infra: move Model.deploy unit tests to separate file (#1425)

Author: laurenyu

tests/unit/sagemaker/model/test_deploy.py

@@ -0,0 +1,269 @@
+# Copyright 2017-2020 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 copy
+
+import pytest
+from mock import Mock, patch
+
+import sagemaker
+from sagemaker.model import Model
+
+MODEL_DATA = "s3://bucket/model.tar.gz"
+MODEL_IMAGE = "mi"
+TIMESTAMP = "2017-10-10-14-14-15"
+MODEL_NAME = "{}-{}".format(MODEL_IMAGE, TIMESTAMP)
+
+INSTANCE_COUNT = 2
+INSTANCE_TYPE = "ml.c4.4xlarge"
+ROLE = "some-role"
+
+BASE_PRODUCTION_VARIANT = {
+    "ModelName": MODEL_NAME,
+    "InstanceType": INSTANCE_TYPE,
+    "InitialInstanceCount": INSTANCE_COUNT,
+    "VariantName": "AllTraffic",
+    "InitialVariantWeight": 1,
+}
+
+
+@pytest.fixture
+def sagemaker_session():
+    return Mock()
+
+
+@patch("sagemaker.production_variant")
+@patch("sagemaker.model.Model.prepare_container_def")
+@patch("sagemaker.utils.name_from_image")
+def test_deploy(name_from_image, prepare_container_def, production_variant, sagemaker_session):
+    name_from_image.return_value = MODEL_NAME
+    production_variant.return_value = BASE_PRODUCTION_VARIANT
+
+    container_def = {"Image": MODEL_IMAGE, "Environment": {}, "ModelDataUrl": MODEL_DATA}
+    prepare_container_def.return_value = container_def
+
+    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
+    model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
+
+    name_from_image.assert_called_with(MODEL_IMAGE)
+    prepare_container_def.assert_called_with(INSTANCE_TYPE, accelerator_type=None)
+    production_variant.assert_called_with(
+        MODEL_NAME, INSTANCE_TYPE, INSTANCE_COUNT, accelerator_type=None
+    )
+
+    sagemaker_session.create_model.assert_called_with(
+        MODEL_NAME, ROLE, container_def, vpc_config=None, enable_network_isolation=False, tags=None
+    )
+
+    sagemaker_session.endpoint_from_production_variants.assert_called_with(
+        name=MODEL_NAME,
+        production_variants=[BASE_PRODUCTION_VARIANT],
+        tags=None,
+        kms_key=None,
+        wait=True,
+        data_capture_config_dict=None,
+    )
+
+
+@patch("sagemaker.model.Model._create_sagemaker_model")
+@patch("sagemaker.production_variant")
+def test_deploy_accelerator_type(production_variant, create_sagemaker_model, sagemaker_session):
+    model = Model(
+        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+    )
+
+    accelerator_type = "ml.eia.medium"
+
+    production_variant_result = copy.deepcopy(BASE_PRODUCTION_VARIANT)
+    production_variant_result["AcceleratorType"] = accelerator_type
+    production_variant.return_value = production_variant_result
+
+    model.deploy(
+        instance_type=INSTANCE_TYPE,
+        initial_instance_count=INSTANCE_COUNT,
+        accelerator_type=accelerator_type,
+    )
+
+    create_sagemaker_model.assert_called_with(INSTANCE_TYPE, accelerator_type, None)
+    production_variant.assert_called_with(
+        MODEL_NAME, INSTANCE_TYPE, INSTANCE_COUNT, accelerator_type=accelerator_type
+    )
+
+    sagemaker_session.endpoint_from_production_variants.assert_called_with(
+        name=MODEL_NAME,
+        production_variants=[production_variant_result],
+        tags=None,
+        kms_key=None,
+        wait=True,
+        data_capture_config_dict=None,
+    )
+
+
+@patch("sagemaker.utils.name_from_image", Mock())
+@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
+@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
+def test_deploy_endpoint_name(sagemaker_session):
+    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE, sagemaker_session=sagemaker_session)
+
+    endpoint_name = "blah"
+    model.deploy(
+        endpoint_name=endpoint_name,
+        instance_type=INSTANCE_TYPE,
+        initial_instance_count=INSTANCE_COUNT,
+    )
+
+    sagemaker_session.endpoint_from_production_variants.assert_called_with(
+        name=endpoint_name,
+        production_variants=[BASE_PRODUCTION_VARIANT],
+        tags=None,
+        kms_key=None,
+        wait=True,
+        data_capture_config_dict=None,
+    )
+
+
+@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
+@patch("sagemaker.model.Model._create_sagemaker_model")
+def test_deploy_tags(create_sagemaker_model, production_variant, sagemaker_session):
+    model = Model(
+        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+    )
+
+    tags = [{"Key": "ModelName", "Value": "TestModel"}]
+    model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, tags=tags)
+
+    create_sagemaker_model.assert_called_with(INSTANCE_TYPE, None, tags)
