mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
[Deployment Graph] Move files out of pipeline folder (#25630)
This commit is contained in:
Jiao
GitHub
parent
2546fbf99d
commit
6b9b1f135b
25 changed files with 142 additions and 169 deletions
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@ -46,9 +46,9 @@ class _PyObjScanner(ray.cloudpickle.CloudPickler):
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from ray.experimental.dag.function_node import FunctionNode
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from ray.experimental.dag.class_node import ClassNode, ClassMethodNode
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from ray.experimental.dag.input_node import InputNode, InputAttributeNode
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from ray.serve.pipeline.deployment_node import DeploymentNode
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from ray.serve.pipeline.deployment_method_node import DeploymentMethodNode
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from ray.serve.pipeline.deployment_function_node import DeploymentFunctionNode
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from ray.serve.deployment_node import DeploymentNode
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from ray.serve.deployment_method_node import DeploymentMethodNode
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from ray.serve.deployment_function_node import DeploymentFunctionNode
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from ray.serve.deployment_executor_node import DeploymentExecutorNode
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from ray.serve.deployment_method_executor_node import (
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DeploymentMethodExecutorNode,
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@ -489,20 +489,18 @@ py_test(
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deps = [":serve_lib"]
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)
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pipeline_tests_srcs = glob(["pipeline/tests/**/*.py"])
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py_test(
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name = "test_deployment_node",
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size = "medium",
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srcs = pipeline_tests_srcs,
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srcs = serve_tests_srcs,
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tags = ["exclusive", "team:serve"],
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deps = [":serve_lib"],
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)
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py_test(
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name = "test_generate",
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name = "test_deployment_graph_build",
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size = "medium",
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srcs = pipeline_tests_srcs,
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srcs = serve_tests_srcs,
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tags = ["exclusive", "team:serve"],
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deps = [":serve_lib"],
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)
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@ -518,7 +516,7 @@ py_test(
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py_test(
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name = "test_json_serde",
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size = "medium",
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srcs = pipeline_tests_srcs,
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srcs = serve_tests_srcs,
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tags = ["exclusive", "team:serve"],
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deps = [":serve_lib"],
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)
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@ -50,7 +50,7 @@ from ray.serve.exceptions import RayServeException
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from ray.serve.handle import RayServeHandle
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from ray.serve.http_util import ASGIHTTPSender, make_fastapi_class_based_view
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from ray.serve.logging_utils import LoggingContext
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from ray.serve.pipeline.api import (
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from ray.serve.deployment_graph_build import (
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build as pipeline_build,
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get_and_validate_ingress_deployment,
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)
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@ -44,7 +44,7 @@ class RayServeDAGHandle:
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# NOTE: There's nothing user can do about these warnings, we should hide it.
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with _mute_sync_handle_warnings():
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if self.dag_node is None:
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from ray.serve.pipeline.json_serde import dagnode_from_json
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from ray.serve.json_serde import dagnode_from_json
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self.dag_node = json.loads(
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self.dag_node_json, object_hook=dagnode_from_json
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@ -2,6 +2,16 @@ import json
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from typing import List
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from collections import OrderedDict
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from ray.serve.deployment import Deployment
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from ray.serve.deployment_graph import RayServeDAGHandle
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from ray.serve.deployment_method_node import DeploymentMethodNode
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from ray.serve.deployment_node import DeploymentNode
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from ray.serve.deployment_function_node import DeploymentFunctionNode
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from ray.serve.deployment_executor_node import DeploymentExecutorNode
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from ray.serve.deployment_method_executor_node import DeploymentMethodExecutorNode
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from ray.serve.deployment_function_executor_node import DeploymentFunctionExecutorNode
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from ray.serve.json_serde import DAGNodeEncoder
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from ray.experimental.dag import (
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DAGNode,
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ClassNode,
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@ -11,15 +21,106 @@ from ray.experimental.dag import (
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from ray.experimental.dag.function_node import FunctionNode
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from ray.experimental.dag.input_node import InputNode
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from ray.experimental.dag.utils import DAGNodeNameGenerator
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from ray.serve.deployment import Deployment
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from ray.serve.deployment_graph import RayServeDAGHandle
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from ray.serve.pipeline.deployment_method_node import DeploymentMethodNode
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from ray.serve.pipeline.deployment_node import DeploymentNode
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from ray.serve.pipeline.deployment_function_node import DeploymentFunctionNode
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from ray.serve.deployment_executor_node import DeploymentExecutorNode
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from ray.serve.deployment_method_executor_node import DeploymentMethodExecutorNode
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from ray.serve.deployment_function_executor_node import DeploymentFunctionExecutorNode
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from ray.serve.pipeline.json_serde import DAGNodeEncoder
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def build(ray_dag_root_node: DAGNode) -> List[Deployment]:
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"""Do all the DAG transformation, extraction and generation needed to
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produce a runnable and deployable serve pipeline application from a valid
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DAG authored with Ray DAG API.
