From 10b4a48160cb72b7ef10e03ab27601ac2059471f Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 13:38:55 +0200
Subject: [PATCH 01/13] Add structured logging (JSON lines or text)

QNNBENCH_LOG_FORMAT=json emits one JSON object per line with extra
fields as top-level keys, for container log shippers; text stays the
default for terminals.
---
 src/qnnbench/logs.py | 62 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 62 insertions(+)
 create mode 100644 src/qnnbench/logs.py

diff --git a/src/qnnbench/logs.py b/src/qnnbench/logs.py
new file mode 100644
index 0000000..f6c760b
--- /dev/null
+++ b/src/qnnbench/logs.py
@@ -0,0 +1,62 @@
+"""Logging setup: JSON lines for machines (containers, log shippers), plain text for terminals.
+
+    configure_logging()                     # QNNBENCH_LOG_FORMAT=json|text, QNNBENCH_LOG_LEVEL
+    log.info("epoch done", extra={"epoch": 3, "test_acc": 0.91})
+
+Fields passed through ``extra`` become top-level keys in JSON output, so
+dashboards can filter on them without parsing message strings.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import sys
+from datetime import datetime, timezone
+
+_RESERVED = set(logging.makeLogRecord({}).__dict__) | {"message", "asctime"}
+
+
+class JsonFormatter(logging.Formatter):
+    def format(self, record: logging.LogRecord) -> str:
+        payload = {
+            "ts": datetime.fromtimestamp(record.created, timezone.utc).isoformat(
+                timespec="milliseconds"
+            ),
+            "level": record.levelname.lower(),
+            "logger": record.name,
+            "msg": record.getMessage(),
+        }
+        payload.update({k: v for k, v in record.__dict__.items() if k not in _RESERVED})
+        if record.exc_info:
+            payload["exc"] = self.formatException(record.exc_info)
+        return json.dumps(payload, default=str)
+
+
+class TextFormatter(logging.Formatter):
+    def format(self, record: logging.LogRecord) -> str:
+        extras = {k: v for k, v in record.__dict__.items() if k not in _RESERVED}
+        line = f"{record.levelname[0]} {record.name}: {record.getMessage()}"
+        if extras:
+            line += "  " + " ".join(f"{k}={_short(v)}" for k, v in extras.items())
+        if record.exc_info:
+            line += "\n" + self.formatException(record.exc_info)
+        return line
+
+
+def _short(v) -> str:
+    return f"{v:.4g}" if isinstance(v, float) else str(v)
+
+
+def configure_logging(fmt: str | None = None, level: str | None = None) -> None:
+    """Idempotent root-logger setup for the qnnbench CLIs and the server."""
+    fmt = fmt or os.environ.get("QNNBENCH_LOG_FORMAT", "text")
+    if fmt not in ("json", "text"):
+        raise ValueError(f"QNNBENCH_LOG_FORMAT must be 'json' or 'text', got {fmt!r}")
+    handler = logging.StreamHandler(sys.stderr)
+    handler.setFormatter(JsonFormatter() if fmt == "json" else TextFormatter())
+    logger = logging.getLogger("qnnbench")
+    logger.handlers[:] = [handler]
+    logger.setLevel(level or os.environ.get("QNNBENCH_LOG_LEVEL", "INFO"))
+    logger.propagate = False

From a09310bfc4a4816b858de31c353dca2d85f2c673 Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 13:38:55 +0200
Subject: [PATCH 02/13] Serve: load shedding, request deadlines, graceful
 drain, Prometheus metrics

The 64-client load test showed an unbounded queue turning overload into
multi-second tails at flat throughput. Now:
- the queue is bounded; when full, requests get 503 + Retry-After
  immediately instead of waiting;
- each request has a deadline; requests that expire while queued are
  dropped before inference (504) so no compute goes to answers nobody
  is waiting for;
- on shutdown /readyz turns 503, queued work drains, then the worker
  stops; /healthz stays a pure liveness check;
- metrics move to prometheus_client histograms (latency, batch size,
  inference time), outcome-labelled counters and a queue-depth gauge;
- responses carry a model version (checkpoint content hash);
- settings are one validated dataclass read from QNNBENCH_* variables.

The load test reports shed and timed-out requests separately from errors.
---
 pyproject.toml           |   1 +
 src/qnnbench/loadtest.py |  19 ++-
 src/qnnbench/serve.py    | 303 +++++++++++++++++++++++++++++----------
 tests/test_serve.py      |  78 +++++++++-
 4 files changed, 314 insertions(+), 87 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index dd1f6e9..8c251dc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -26,6 +26,7 @@ bench = [
 ]
 serve = [
     "fastapi>=0.110",
+    "prometheus-client>=0.20",
     "uvicorn>=0.29",
     "httpx>=0.27",
 ]
diff --git a/src/qnnbench/loadtest.py b/src/qnnbench/loadtest.py
index 5689497..48707cb 100644
--- a/src/qnnbench/loadtest.py
+++ b/src/qnnbench/loadtest.py
@@ -27,6 +27,7 @@
 async def run_load(url: str, concurrency: int, n_requests: int) -> dict:
     latencies: list[float] = []
     errors = 0
+    status_counts: dict[int, int] = {}
     remaining = n_requests
     rng = random.Random(0)
 