+    sagemaker_session.endpoint_from_production_variants.assert_called_with(
+        name=MODEL_NAME,
+        production_variants=[BASE_PRODUCTION_VARIANT],
+        tags=tags,
+        kms_key=None,
+        wait=True,
+        data_capture_config_dict=None,
+    )
+
+
+@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
+@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
+def test_deploy_kms_key(production_variant, sagemaker_session):
+    model = Model(
+        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+    )
+
+    key = "some-key-arn"
+    model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, kms_key=key)
+
+    sagemaker_session.endpoint_from_production_variants.assert_called_with(
+        name=MODEL_NAME,
+        production_variants=[BASE_PRODUCTION_VARIANT],
+        tags=None,
+        kms_key=key,
+        wait=True,
+        data_capture_config_dict=None,
+    )
+
+
+@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
+@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
+def test_deploy_async(production_variant, sagemaker_session):
+    model = Model(
+        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+    )
+
+    model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT, wait=False)
+
+    sagemaker_session.endpoint_from_production_variants.assert_called_with(
+        name=MODEL_NAME,
+        production_variants=[BASE_PRODUCTION_VARIANT],
+        tags=None,
+        kms_key=None,
+        wait=False,
+        data_capture_config_dict=None,
+    )
+
+
+@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
+@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
+def test_deploy_data_capture_config(production_variant, sagemaker_session):
+    model = Model(
+        MODEL_DATA, MODEL_IMAGE, role=ROLE, name=MODEL_NAME, sagemaker_session=sagemaker_session
+    )
+
+    data_capture_config = Mock()
+    data_capture_config_dict = {"EnableCapture": True}
+    data_capture_config._to_request_dict.return_value = data_capture_config_dict
+    model.deploy(
+        instance_type=INSTANCE_TYPE,
+        initial_instance_count=INSTANCE_COUNT,
+        data_capture_config=data_capture_config,
+    )
+
+    data_capture_config._to_request_dict.assert_called_with()
+    sagemaker_session.endpoint_from_production_variants.assert_called_with(
+        name=MODEL_NAME,
+        production_variants=[BASE_PRODUCTION_VARIANT],
+        tags=None,
+        kms_key=None,
+        wait=True,
+        data_capture_config_dict=data_capture_config_dict,
+    )
+
+
+@patch("sagemaker.session.Session")
+@patch("sagemaker.local.LocalSession")
+def test_deploy_creates_correct_session(local_session, session):
+    # We expect a LocalSession when deploying to instance_type = 'local'
+    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
+    model.deploy(endpoint_name="blah", instance_type="local", initial_instance_count=1)
+    assert model.sagemaker_session == local_session.return_value
+
+    # We expect a real Session when deploying to instance_type != local/local_gpu
+    model = Model(MODEL_DATA, MODEL_IMAGE, role=ROLE)
+    model.deploy(
+        endpoint_name="remote_endpoint", instance_type="ml.m4.4xlarge", initial_instance_count=2
+    )
+    assert model.sagemaker_session == session.return_value
+
+
+def test_deploy_no_role(sagemaker_session):
+    model = Model(MODEL_DATA, MODEL_IMAGE, sagemaker_session=sagemaker_session)
+
+    with pytest.raises(ValueError, match="Role can not be null for deploying a model"):
+        model.deploy(instance_type=INSTANCE_TYPE, initial_instance_count=INSTANCE_COUNT)
+
+
+@patch("sagemaker.model.Model._create_sagemaker_model", Mock())
+@patch("sagemaker.predictor.RealTimePredictor._get_endpoint_config_name", Mock())
+@patch("sagemaker.predictor.RealTimePredictor._get_model_names", Mock())
+@patch("sagemaker.production_variant", return_value=BASE_PRODUCTION_VARIANT)
+def test_deploy_predictor_cls(production_variant, sagemaker_session):
+    model = Model(
+        MODEL_DATA,
+        MODEL_IMAGE,
+        role=ROLE,
+        name=MODEL_NAME,
+        predictor_cls=sagemaker.predictor.RealTimePredictor,
+        sagemaker_session=sagemaker_session,
+    )
+
+    endpoint_name = "foo"
+    predictor = model.deploy(
+        instance_type=INSTANCE_TYPE,
+        initial_instance_count=INSTANCE_COUNT,
+        endpoint_name=endpoint_name,
+    )
+
+    assert isinstance(predictor, sagemaker.predictor.RealTimePredictor)
+    assert predictor.endpoint == endpoint_name
+    assert predictor.sagemaker_session == sagemaker_session