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This should be the only user facing API that user interacts with.
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Assumptions:
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Following enforcements are only applied at generating and applying
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pipeline artifact, but not blockers for local development and testing.
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- ALL args and kwargs used in DAG building should be JSON serializable.
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This means in order to ensure your pipeline application can run on
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a remote cluster potentially with different runtime environment,
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among all options listed:
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1) binding in-memory objects
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2) Rely on pickling
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3) Enforce JSON serialibility on all args used
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We believe both 1) & 2) rely on unstable in-memory objects or
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cross version pickling / closure capture, where JSON serialization
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provides the right contract needed for proper deployment.
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- ALL classes and methods used should be visible on top of the file and
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importable via a fully qualified name. Thus no inline class or
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function definitions should be used.
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Args:
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ray_dag_root_node: DAGNode acting as root of a Ray authored DAG. It
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should be executable via `ray_dag_root_node.execute(user_input)`
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and should have `InputNode` in it.
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Returns:
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deployments: All deployments needed for an e2e runnable serve pipeline,
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accessible via python .remote() call.
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Examples:
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>>> with InputNode() as dag_input:
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... m1 = Model.bind(1)
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... m2 = Model.bind(2)
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... m1_output = m1.forward.bind(dag_input[0])
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... m2_output = m2.forward.bind(dag_input[1])
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... ray_dag = ensemble.bind(m1_output, m2_output)
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Assuming we have non-JSON serializable or inline defined class or
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function in local pipeline development.
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>>> from ray.serve.api import build as build_app
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>>> deployments = build_app(ray_dag) # it can be method node
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>>> deployments = build_app(m1) # or just a regular node.
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"""
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with DAGNodeNameGenerator() as node_name_generator:
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serve_root_dag = ray_dag_root_node.apply_recursive(
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lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
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)
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deployments = extract_deployments_from_serve_dag(serve_root_dag)
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# After Ray DAG is transformed to Serve DAG with deployments and their init
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# args filled, generate a minimal weight executor serve dag for perf
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serve_executor_root_dag = serve_root_dag.apply_recursive(
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transform_serve_dag_to_serve_executor_dag
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)
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root_driver_deployment = deployments[-1]
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new_driver_deployment = generate_executor_dag_driver_deployment(
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serve_executor_root_dag, root_driver_deployment
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)
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# Replace DAGDriver deployment with executor DAGDriver deployment
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deployments[-1] = new_driver_deployment
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# Validate and only expose HTTP for the endpoint
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deployments_with_http = process_ingress_deployment_in_serve_dag(deployments)
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return deployments_with_http
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def get_and_validate_ingress_deployment(
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deployments: List[Deployment],
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) -> Deployment:
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"""Validation for http route prefixes for a list of deployments in pipeline.
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Ensures:
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1) One and only one ingress deployment with given route prefix.
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2) All other not ingress deployments should have prefix of None.
