refactor(dual-driver): PipelineHandle dataclass + local imports

_build_pipeline took six threaded dependencies (ray, MilesCoordinator,
MilesPipelineConfig, get_coordinator_actor_name, get_pipeline_namespace,
logger) and returned a 5-tuple that main() then re-packed into a 7-tuple,
unpacked positionally in four places. A missed position would silently
mis-wire a handle.

- Drop the threaded deps: _build_pipeline imports ray / MilesCoordinator /
  get_coordinator_actor_name / get_pipeline_namespace locally (still after
  the env guard, since it only runs from main); MilesPipelineConfig and the
  logger are module-level.
- Return a PipelineHandle dataclass; main fills train_group / rollout_manager
  after build and accesses everything by attribute.

No behavior change; both drivers still import cleanly with the heavy imports
deferred past the RLIX_CONTROL_PLANE guard.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

Author: TianyeGGBond

examples/rlix/run_miles_dual.py

@@ -37,7 +37,10 @@
 from __future__ import annotations
 
 import copy
+import logging
 import os
+from dataclasses import dataclass
+from typing import Any
 
 from ._common import (
     MilesPipelineConfig,
@@ -51,6 +54,22 @@
     "examples/rlix/run_miles_dual.py", "examples.rlix.run_miles_dual"
 )
 
+logger = logging.getLogger("run_miles_dual")
+
+
+@dataclass
+class PipelineHandle:
+    """One pipeline's actors + handles, populated across build and setup."""
+
+    index: int
+    pipeline_id: str
+    namespace: str
+    coordinator: Any
+    pipeline: Any
+    args: Any
+    train_group: Any = None
+    rollout_manager: Any = None
+
 
 def _split_pools_for_dual(
     *, num_gpus_per_node: int, infer_pool_size: int
@@ -217,13 +236,7 @@ def _build_pipeline(
     train_mapping: list[int],
     infer_mapping: list[int],
     orchestrator,
-    ray,
-    MilesCoordinator,
-    MilesPipelineConfig,
-    get_coordinator_actor_name,
-    get_pipeline_namespace,
-    logger,
-):
+) -> PipelineHandle:
     """Allocate one pipeline_id, register, admit, create coordinator+pipeline.
 
     ``train_mapping`` / ``infer_mapping`` are the EXPLICIT physical GPU
@@ -232,8 +245,15 @@ def _build_pipeline(
     partial-overlap invariant is asserted; cross-pipeline overlap is
     asserted by ``grep_overlap_log.sh`` end-to-end.
 
-    Returns ``(pipeline_id, namespace, coordinator_handle, pipeline_handle, args)``.
+    Returns a :class:`PipelineHandle`.
     """
+    import ray
+    from rlix.pipeline.miles_coordinator import MilesCoordinator
+    from rlix.protocol.types import (
+        get_coordinator_actor_name,
+        get_pipeline_namespace,
+    )
+
     pipeline_id = ray.get(orchestrator.allocate_pipeline_id.remote("miles"))
     pipeline_namespace = get_pipeline_namespace(pipeline_id)
 
@@ -331,32 +351,32 @@ def _build_pipeline(
         pipeline_index, pipeline_id,
     )
 
-    return pipeline_id, pipeline_namespace, coordinator, pipeline, args
+    return PipelineHandle(
+        index=pipeline_index,
+        pipeline_id=pipeline_id,
+        namespace=pipeline_namespace,
+        coordinator=coordinator,
+        pipeline=pipeline,
+        args=args,
+    )
 
 
 def main():
     """Dual-pipeline entry. Imports heavy modules lazily so the env-var
     guard above fires before transitive ``import torch`` / ``import sglang``.
     """
     import asyncio
-    import logging
 
     import ray
 
     from miles.utils.arguments import parse_args
     from miles.utils.logging_utils import configure_logger
     from miles.utils.rlix_train_loop import run_async_train_loop
     from miles.utils.rlix_validation import assert_rlix_topology
-    from rlix.pipeline.miles_coordinator import MilesCoordinator
-    from rlix.protocol.types import (
-        get_coordinator_actor_name,
-        get_pipeline_namespace,
-    )
 
     import rlix
 
     configure_logger()
-    logger = logging.getLogger("run_miles_dual")
     base_args = parse_args()
 
     # F10 startup fail-fast on the BASE args. Per-pipeline arg overrides
@@ -411,75 +431,64 @@ def main():
         train_mapping=p1_train,
         infer_mapping=p1_infer,
         orchestrator=orchestrator,
-        ray=ray,
-        MilesCoordinator=MilesCoordinator,
-        MilesPipelineConfig=MilesPipelineConfig,
-        get_coordinator_actor_name=get_coordinator_actor_name,
-        get_pipeline_namespace=get_pipeline_namespace,
-        logger=logger,
     )
     p2 = _build_pipeline(
         base_args=base_args,
         pipeline_index=2,
         train_mapping=p2_train,
         infer_mapping=p2_infer,
         orchestrator=orchestrator,
-        ray=ray,
-        MilesCoordinator=MilesCoordinator,
-        MilesPipelineConfig=MilesPipelineConfig,
-        get_coordinator_actor_name=get_coordinator_actor_name,
-        get_pipeline_namespace=get_pipeline_namespace,
-        logger=logger,
     )
 
