File size: 2,411 Bytes
476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | # 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
import pytest
from mock import Mock
from sagemaker import image_uris
from sagemaker.sparkml import SparkMLModel, SparkMLPredictor
MODEL_DATA = "s3://bucket/model.tar.gz"
ROLE = "myrole"
TRAIN_INSTANCE_TYPE = "ml.c4.xlarge"
REGION = "us-west-2"
BUCKET_NAME = "Some-Bucket"
ENDPOINT = "some-endpoint"
ENDPOINT_DESC = {"EndpointConfigName": ENDPOINT}
ENDPOINT_CONFIG_DESC = {"ProductionVariants": [{"ModelName": "model-1"}, {"ModelName": "model-2"}]}
@pytest.fixture()
def sagemaker_session():
boto_mock = Mock(name="boto_session", region_name=REGION)
sms = Mock(
name="sagemaker_session",
boto_session=boto_mock,
region_name=REGION,
config=None,
local_mode=False,
)
sms.boto_region_name = REGION
sms.sagemaker_client.describe_endpoint = Mock(return_value=ENDPOINT_DESC)
sms.sagemaker_client.describe_endpoint_config = Mock(return_value=ENDPOINT_CONFIG_DESC)
return sms
def test_sparkml_model(sagemaker_session):
sparkml = SparkMLModel(sagemaker_session=sagemaker_session, model_data=MODEL_DATA, role=ROLE)
assert sparkml.image_uri == image_uris.retrieve("sparkml-serving", REGION, version="2.4")
def test_predictor_type(sagemaker_session):
sparkml = SparkMLModel(sagemaker_session=sagemaker_session, model_data=MODEL_DATA, role=ROLE)
predictor = sparkml.deploy(1, TRAIN_INSTANCE_TYPE)
assert isinstance(predictor, SparkMLPredictor)
def test_predictor_custom_serialization(sagemaker_session):
sparkml = SparkMLModel(sagemaker_session=sagemaker_session, model_data=MODEL_DATA, role=ROLE)
custom_serializer = Mock()
predictor = sparkml.deploy(1, TRAIN_INSTANCE_TYPE, serializer=custom_serializer)
assert isinstance(predictor, SparkMLPredictor)
assert predictor.serializer is custom_serializer
|