def test_get_pipeline_input_node():
# 1) No InputNode found
ray_dag = combine.bind(1, 2)
serve_dag = ray_dag.apply_recursive(transform_ray_dag_to_serve_dag)
with pytest.raises(AssertionError,
match="There should be one and only one InputNode"):
get_pipeline_input_node(serve_dag)
# 2) More than one InputNode found
with InputNode() as dag_input:
a = combine.bind(dag_input[0], dag_input[1])
with InputNode() as dag_input_2:
b = combine.bind(dag_input_2[0], dag_input_2[1])
ray_dag = combine.bind(a, b)
with pytest.raises(AssertionError,
match="Each DAG should only have one unique InputNode"):
serve_dag = ray_dag.apply_recursive(transform_ray_dag_to_serve_dag)
get_pipeline_input_node(serve_dag)
def test_get_pipeline_input_node():
# 1) No PipelineInputNode found
ray_dag = combine.bind(1, 2)
serve_dag = ray_dag.apply_recursive(transform_ray_dag_to_serve_dag)
with pytest.raises(
AssertionError,
match="There should be one and only one PipelineInputNode"):
get_pipeline_input_node(serve_dag)
# 2) More than one PipelineInputNode found
with PipelineInputNode(preprocessor=request_to_data_int) as dag_input:
a = combine.bind(dag_input[0], dag_input[1])
with PipelineInputNode(preprocessor=request_to_data_int) as dag_input_2:
b = combine.bind(dag_input_2[0], dag_input_2[1])
ray_dag = combine.bind(a, b)
serve_dag = ray_dag.apply_recursive(transform_ray_dag_to_serve_dag)
with pytest.raises(
AssertionError,
match="There should be one and only one PipelineInputNode"):
get_pipeline_input_node(serve_dag)
# 3) User forgot to change InputNode to PipelineInputNode
with InputNode() as dag_input:
ray_dag = combine.bind(dag_input[0], dag_input[1])
serve_dag = ray_dag.apply_recursive(transform_ray_dag_to_serve_dag)
with pytest.raises(
ValueError,
match="Please change Ray DAG InputNode to PipelineInputNode"):
get_pipeline_input_node(serve_dag)
def test_get_pipeline_input_node():
# 1) No InputNode found
ray_dag = combine.bind(1, 2)
with DeploymentNameGenerator() as deployment_name_generator:
serve_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, deployment_name_generator))
with pytest.raises(AssertionError,
match="There should be one and only one InputNode"):
get_pipeline_input_node(serve_dag)
# 2) More than one InputNode found
with InputNode() as dag_input:
a = combine.bind(dag_input[0], dag_input[1])
with InputNode() as dag_input_2:
b = combine.bind(dag_input_2[0], dag_input_2[1])
ray_dag = combine.bind(a, b)
with pytest.raises(AssertionError,
match="Each DAG should only have one unique InputNode"):
with DeploymentNameGenerator() as deployment_name_generator:
serve_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, deployment_name_generator))
get_pipeline_input_node(serve_dag)
def build(ray_dag_root_node: DAGNode):
"""Do all the DAG transformation, extraction and generation needed to
produce a runnable and deployable serve pipeline application from a valid
DAG authored with Ray DAG API.
This should be the only user facing API that user interacts with.
Assumptions:
Following enforcements are only applied at generating and applying
pipeline artifact, but not blockers for local development and testing.
- ALL args and kwargs used in DAG building should be JSON serializable.
This means in order to ensure your pipeline application can run on
a remote cluster potentially with different runtime environment,
among all options listed:
1) binding in-memory objects
2) Rely on pickling
3) Enforce JSON serialibility on all args used
We believe both 1) & 2) rely on unstable in-memory objects or
cross version pickling / closure capture, where JSON serialization
provides the right contract needed for proper deployment.
- ALL classes and methods used should be visible on top of the file and
importable via a fully qualified name. Thus no inline class or
function definitions should be used.
Args:
ray_dag_root_node: DAGNode acting as root of a Ray authored DAG. It
should be executable via `ray_dag_root_node.execute(user_input)`
and should have `PipelineInputNode` in it.
Returns:
app: The Ray Serve application object that wraps all deployments needed
along with ingress deployment for an e2e runnable serve pipeline,
accessible via python .remote() call and HTTP.
Examples:
>>> with ServeInputNode(preprocessor=request_to_data_int) as dag_input:
... m1 = Model.bind(1)
... m2 = Model.bind(2)
... m1_output = m1.forward.bind(dag_input[0])
... m2_output = m2.forward.bind(dag_input[1])
... ray_dag = ensemble.bind(m1_output, m2_output)
Assuming we have non-JSON serializable or inline defined class or
function in local pipeline development.
>>> app = serve.pipeline.build(ray_dag) # This works
>>> handle = app.deploy()
>>> # This also works, we're simply executing the transformed serve_dag.
>>> ray.get(handle.remote(data)
>>> # This will fail where enforcements are applied.
>>> deployment_yaml = app.to_yaml()
"""
serve_root_dag = ray_dag_root_node.apply_recursive(transform_ray_dag_to_serve_dag)
deployments = extract_deployments_from_serve_dag(serve_root_dag)
pipeline_input_node = get_pipeline_input_node(serve_root_dag)
ingress_deployment = get_ingress_deployment(serve_root_dag, pipeline_input_node)
deployments.insert(0, ingress_deployment)
# TODO (jiaodong): Call into Application once Shreyas' PR is merged
# TODO (jiaodong): Apply enforcements at serve app to_yaml level
return deployments