|
13 | 13 | from __future__ import absolute_import
|
14 | 14 |
|
15 | 15 | import json
|
16 |
| -import os |
17 | 16 | import math
|
| 17 | +import os |
| 18 | + |
18 | 19 | import pytest
|
19 | 20 | import scipy.stats as st
|
20 | 21 |
|
21 | 22 | from sagemaker import image_uris
|
22 | 23 | from sagemaker.s3 import S3Uploader
|
23 | 24 | from sagemaker.session import production_variant
|
24 | 25 | from sagemaker.sparkml import SparkMLModel
|
25 |
| -from sagemaker.utils import sagemaker_timestamp |
26 | 26 | from sagemaker.content_types import CONTENT_TYPE_CSV
|
27 | 27 | from sagemaker.utils import unique_name_from_base
|
28 | 28 | from sagemaker.predictor import Predictor
|
29 | 29 | from sagemaker.serializers import CSVSerializer
|
30 |
| - |
31 |
| - |
32 | 30 | import tests.integ
|
33 | 31 |
|
34 | 32 |
|
35 | 33 | ROLE = "SageMakerRole"
|
36 |
| -MODEL_NAME = "test-xgboost-model-{}".format(sagemaker_timestamp()) |
| 34 | +MODEL_NAME = unique_name_from_base("test-xgboost-model") |
37 | 35 | DEFAULT_REGION = "us-west-2"
|
38 | 36 | DEFAULT_INSTANCE_TYPE = "ml.m5.xlarge"
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39 | 37 | DEFAULT_INSTANCE_COUNT = 1
|
@@ -98,7 +96,11 @@ def multi_variant_endpoint(sagemaker_session):
|
98 | 96 | )
|
99 | 97 |
|
100 | 98 | image_uri = image_uris.retrieve(
|
101 |
| - "xgboost", sagemaker_session.boto_region_name, version="0.90-1", image_scope="inference" |
| 99 | + "xgboost", |
| 100 | + sagemaker_session.boto_region_name, |
| 101 | + version="0.90-1", |
| 102 | + instancee_type=DEFAULT_INSTANCE_TYPE, |
| 103 | + image_scope="inference", |
102 | 104 | )
|
103 | 105 | multi_variant_endpoint_model = sagemaker_session.create_model(
|
104 | 106 | name=MODEL_NAME,
|
|
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