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"""
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ingress_deployments = []
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for deployment in deployments:
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if deployment.route_prefix is not None:
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ingress_deployments.append(deployment)
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if len(ingress_deployments) != 1:
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raise ValueError(
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"Only one deployment in an Serve Application or DAG can have "
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f"non-None route prefix. {len(ingress_deployments)} ingress "
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f"deployments found: {ingress_deployments}"
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)
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return ingress_deployments[0]
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def transform_ray_dag_to_serve_dag(
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@ -11,8 +11,8 @@ from ray.serve.deployment_method_executor_node import (
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)
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from ray.serve.handle import RayServeLazySyncHandle
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from ray.serve.pipeline.deployment_method_node import DeploymentMethodNode
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from ray.serve.pipeline.deployment_function_node import DeploymentFunctionNode
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from ray.serve.deployment_method_node import DeploymentMethodNode
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from ray.serve.deployment_function_node import DeploymentFunctionNode
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from ray.experimental.dag.constants import PARENT_CLASS_NODE_KEY
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from ray.experimental.dag.format_utils import get_dag_node_str
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from ray.serve.deployment import Deployment, schema_to_deployment
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@ -1,112 +0,0 @@
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from typing import List
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from ray.experimental.dag.dag_node import DAGNode
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from ray.serve.pipeline.generate import (
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transform_ray_dag_to_serve_dag,
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extract_deployments_from_serve_dag,
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transform_serve_dag_to_serve_executor_dag,
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process_ingress_deployment_in_serve_dag,
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generate_executor_dag_driver_deployment,
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)
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from ray.serve.deployment import Deployment
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from ray.experimental.dag.utils import DAGNodeNameGenerator
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def build(ray_dag_root_node: DAGNode) -> List[Deployment]:
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"""Do all the DAG transformation, extraction and generation needed to
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produce a runnable and deployable serve pipeline application from a valid
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DAG authored with Ray DAG API.
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This should be the only user facing API that user interacts with.
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Assumptions:
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Following enforcements are only applied at generating and applying
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pipeline artifact, but not blockers for local development and testing.
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- ALL args and kwargs used in DAG building should be JSON serializable.
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This means in order to ensure your pipeline application can run on
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a remote cluster potentially with different runtime environment,
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among all options listed:
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1) binding in-memory objects
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2) Rely on pickling
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3) Enforce JSON serialibility on all args used
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We believe both 1) & 2) rely on unstable in-memory objects or
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cross version pickling / closure capture, where JSON serialization
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provides the right contract needed for proper deployment.
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- ALL classes and methods used should be visible on top of the file and
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importable via a fully qualified name. Thus no inline class or
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function definitions should be used.
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Args:
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ray_dag_root_node: DAGNode acting as root of a Ray authored DAG. It
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should be executable via `ray_dag_root_node.execute(user_input)`
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and should have `InputNode` in it.
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Returns:
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deployments: All deployments needed for an e2e runnable serve pipeline,
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accessible via python .remote() call.
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Examples:
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>>> with InputNode() as dag_input:
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... m1 = Model.bind(1)
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... m2 = Model.bind(2)
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... m1_output = m1.forward.bind(dag_input[0])
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... m2_output = m2.forward.bind(dag_input[1])
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... ray_dag = ensemble.bind(m1_output, m2_output)
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Assuming we have non-JSON serializable or inline defined class or
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function in local pipeline development.
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>>> from ray.serve.api import build as build_app
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>>> deployments = build_app(ray_dag) # it can be method node
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>>> deployments = build_app(m1) # or just a regular node.
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"""
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with DAGNodeNameGenerator() as node_name_generator:
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serve_root_dag = ray_dag_root_node.apply_recursive(
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lambda node: transform_ray_dag_to_serve_dag(node, node_name_generator)
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)
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deployments = extract_deployments_from_serve_dag(serve_root_dag)
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# After Ray DAG is transformed to Serve DAG with deployments and their init
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# args filled, generate a minimal weight executor serve dag for perf
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serve_executor_root_dag = serve_root_dag.apply_recursive(
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transform_serve_dag_to_serve_executor_dag
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)
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root_driver_deployment = deployments[-1]
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new_driver_deployment = generate_executor_dag_driver_deployment(
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serve_executor_root_dag, root_driver_deployment
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)
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# Replace DAGDriver deployment with executor DAGDriver deployment
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deployments[-1] = new_driver_deployment
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# Validate and only expose HTTP for the endpoint
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deployments_with_http = process_ingress_deployment_in_serve_dag(deployments)
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return deployments_with_http
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def get_and_validate_ingress_deployment(
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deployments: List[Deployment],
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) -> Deployment:
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"""Validation for http route prefixes for a list of deployments in pipeline.
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Ensures:
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1) One and only one ingress deployment with given route prefix.
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2) All other not ingress deployments should have prefix of None.
