[TEST] fix sagemaker e2e test issue with scikit-learn new release (#692)

* pin sklearn version and update deployment name * use SKLearn artifact and auto pip
bentoml · May 18, 2020 · 37505a2 · 37505a2
1 parent 838d31c
commit 37505a2
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diff --git a/e2e_tests/aws_sagemaker/test_sagemaker_deployment.py b/e2e_tests/aws_sagemaker/test_sagemaker_deployment.py
@@ -16,7 +16,7 @@
 def test_sagemaker_deployment(iris_clf_service):
 
     random_hash = uuid.uuid4().hex[:6]
-    deployment_name = f'tests-lambda-e2e-{random_hash}'
+    deployment_name = f'tests-sagemaker-e2e-{random_hash}'
     region = 'us-west-2'
     create_deployment_command = [
         'bentoml',

diff --git a/e2e_tests/iris_classifier_example.py b/e2e_tests/iris_classifier_example.py
@@ -1,10 +1,10 @@
 from bentoml import BentoService, api, env, artifacts
-from bentoml.artifact import PickleArtifact
+from bentoml.artifact import SklearnModelArtifact
 from bentoml.handlers import DataframeHandler
 
 
-@env(pip_dependencies=['scikit-learn'])
-@artifacts([PickleArtifact('clf')])
+@env(auto_pip_dependencies=True)
+@artifacts([SklearnModelArtifact('clf')])
 class IrisClassifier(BentoService):
     @api(DataframeHandler)
     def predict(self, df):