def METHOD_NAME(sagemaker_session, cpu_instance_type, training_set):
    job_name = unique_name_from_base("fm")
    with timeout(minutes=5):
        fm = FactorizationMachines(
            role="SageMakerRole",
            instance_count=1,
            instance_type=cpu_instance_type,
            num_factors=10,
            predictor_type="regressor",
            epochs=2,
            clip_gradient=1e2,
            eps=0.001,
            rescale_grad=1.0 / 100,
            sagemaker_session=sagemaker_session,
        )
        # training labels must be 'float32'
        fm.fit(
            fm.record_set(training_set[0][:200], training_set[1][:200].astype("float32")),
            job_name=job_name,
            wait=False,
        )
        print("Detached from training job. Will re-attach in 20 seconds")
        time.sleep(20)
        print("attaching now...")
    with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
        estimator = FactorizationMachines.attach(
            training_job_name=job_name, sagemaker_session=sagemaker_session
        )
        model = FactorizationMachinesModel(
            estimator.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session
        )
        predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
        result = predictor.predict(training_set[0][:10])
        assert len(result) == 10
        for record in result:
            assert record.label["score"] is not None