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"""
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ingress_deployments = []
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for deployment in deployments:
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if deployment.route_prefix is not None:
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ingress_deployments.append(deployment)
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if len(ingress_deployments) != 1:
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raise ValueError(
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"Only one deployment in an Serve Application or DAG can have "
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f"non-None route prefix. {len(ingress_deployments)} ingress "
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f"deployments found: {ingress_deployments}"
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)
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return ingress_deployments[0]
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@ -1,9 +0,0 @@
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import pytest
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import ray
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from ray.serve.tests.conftest import _shared_serve_instance, serve_instance # noqa
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@pytest.fixture(scope="session")
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def shared_ray_instance():
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yield ray.init(num_cpus=36, _system_config={"task_retry_delay_ms": 50})
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@ -1,5 +0,0 @@
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# Reserved constant used as key in other_args_to_resolve to configure if we
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# return sync or async handle of a deployment.
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# True -> RayServeSyncHandle
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# False -> RayServeHandle
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USE_SYNC_HANDLE_KEY = "__use_sync_handle__"
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@ -1,4 +1,4 @@
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from ray.serve.pipeline.tests.resources.test_modules import (
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from ray.serve.tests.resources.test_modules import (
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Model,
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Combine,
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combine,
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@ -4,18 +4,18 @@ import ray
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from ray import serve
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from ray.experimental.dag import InputNode
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from ray.serve.handle import RayServeLazySyncHandle
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from ray.serve.pipeline.generate import (
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from ray.serve.deployment_graph_build import (
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transform_ray_dag_to_serve_dag,
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extract_deployments_from_serve_dag,
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transform_serve_dag_to_serve_executor_dag,
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get_pipeline_input_node,
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)
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from ray.serve.pipeline.tests.resources.test_modules import (
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from ray.serve.tests.resources.test_modules import (
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Model,
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NESTED_HANDLE_KEY,
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combine,
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)
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from ray.serve.pipeline.tests.resources.test_dags import (
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from ray.serve.tests.resources.test_dags import (
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get_simple_class_with_class_method_dag,
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get_func_class_with_class_method_dag,
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get_multi_instantiation_class_deployment_in_init_args_dag,
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@ -2,7 +2,7 @@ import pytest
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import ray
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from ray import serve
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from ray.serve.pipeline.deployment_node import (
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from ray.serve.deployment_node import (
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DeploymentNode,
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)
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@ -12,12 +12,12 @@ from ray.serve.handle import (
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serve_handle_to_json_dict,
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serve_handle_from_json_dict,
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)
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from ray.serve.pipeline.json_serde import (
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from ray.serve.json_serde import (
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DAGNodeEncoder,
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dagnode_from_json,
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DAGNODE_TYPE_KEY,
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)
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from ray.serve.pipeline.tests.resources.test_modules import (
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from ray.serve.tests.resources.test_modules import (
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Model,
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combine,
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Counter,
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@ -26,7 +26,7 @@ from ray.serve.pipeline.tests.resources.test_modules import (
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Combine,
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NESTED_HANDLE_KEY,
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)
