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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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | # 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 MagicMock, Mock
import sagemaker
JOB_NAME = "myjob"
INITIAL_INSTANCE_COUNT = 1
INSTANCE_TYPE = "ml.c4.xlarge"
ACCELERATOR_TYPE = "ml.eia.medium"
IMAGE = "myimage"
S3_MODEL_ARTIFACTS = "s3://mybucket/mymodel"
TRAIN_ROLE = "mytrainrole"
VPC_CONFIG = {"Subnets": ["subnet-foo"], "SecurityGroupIds": ["sg-foo"]}
TRAINING_JOB_RESPONSE = {
"AlgorithmSpecification": {"TrainingImage": IMAGE},
"ModelArtifacts": {"S3ModelArtifacts": S3_MODEL_ARTIFACTS},
"RoleArn": TRAIN_ROLE,
"VpcConfig": VPC_CONFIG,
}
FULL_CONTAINER_DEF = {"Environment": {}, "Image": IMAGE, "ModelDataUrl": S3_MODEL_ARTIFACTS}
DEPLOY_IMAGE = "mydeployimage"
DEPLOY_ROLE = "mydeployrole"
NEW_ENTITY_NAME = "mynewendpoint"
ENV_VARS = {"PYTHONUNBUFFERED": "TRUE", "some": "nonsense"}
ENDPOINT_FROM_MODEL_RETURNED_NAME = "endpointfrommodelname"
REGION = "us-west-2"
@pytest.fixture()
def sagemaker_session():
boto_mock = MagicMock(name="boto_session", region_name=REGION)
ims = sagemaker.Session(
sagemaker_client=MagicMock(name="sagemaker_client"), boto_session=boto_mock
)
ims.sagemaker_client.describe_training_job = Mock(
name="describe_training_job", return_value=TRAINING_JOB_RESPONSE
)
ims.endpoint_from_model_data = Mock(
"endpoint_from_model_data", return_value=ENDPOINT_FROM_MODEL_RETURNED_NAME
)
return ims
def test_all_defaults_no_existing_entities(sagemaker_session):
original_args = {
"job_name": JOB_NAME,
"initial_instance_count": INITIAL_INSTANCE_COUNT,
"instance_type": INSTANCE_TYPE,
"wait": False,
}
returned_name = sagemaker_session.endpoint_from_job(**original_args)
expected_args = original_args.copy()
expected_args.pop("job_name")
expected_args["model_s3_location"] = S3_MODEL_ARTIFACTS
expected_args["image_uri"] = IMAGE
expected_args["role"] = TRAIN_ROLE
expected_args["name"] = JOB_NAME
expected_args["model_environment_vars"] = None
expected_args["model_vpc_config"] = VPC_CONFIG
expected_args["accelerator_type"] = None
expected_args["data_capture_config"] = None
sagemaker_session.endpoint_from_model_data.assert_called_once_with(**expected_args)
assert returned_name == ENDPOINT_FROM_MODEL_RETURNED_NAME
def test_no_defaults_no_existing_entities(sagemaker_session):
vpc_config_override = {"Subnets": ["foo", "bar"], "SecurityGroupIds": ["baz"]}
original_args = {
"job_name": JOB_NAME,
"initial_instance_count": INITIAL_INSTANCE_COUNT,
"instance_type": INSTANCE_TYPE,
"image_uri": DEPLOY_IMAGE,
"role": DEPLOY_ROLE,
"name": NEW_ENTITY_NAME,
"model_environment_vars": ENV_VARS,
"vpc_config_override": vpc_config_override,
"accelerator_type": ACCELERATOR_TYPE,
"wait": False,
}
returned_name = sagemaker_session.endpoint_from_job(**original_args)
expected_args = original_args.copy()
expected_args.pop("job_name")
expected_args["model_s3_location"] = S3_MODEL_ARTIFACTS
expected_args["model_vpc_config"] = expected_args.pop("vpc_config_override")
expected_args["data_capture_config"] = None
sagemaker_session.endpoint_from_model_data.assert_called_once_with(**expected_args)
assert returned_name == ENDPOINT_FROM_MODEL_RETURNED_NAME
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