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)