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from ray.serve.pipeline.generate import (
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from ray.serve.deployment_graph_build import (
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transform_ray_dag_to_serve_dag,
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extract_deployments_from_serve_dag,
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transform_serve_dag_to_serve_executor_dag,
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@ -124,7 +124,7 @@ def test_simple_function_node_json_serde(serve_instance):
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executor_fn=_test_execution_function_node,
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expected_json_dict={
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DAGNODE_TYPE_KEY: "FunctionNode",
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"import_path": "ray.serve.pipeline.tests.resources.test_modules.combine",
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"import_path": "ray.serve.tests.resources.test_modules.combine",
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"args": [1, 2],
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"kwargs": {},
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"options": {},
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@ -139,7 +139,7 @@ def test_simple_function_node_json_serde(serve_instance):
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executor_fn=_test_execution_function_node,
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expected_json_dict={
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DAGNODE_TYPE_KEY: "FunctionNode",
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"import_path": "ray.serve.pipeline.tests.resources.test_modules.combine",
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"import_path": "ray.serve.tests.resources.test_modules.combine",
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"args": [1, 2],
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"kwargs": {"kwargs_output": 3},
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"options": {},
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@ -154,7 +154,7 @@ def test_simple_function_node_json_serde(serve_instance):
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executor_fn=_test_execution_function_node,
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expected_json_dict={
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DAGNODE_TYPE_KEY: "FunctionNode",
|
||||
"import_path": "ray.serve.pipeline.tests.resources.test_modules.fn_hello",
|
||||
"import_path": "ray.serve.tests.resources.test_modules.fn_hello",
|
||||
"args": [],
|
||||
"kwargs": {},
|
||||
"options": {},
|
||||
|
|
@ -184,7 +184,7 @@ def test_simple_class_node_json_serde(serve_instance):
|
|||
executor_fn=_test_execution_class_node_ClassHello,
|
||||
expected_json_dict={
|
||||
DAGNODE_TYPE_KEY: "ClassNode",
|
||||
"import_path": "ray.serve.pipeline.tests.resources.test_modules.ClassHello",
|
||||
"import_path": "ray.serve.tests.resources.test_modules.ClassHello",
|
||||
"args": [],
|
||||
"kwargs": {},
|
||||
"options": {},
|
||||
|
|
@ -199,7 +199,7 @@ def test_simple_class_node_json_serde(serve_instance):
|
|||
executor_fn=_test_execution_class_node_Model,
|
||||
expected_json_dict={
|
||||
DAGNODE_TYPE_KEY: "ClassNode",
|
||||
"import_path": "ray.serve.pipeline.tests.resources.test_modules.Model",
|
||||
"import_path": "ray.serve.tests.resources.test_modules.Model",
|
||||
"args": [1],
|
||||
"kwargs": {},
|
||||
"options": {},
|
||||
|
|
@ -214,7 +214,7 @@ def test_simple_class_node_json_serde(serve_instance):
|
|||
executor_fn=_test_execution_class_node_Model,
|
||||
expected_json_dict={
|
||||
DAGNODE_TYPE_KEY: "ClassNode",
|
||||
"import_path": "ray.serve.pipeline.tests.resources.test_modules.Model",
|
||||
"import_path": "ray.serve.tests.resources.test_modules.Model",
|
||||
"args": [1],
|
||||
"kwargs": {"ratio": 0.5},
|
||||
"options": {},
|
||||
|
|
@ -14,7 +14,7 @@ from ray.serve.model_wrappers import (
|
|||
)
|
||||
from ray.air.checkpoint import Checkpoint
|
||||
from ray.air.predictor import DataBatchType, Predictor
|
||||
from ray.serve.pipeline.api import build
|
||||
from ray.serve.deployment_graph_build import build
|
||||
from ray.serve.dag import InputNode
|
||||
from ray.serve.deployment_graph import RayServeDAGHandle
|
||||
from ray.serve.http_adapters import json_to_ndarray
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@ from ray import serve
|
|||
from ray.serve.application import Application
|
||||
from ray.serve.api import build as build_app
|
||||
from ray.serve.deployment_graph import RayServeDAGHandle
|
||||
from ray.serve.pipeline.api import build as pipeline_build
|
||||
from ray.serve.deployment_graph_build import build as pipeline_build
|
||||
from ray.serve.deployment_graph import ClassNode, InputNode
|
||||
from ray.serve.drivers import DAGDriver
|
||||
import starlette.requests
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ from ray.serve.deployment_graph import InputNode
|
|||
from ray.serve.drivers import DAGDriver
|
||||
import starlette.requests
|
||||
|
||||
from ray.serve.pipeline.generate import transform_ray_dag_to_serve_dag
|
||||
from ray.serve.generate import transform_ray_dag_to_serve_dag
|
||||
|
||||
NESTED_HANDLE_KEY = "nested_handle"
|
||||
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from typing import Any
|
|||
from ray import serve
|
||||
from ray.serve.deployment_graph import RayServeDAGHandle
|
||||
from ray.serve.dag import InputNode
|
||||
from ray.serve.pipeline.api import build as pipeline_build
|
||||
from ray.serve.deployment_graph_build import build as pipeline_build
|
||||
|
||||
|
||||
@serve.deployment(name="counter", num_replicas=2, user_config={"count": 123, "b": 2})
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import sys
|
|||
|
||||
import pytest
|
||||
from ray.serve.dag import InputNode
|
||||
from ray.serve.pipeline.api import build as pipeline_build
|
||||
from ray.serve.deployment_graph_build import build as pipeline_build
|
||||
|
||||
import ray
|
||||
from ray import serve
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
import pytest
|
||||
import sys
|
||||
|
||||
from ray.serve.pipeline.api import get_and_validate_ingress_deployment
|
||||
from ray.serve.pipeline.api import build as pipeline_build
|
||||
from ray.serve.deployment_graph_build import get_and_validate_ingress_deployment
|
||||
from ray.serve.deployment_graph_build import build as pipeline_build
|
||||
from ray.serve.dag import InputNode
|
||||
from ray import serve
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue