def dag_to_dot(dag: DAGNode):
"""Create a Dot graph from dag.
TODO(lchu):
1. add more Dot configs in kwargs,
e.g. rankdir, alignment, etc.
2. add more contents to graph,
e.g. args, kwargs and options of each node
"""
# Step 0: check dependencies and init graph
check_pydot_and_graphviz()
import pydot
graph = pydot.Dot(rankdir="LR")
# Step 1: generate unique name for each node in dag
nodes, edges = get_nodes_and_edges(dag)
name_generator = DAGNodeNameGenerator()
node_names = {}
for node in nodes:
node_names[node] = name_generator.get_node_name(node)
# Step 2: create graph with all the edges
for edge in edges:
graph.add_edge(pydot.Edge(node_names[edge[0]], node_names[edge[1]]))
# if there is only one node
if len(nodes) == 1 and len(edges) == 0:
graph.add_node(pydot.Node(node_names[nodes[0]]))
return graph
def transform_ray_dag_to_serve_dag(
dag_node: DAGNode, node_name_generator: DAGNodeNameGenerator
):
"""
Transform a Ray DAG to a Serve DAG. Map ClassNode to DeploymentNode with
ray decorated body passed in, and ClassMethodNode to DeploymentMethodNode.
"""
if isinstance(dag_node, ClassNode):
deployment_name = node_name_generator.get_node_name(dag_node)
return DeploymentNode(
dag_node._body,
deployment_name,
dag_node.get_args(),
dag_node.get_kwargs(),
dag_node.get_options(),
# TODO: (jiaodong) Support .options(metadata=xxx) for deployment
other_args_to_resolve=dag_node.get_other_args_to_resolve(),
)
elif isinstance(dag_node, ClassMethodNode):
other_args_to_resolve = dag_node.get_other_args_to_resolve()
# TODO: (jiaodong) Need to capture DAGNodes in the parent node
parent_deployment_node = other_args_to_resolve[PARENT_CLASS_NODE_KEY]
return DeploymentMethodNode(
parent_deployment_node._deployment,
dag_node._method_name,
dag_node.get_args(),
dag_node.get_kwargs(),
dag_node.get_options(),
other_args_to_resolve=dag_node.get_other_args_to_resolve(),
)
elif isinstance(
dag_node,
FunctionNode
# TODO (jiaodong): We do not convert ray function to deployment function
# yet, revisit this later
) and dag_node.get_other_args_to_resolve().get("is_from_serve_deployment"):
deployment_name = node_name_generator.get_node_name(dag_node)
return DeploymentFunctionNode(
dag_node._body,
deployment_name,
dag_node.get_args(),
dag_node.get_kwargs(),
dag_node.get_options(),
other_args_to_resolve=dag_node.get_other_args_to_resolve(),
)
else:
# TODO: (jiaodong) Support FunctionNode or leave it as ray task
return dag_node
def test_multi_instantiation_class_nested_deployment_arg(serve_instance):
"""
Test we can pass deployments with **nested** init_arg or init_kwarg,
instantiated multiple times for the same class, and we can still correctly
replace args with deployment handle and parse correct deployment instances.
"""
ray_dag, _ = get_multi_instantiation_class_nested_deployment_arg_dag()
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
print(f"Serve DAG: \n{serve_root_dag}")
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 3
# Ensure Deployments with other deployment nodes in init arg are replaced
# with correct handle
combine_deployment = deployments[2]
init_arg_handle = combine_deployment.init_args[0]
assert isinstance(init_arg_handle, RayServeLazySyncHandle)
assert init_arg_handle.deployment_name == "Model"
init_kwarg_handle = combine_deployment.init_kwargs["m2"][NESTED_HANDLE_KEY]
assert isinstance(init_kwarg_handle, RayServeLazySyncHandle)
assert init_kwarg_handle.deployment_name == "Model_1"
for deployment in deployments:
deployment.deploy()
_validate_consistent_python_output(deployments[2],
ray_dag,
"Combine",
input=1,
output=5)
def _test_deployment_json_serde_helper(ray_dag: DAGNode,
input=None,
expected_num_deployments=None):
"""Helper function for DeploymentNode and DeploymentMethodNode calls, checks
the following:
1) Transform ray dag to serve dag, and ensure serve dag is JSON
serializable.
2) Serve dag JSON and be deserialized back to serve dag.
3) Deserialized serve dag can extract correct number and definition of
serve deployments.
