training/producers: move out of dashboard/ per ownership boundary

Producers are event *sources* — the renderer is everything inside
training/dashboard/. Sibling layout makes the dependency direction
one-way (producers import from training.dashboard.events; dashboard
never reaches into producers).

  training/dashboard/producers/   →   training/producers/

Internal imports rewritten via sed; eval_/run.py and training/README.md
cross-references updated. CLI entry stays via `python -m training.producers.<sub>`
(replay / metrics / perf / profiles).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

training/README.md

@@ -32,7 +32,9 @@ training/
     _metrics.py           macro F1 + per-class F1 with bootstrap 95 % CIs;
                           paired-bootstrap significance for model-vs-model
     breakdown.py          per-profile, per-host metric tables
-  dashboard/producers/    live event emitters — see ../dashboard/PRODUCERS.md
+  producers/              live event sources for the dashboard
+                          (replay, metrics, perf, profiles)
+                          → see training/dashboard/PRODUCERS.md
 ```
 
 ## The honesty rules this implements
@@ -162,7 +164,7 @@ uv run --group training python -m training.eval_.run \
 
 ## Live dashboard
 
-Producers under `training/dashboard/producers/` push events to the
+Producers under `training/producers/` push events to the
 `dashboard.wg` WebSocket via the canonical
 `training.dashboard.client.Publisher` (loopback HTTP, stdlib-only).
 See [`../dashboard/PRODUCERS.md`](../dashboard/PRODUCERS.md) for the
@@ -170,7 +172,7 @@ event contract.
 
 ```sh
 # After training, push live model_metric + model_perf bars:
-uv run --group training python -m training.dashboard.producers.metrics \
+uv run --group training python -m training.producers.metrics \
     --validation data/processed/validation_v1.parquet \
     --artifacts artifacts \
     --summary data/processed/features_window_v1.parquet \
@@ -178,7 +180,7 @@ uv run --group training python -m training.dashboard.producers.metrics \
 
 # Replay one episode at wall-clock speed (drives phase + prediction +
 # embedding events):
-uv run --group training python -m training.dashboard.producers.replay \
+uv run --group training python -m training.producers.replay \
     --episode  /var/lib/cis490/episodes/elliott-thinkpad/<id>.tar.zst \
     --host-id  elliott-thinkpad \
     --artifacts artifacts

training/eval_/run.py

@@ -24,7 +24,7 @@ from training._features import PHASES
 from training._split import (
     held_out_host, held_out_sample, held_out_time,
 )
-from training.dashboard.producers._models import load_models
+from training.producers._models import load_models
 from training.eval_._metrics import (
     bootstrap_macro_f1, bootstrap_per_class_f1,
     confusion_matrix, paired_bootstrap_macro_f1_diff,

training/producers/__main__.py

@@ -1,9 +1,9 @@
 """CLI dispatcher for dashboard producers.
 
-  python -m training.dashboard.producers replay   --episode … --host-id …
-  python -m training.dashboard.producers metrics  --window … --schema …
-  python -m training.dashboard.producers perf     --window … --schema …
-  python -m training.dashboard.producers profiles --validation … --store …
+  python -m training.producers replay   --episode … --host-id …
+  python -m training.producers metrics  --window … --schema …
+  python -m training.producers perf     --window … --schema …
+  python -m training.producers profiles --validation … --store …
 
 Each subcommand forwards remaining argv to the matching module's main().
 """
@@ -13,16 +13,16 @@ import sys
 
 
 SUBCOMMANDS = {
-    "replay":   "training.dashboard.producers.replay",
-    "metrics":  "training.dashboard.producers.metrics",
-    "perf":     "training.dashboard.producers.perf",
-    "profiles": "training.dashboard.producers.profiles",
+    "replay":   "training.producers.replay",
+    "metrics":  "training.producers.metrics",
+    "perf":     "training.producers.perf",
+    "profiles": "training.producers.profiles",
 }
 
 
 def main() -> int:
     if len(sys.argv) < 2 or sys.argv[1] in {"-h", "--help"}:
-        print("usage: python -m training.dashboard.producers "
+        print("usage: python -m training.producers "
               "<replay|metrics|perf|profiles> [args]", file=sys.stderr)
         return 2
     sub = sys.argv[1]

training/producers/metrics.py

@@ -28,8 +28,8 @@ sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 from training._split import (
     held_out_host, held_out_sample, held_out_time,
 )
-from training.dashboard.producers._models import load_models
-from training.dashboard.producers._publish import (
+from training.producers._models import load_models
+from training.producers._publish import (
     PublishFn, http_publisher, null_publisher,
 )
 from training.eval_._metrics import _macro_f1

training/producers/perf.py

@@ -20,10 +20,10 @@ import pyarrow.parquet as pq
 
 sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 from training._split import held_out_host
-from training.dashboard.producers._models import (
+from training.producers._models import (
     latency_us_batched, load_models,
 )
-from training.dashboard.producers._publish import (
+from training.producers._publish import (
     PublishFn, http_publisher, null_publisher,
 )
 from training.eval_._metrics import _macro_f1
@@ -37,7 +37,7 @@ async def emit_perf(*, publish: PublishFn, artifacts_dir: Path,
                      summary_path: Path | None,
                      tensors_root: Path | None,
                      batch_for_scatter: int = 64) -> int:
-    from training.dashboard.producers.metrics import _build_test_set
+    from training.producers.metrics import _build_test_set
     models = load_models(artifacts_dir)
     if not models:
         return 0

training/producers/profiles.py

@@ -29,7 +29,7 @@ import pyarrow.parquet as pq
 sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
 from training._episode_io import open_episode
 from training._features import ALL_CHANNELS, channel_arrays
-from training.dashboard.producers._publish import (
+from training.producers._publish import (
     PublishFn, http_publisher, null_publisher,
 )
 

training/producers/replay.py

@@ -32,10 +32,10 @@ from training._episode_io import open_episode
 from training._features import (
     PHASE_TO_INT, summary_windows, tensor_windows,
 )
-from training.dashboard.producers._models import (
+from training.producers._models import (
     load_models, model_display_name,
 )
-from training.dashboard.producers._publish import (
+from training.producers._publish import (
     PublishFn, http_publisher, null_publisher,
 )
 from training.models import BaseModel