-    pipelines = [p1, p2]
+    handles = [p1, p2]
 
     # ---- 3. Pull handles for each pipeline. ------------------------------
-    handles = []
-    for pid, ns, coord, pipe, args in pipelines:
-        train_group = ray.get(pipe.get_train_group.remote())
-        rollout_manager = ray.get(pipe.get_rollout_manager.remote())
-        engine_count = int(ray.get(pipe.get_declared_engine_count.remote()))
+    for h in handles:
+        h.train_group = ray.get(h.pipeline.get_train_group.remote())
+        h.rollout_manager = ray.get(h.pipeline.get_rollout_manager.remote())
+        engine_count = int(ray.get(h.pipeline.get_declared_engine_count.remote()))
         logger.info(
             "[run_miles_dual] handles ready pipeline_id=%s engines=%d",
-            pid, engine_count,
+            h.pipeline_id, engine_count,
         )
-        handles.append((pid, ns, coord, pipe, args, train_group, rollout_manager))
 
     # ---- 4. Drive 2 concurrent rlix_train_loops via asyncio.gather. -----
-    async def _run_one_pipeline(idx, pid, pipe, args, train_group, rollout_manager):
+    async def _run_one_pipeline(h: PipelineHandle):
         async def _before(step: int) -> None:
-            await pipe.before_training.remote(step)
+            await h.pipeline.before_training.remote(step)
 
         async def _after(step: int) -> None:
-            await pipe.after_training.remote(step)
+            await h.pipeline.after_training.remote(step)
 
         async def _release_only(step: int) -> None:
-            # R04-F1 cleanup hook: releases actor_train allocation only.
-            await pipe.release_train_only.remote(step)
+            # Cleanup hook: releases actor_train allocation only.
+            await h.pipeline.release_train_only.remote(step)
 
         # Per-rollout step_target = rollout_batch_size. See
         # MilesPipeline.signal_rollout_demand docstring for why pre-signalling
         # demand to the scheduler is required for 4-GPU 2-pipeline full
         # cross-overlap (without it, rollout 2+ hangs when both pipelines
         # release all DP workers between rollouts).
-        _step_target = int(getattr(args, "rollout_batch_size", 0) or 0)
+        _step_target = int(getattr(h.args, "rollout_batch_size", 0) or 0)
 
         async def _signal_demand(rollout_id: int) -> None:
             if _step_target <= 0:
                 return
-            await pipe.signal_rollout_demand.remote(rollout_id, _step_target)
+            await h.pipeline.signal_rollout_demand.remote(rollout_id, _step_target)
 
         await run_async_train_loop(
-            args,
-            train_group=train_group,
-            rollout_manager=rollout_manager,
+            h.args,
+            train_group=h.train_group,
+            rollout_manager=h.rollout_manager,
             before_step=_before,
             after_step=_after,
             release_only=_release_only,
             signal_demand=_signal_demand,
         )
-        logger.info("[run_miles_dual] mp%d training loop complete pipeline_id=%s", idx, pid)
+        logger.info(
+            "[run_miles_dual] mp%d training loop complete pipeline_id=%s",
+            h.index, h.pipeline_id,
+        )
 
     async def _async_main():
         # F4 fix (m11-review.review-report.md §2): use create_task + wait(
@@ -491,13 +500,7 @@ async def _async_main():
         # so Phase 1's try/finally inside run_async_train_loop fires
         # release_only on the CancelledError path and the scheduler
         # ledger stays consistent.
-        tasks = [
-            asyncio.create_task(
-                _run_one_pipeline(i + 1, pid, pipe, args, train_group, rollout_manager)
-            )
-            for i, (pid, ns, coord, pipe, args, train_group, rollout_manager)
-            in enumerate(handles)
-        ]
+        tasks = [asyncio.create_task(_run_one_pipeline(h)) for h in handles]
         try:
             done, pending = await asyncio.wait(
                 tasks, return_when=asyncio.FIRST_EXCEPTION
@@ -530,13 +533,13 @@ async def _async_main():
             # original training exception. Codex Phase 7 review MEDIUM.
             try:
                 shutdown_refs = [
-                    pipe.shutdown_hard.remote() for _, _, _, pipe, _, _, _ in handles
+                    h.pipeline.shutdown_hard.remote() for h in handles
                 ]
                 ray.get(shutdown_refs, timeout=60.0)
-                for pid, _, _, _, _, _, _ in handles:
+                for h in handles:
                     logger.info(
                         "[run_miles_dual] shutdown_hard complete pipeline_id=%s",
-                        pid,
+                        h.pipeline_id,
                     )
             except Exception as exc:  # noqa: BLE001
                 logger.warning(