"""
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
json_serialized = json.dumps(serve_root_dag, cls=DAGNodeEncoder)
deserialized_serve_root_dag_node = json.loads(
json_serialized, object_hook=dagnode_from_json)
deserialized_deployments = extract_deployments_from_serve_dag(
deserialized_serve_root_dag_node)
assert len(deserialized_deployments) == expected_num_deployments
# Deploy deserilized version to ensure JSON serde correctness
for model in deserialized_deployments:
model.deploy()
if input is None:
assert ray.get(ray_dag.execute()) == ray.get(serve_root_dag.execute())
else:
assert ray.get(ray_dag.execute(input)) == ray.get(
serve_root_dag.execute(input))
return serve_root_dag, deserialized_serve_root_dag_node
def test_unique_name_reset_upon_build(serve_instance):
ray_dag, _ = get_multi_instantiation_class_deployment_in_init_args_dag()
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert deployments[0].name == "Model"
assert deployments[1].name == "Model_1"
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
# Assert we don't keep increasing suffix id between build() calls
assert deployments[0].name == "Model"
assert deployments[1].name == "Model_1"
def test_func_class_with_class_method_dag(serve_instance):
ray_dag, _ = get_func_class_with_class_method_dag()
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 2
for deployment in deployments:
deployment.deploy()
assert ray.get(ray_dag.execute(1, 2, 3)) == 8
assert ray.get(serve_root_dag.execute(1, 2, 3)) == 8
def test_simple_single_class(serve_instance):
ray_dag, _ = get_simple_class_with_class_method_dag()
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 1
deployments[0].deploy()
_validate_consistent_python_output(deployments[0],
ray_dag,
"Model",
input=1,
output=0.6)
def test_get_pipeline_input_node():
# 1) No InputNode found
ray_dag = combine.bind(1, 2)
with DAGNodeNameGenerator() as node_name_generator:
serve_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_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 DAGNodeNameGenerator() as node_name_generator:
serve_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
get_pipeline_input_node(serve_dag)
def test_single_class_with_invalid_deployment_options(serve_instance):
with InputNode() as dag_input:
model = Model.options(name="my_deployment").bind(2, ratio=0.3)
ray_dag = model.forward.bind(dag_input)
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 1
with pytest.raises(
ValueError,
match="Specifying 'name' in ray_actor_options is not allowed"):
deployments[0].deploy()
def test_build_simple_func_dag(serve_instance):
ray_dag, _ = get_simple_func_dag()
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
serve_root_dag = ray_dag.apply_recursive(transform_ray_dag_to_serve_dag)
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 1
deployments[0].deploy()
deployment_handle = deployments[0].get_handle()
# Because the bound kwarg is stored in dag, so it has to be explicitly passed in.
assert ray.get(deployment_handle.remote(1, 2, kwargs_output=1)) == 4
assert ray.get(ray_dag.execute([1, 2])) == 4
def test_single_class_with_valid_ray_options(serve_instance):
with InputNode() as dag_input:
model = Model.options(num_cpus=1, memory=1000).bind(2, ratio=0.3)
ray_dag = model.forward.bind(dag_input)
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 1
deployments[0].deploy()
_validate_consistent_python_output(deployments[0],
ray_dag,
deployments[0].name,
input=1,
output=0.6)
deployment = serve.get_deployment(deployments[0].name)
assert deployment.ray_actor_options.get("num_cpus") == 1
assert deployment.ray_actor_options.get("memory") == 1000
assert deployment.ray_actor_options.get("runtime_env") == {}
def test_shared_deployment_handle(serve_instance):
"""
Test we can re-use the same deployment handle multiple times or in
multiple places, without incorrectly parsing duplicated deployments.
"""
ray_dag, _ = get_shared_deployment_handle_dag()
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
print(f"Serve DAG: \n{serve_root_dag}")
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 2
for deployment in deployments:
deployment.deploy()
_validate_consistent_python_output(deployments[1],
ray_dag,
"Combine",
input=1,
output=4)
def test_multi_instantiation_class_deployment_in_init_args(serve_instance):
"""
Test we can pass deployments as init_arg or init_kwarg, instantiated
multiple times for the same class, and we can still correctly replace
args with deployment handle and parse correct deployment instances.
"""
ray_dag, _ = get_multi_instantiation_class_deployment_in_init_args_dag()
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, node_name_generator))
print(f"Serve DAG: \n{serve_root_dag}")
deployments = extract_deployments_from_serve_dag(serve_root_dag)
assert len(deployments) == 3
for deployment in deployments:
deployment.deploy()
_validate_consistent_python_output(deployments[2],
ray_dag,
"Combine",
input=1,
output=5)
def build(ray_dag_root_node: DAGNode) -> List[Deployment]:
"""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 `InputNode` in it.
Returns:
deployments: All deployments needed for an e2e runnable serve pipeline,
accessible via python .remote() call.
Examples:
>>> with InputNode() 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.
>>> from ray.serve.api import build as build_app
>>> deployments = build_app(ray_dag) # it can be method node
>>> deployments = build_app(m1) # or just a regular node.
"""
with DAGNodeNameGenerator() as node_name_generator:
serve_root_dag = ray_dag_root_node.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
)
deployments = extract_deployments_from_serve_dag(serve_root_dag)
# After Ray DAG is transformed to Serve DAG with deployments and their init
# args filled, generate a minimal weight executor serve dag for perf
serve_executor_root_dag = serve_root_dag.apply_recursive(
transform_serve_dag_to_serve_executor_dag
)
root_driver_deployment = deployments[-1]
new_driver_deployment = generate_executor_dag_driver_deployment(
serve_executor_root_dag, root_driver_deployment
)
# Replace DAGDriver deployment with executor DAGDriver deployment
deployments[-1] = new_driver_deployment
# Validate and only expose HTTP for the endpoint
deployments_with_http = process_ingress_deployment_in_serve_dag(deployments)
return deployments_with_http
def ray_dag_to_serve_dag(dag: DAGNode):
with DAGNodeNameGenerator() as deployment_name_generator:
serve_dag = dag.apply_recursive(
lambda node: transform_ray_dag_to_serve_dag(
node, deployment_name_generator))
return serve_dag