enable ei integ tests (#527)

Author: ChoiByungWook

tests/conftest.py

@@ -95,6 +95,11 @@ def mxnet_version(request):
     return request.param
 
 
+@pytest.fixture(scope='module', params=['1.3', '1.3.0'])
+def ei_mxnet_version(request):
+    return request.param
+
+
 @pytest.fixture(scope='module', params=['0.4', '0.4.0'])
 def pytorch_version(request):
     return request.param
@@ -112,6 +117,11 @@ def tf_version(request):
     return request.param
 
 
+@pytest.fixture(scope='module', params=['1.11', '1.11.0'])
+def ei_tf_version(request):
+    return request.param
+
+
 @pytest.fixture(scope='module', params=['0.10.1', '0.10.1', '0.11', '0.11.0'])
 def rl_coach_tf_version(request):
     return request.param

tests/integ/test_mxnet_train.py

@@ -71,16 +71,16 @@ def test_deploy_model(mxnet_training_job, sagemaker_session):
         predictor.predict(data)
 
 
-@pytest.mark.skip
-def test_deploy_model_with_accelerator(mxnet_training_job, sagemaker_session):
+def test_deploy_model_with_accelerator(mxnet_training_job, sagemaker_session, ei_mxnet_version):
     endpoint_name = 'test-mxnet-deploy-model-ei-{}'.format(sagemaker_timestamp())
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=mxnet_training_job)
         model_data = desc['ModelArtifacts']['S3ModelArtifacts']
         script_path = os.path.join(DATA_DIR, 'mxnet_mnist', 'mnist.py')
         model = MXNetModel(model_data, 'SageMakerRole', entry_point=script_path,
-                           py_version=PYTHON_VERSION, sagemaker_session=sagemaker_session)
+                           framework_version=ei_mxnet_version, py_version=PYTHON_VERSION,
+                           sagemaker_session=sagemaker_session)
         predictor = model.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name, accelerator_type='ml.eia1.medium')
 
         data = numpy.zeros(shape=(1, 1, 28, 28))

tests/integ/test_tf.py

@@ -76,9 +76,8 @@ def test_deploy_model(sagemaker_session, tf_training_job):
         assert dict_result == list_result
 
 
-@pytest.mark.skip
 @pytest.mark.skipif(PYTHON_VERSION != 'py2', reason="TensorFlow image supports only python 2.")
-def test_deploy_model_with_accelerator(sagemaker_session, tf_training_job):
+def test_deploy_model_with_accelerator(sagemaker_session, tf_training_job, ei_tf_version):
     endpoint_name = 'test-tf-deploy-model-ei-{}'.format(sagemaker_timestamp())
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
@@ -87,7 +86,7 @@ def test_deploy_model_with_accelerator(sagemaker_session, tf_training_job):
 
         script_path = os.path.join(DATA_DIR, 'iris', 'iris-dnn-classifier.py')
         model = TensorFlowModel(model_data, 'SageMakerRole', entry_point=script_path,
-                                sagemaker_session=sagemaker_session)
+                                framework_version=ei_tf_version, sagemaker_session=sagemaker_session)
 
         json_predictor = model.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge',
                                       endpoint_name=endpoint_name, accelerator_type='ml.eia1.medium')