b38e4ca3161e36d60df57dd523b2ca0119f890c8,tests/integ/test_mxnet_train.py,,test_deploy_model,#Any#Any#,63

Before Change

def test_deploy_model(mxnet_training_job, sagemaker_session):
    with timeout(minutes=15):
        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, sagemaker_session=sagemaker_session)
        predictor = model.deploy(1, "ml.m4.xlarge", endpoint_name="test-mxnet-deploy-model-{}".format(int(time.time())))
        try:
            data = numpy.zeros(shape=(1, 1, 28, 28))
            predictor.predict(data)
        finally:
            sagemaker_session.delete_endpoint(predictor.endpoint)

After Change

def test_deploy_model(mxnet_training_job, sagemaker_session):
    endpoint_name = "test-mxnet-deploy-model-{}".format(int(time.time()))

    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=15):
        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, sagemaker_session=sagemaker_session)
        predictor = model.deploy(1, "ml.m4.xlarge", endpoint_name=endpoint_name)

        data = numpy.zeros(shape=(1, 1, 28, 28))
        predictor.predict(data)

Instances