def test_ray_tf(sagemaker_session, ray_tensorflow_latest_version, cpu_instance_type): source_dir = os.path.join(DATA_DIR, "ray_cartpole") cartpole = "train_ray.py" estimator = RLEstimator( entry_point=cartpole, source_dir=source_dir, toolkit=RLToolkit.RAY, framework=RLFramework.TENSORFLOW, toolkit_version=ray_tensorflow_latest_version, sagemaker_session=sagemaker_session, role="SageMakerRole", instance_type=cpu_instance_type, instance_count=1, ) job_name = unique_name_from_base("test-ray-tf") with timeout(minutes=15): estimator.fit(job_name=job_name) with pytest.raises(NotImplementedError) as e: estimator.deploy(1, cpu_instance_type) assert "Automatic deployment of Ray models is not currently available" in str( e.value)
def test_ray_tf(sagemaker_session, rl_ray_full_version): source_dir = os.path.join(DATA_DIR, 'ray_cartpole') cartpole = 'train_ray.py' estimator = RLEstimator(entry_point=cartpole, source_dir=source_dir, toolkit=RLToolkit.RAY, framework=RLFramework.TENSORFLOW, toolkit_version=rl_ray_full_version, sagemaker_session=sagemaker_session, role='SageMakerRole', train_instance_type=CPU_INSTANCE, train_instance_count=1) with timeout(minutes=15): estimator.fit() with pytest.raises(NotImplementedError) as e: estimator.deploy(1, CPU_INSTANCE) assert 'Automatic deployment of Ray models is not currently available' in str(e.value)