None,
)
def test_delete_model_without_deploy(sagemaker_session):
pipeline_model = PipelineModel([], role=ROLE, sagemaker_session=sagemaker_session)
expected_error_message = "The SageMaker model must be created before attempting to delete."
with pytest.raises(ValueError, match=expected_error_message):
pipeline_model.delete_model()
@patch("tarfile.open")
@patch("time.strftime", return_value=TIMESTAMP)
def test_delete_model(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
pipeline_model = PipelineModel(
[framework_model], role=ROLE, sagemaker_session=sagemaker_session
)
pipeline_model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1)
pipeline_model.delete_model()
sagemaker_session.delete_model.assert_called_with(pipeline_model.name)
@patch("tarfile.open")
@patch("time.strftime", return_value=TIMESTAMP)
def test_network_isolation(tfo, time, sagemaker_session):
framework_model = DummyFrameworkModel(sagemaker_session)
sparkml_model = SparkMLModel(
model_data=MODEL_DATA_2, role=ROLE, sagemaker_session=sagemaker_session
)
model = PipelineModel(
models=[framework_model, sparkml_model],
role=ROLE,
sagemaker_session=sagemaker_session,
enable_network_isolation=True,
)
model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=1)
sagemaker_session.create_model.assert_called_with(
model.name,
ROLE,
[
{
"Image": "mi-1",
"Environment": {
"SAGEMAKER_PROGRAM": "blah.py",
"SAGEMAKER_SUBMIT_DIRECTORY": "s3://mybucket/mi-1-2017-10-10-14-14-15/sourcedir.tar.gz",
"SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
"SAGEMAKER_REGION": "us-west-2",
},
"ModelDataUrl": "s3://bucket/model_1.tar.gz",
},
{
"Image": "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-sparkml-serving:2.4",
"Environment": {},
"ModelDataUrl": "s3://bucket/model_2.tar.gz",
},
],
vpc_config=None,
enable_network_isolation=True,
)
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