@@ -38,10 +39,14 @@ async def client(http: httpx.AsyncClient):
             t0 = time.perf_counter()
             try:
                 r = await http.post(f"{url}/predict", json={"pixels": pixels})
-                r.raise_for_status()
-                latencies.append((time.perf_counter() - t0) * 1e3)
             except httpx.HTTPError:
                 errors += 1
+                continue
+            status_counts[r.status_code] = status_counts.get(r.status_code, 0) + 1
+            if r.status_code == 200:
+                latencies.append((time.perf_counter() - t0) * 1e3)
+            elif r.status_code not in (503, 504):  # shed / deadline: expected under overload
+                errors += 1
 
     limits = httpx.Limits(max_connections=concurrency)
     async with httpx.AsyncClient(timeout=60, limits=limits) as http:
@@ -56,6 +61,8 @@ async def client(http: httpx.AsyncClient):
         "concurrency": concurrency,
         "requests": len(lat),
         "errors": errors,
+        "shed_503": status_counts.get(503, 0),
+        "timeout_504": status_counts.get(504, 0),
         "throughput_rps": len(lat) / elapsed,
         "p50_ms": statistics.median(lat) if lat else float("nan"),
         "p95_ms": pct(0.95),
@@ -70,7 +77,7 @@ def _wait_ready(url: str, proc: subprocess.Popen, timeout: float = 60):
         if proc.poll() is not None:
             raise RuntimeError("server exited during startup")
         try:
-            if httpx.get(f"{url}/healthz", timeout=1).status_code == 200:
+            if httpx.get(f"{url}/readyz", timeout=1).status_code == 200:
                 return
         except httpx.HTTPError:
             time.sleep(0.25)
@@ -85,6 +92,7 @@ def main(argv=None):
     p.add_argument("--concurrency", type=int, nargs="+", default=[1, 16, 64])
     p.add_argument("--requests", type=int, default=1000)
     p.add_argument("--torch-threads", type=int, help="QNNBENCH_TORCH_THREADS for spawned servers")
+    p.add_argument("--max-queue", type=int, help="QNNBENCH_MAX_QUEUE for spawned servers")
     p.add_argument("--out", type=Path)
     args = p.parse_args(argv)
 
@@ -96,6 +104,8 @@ def main(argv=None):
             env = {**os.environ, "QNNBENCH_MAX_BATCH": str(max_batch)}
             if args.torch_threads:
                 env["QNNBENCH_TORCH_THREADS"] = str(args.torch_threads)
+            if args.max_queue:
+                env["QNNBENCH_MAX_QUEUE"] = str(args.max_queue)
             proc = subprocess.Popen(
                 [sys.executable, "-m", "uvicorn", "qnnbench.serve:app", "--port", port,
                  "--log-level", "warning"],
@@ -110,7 +120,8 @@ def main(argv=None):
                 print(
                     f"max_batch={max_batch} concurrency={c:>3}: "
                     f"{r['throughput_rps']:7.1f} req/s  p50={r['p50_ms']:7.1f} ms  "
-                    f"p95={r['p95_ms']:7.1f} ms  p99={r['p99_ms']:7.1f} ms  errors={r['errors']}"
+                    f"p95={r['p95_ms']:7.1f} ms  p99={r['p99_ms']:7.1f} ms  "
+                    f"shed={r['shed_503']} timeout={r['timeout_504']} errors={r['errors']}"
                 )
         finally:
             if proc:
diff --git a/src/qnnbench/serve.py b/src/qnnbench/serve.py
index b5e0e05..6e067ba 100644
--- a/src/qnnbench/serve.py
+++ b/src/qnnbench/serve.py
@@ -1,90 +1,219 @@
-"""Inference service for the QNN with dynamic micro-batching.
+"""Inference service for the QNN with dynamic micro-batching and load shedding.
 
     QNNBENCH_CHECKPOINT=runs/qnn.pt uvicorn qnnbench.serve:app --port 8000
 
 A state-vector simulation costs nearly the same for 1 sample as for dozens
 (see the ``batch`` benchmark), so serving one request per forward pass wastes
 most of the hardware. Requests are queued; a single worker collects up to
-``MAX_BATCH`` of them, or whatever arrived within ``MAX_WAIT_MS`` of the
-first, and runs them as one batch. ``MAX_BATCH=1`` disables batching for
+``max_batch`` of them, or whatever arrived within ``max_wait_ms`` of the
+first, and runs them as one batch. ``max_batch=1`` disables batching for
 comparison (see :mod:`qnnbench.loadtest`).
 
-Configuration (environment): QNNBENCH_CHECKPOINT, QNNBENCH_DEVICE,
-QNNBENCH_MAX_BATCH (64), QNNBENCH_MAX_WAIT_MS (5), QNNBENCH_FUSE (5),
-QNNBENCH_TORCH_THREADS (CPU only; default: torch's choice). On CPU, leave
-a core free for the event loop: if torch's threads take every core, HTTP
-parsing stalls, requests reach the batcher late, and batches stay small.
+Overload behaviour, which an unbounded queue gets wrong: past saturation every
+extra client only adds queueing delay, so latency grows without bound while
+throughput stays flat. Instead:
+
+* The queue is bounded (``max_queue``). When it is full the request is
+  rejected at once with 503 + ``Retry-After``, so clients back off or retry
+  another replica rather than wait.
+* Every request has a deadline (``request_timeout_ms``). Requests that expire
+  while queued are dropped before inference, so no compute is spent on
+  answers nobody is waiting for; the client gets 504.
+* On shutdown the server stops admitting work (``/readyz`` turns 503 so a load
+  balancer stops routing to it), finishes what is queued, then exits.
+
+Configuration is read from ``QNNBENCH_*`` environment variables; see
+:class:`Settings`.
 """
 
 from __future__ import annotations
 
 import asyncio
+import hashlib
+import logging
 import os
 import time
-from collections import deque
 from collections.abc import Callable
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager
+from dataclasses import dataclass, fields
 
 import torch
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import PlainTextResponse
+from fastapi import FastAPI, HTTPException, Response
+from prometheus_client import (
+    CONTENT_TYPE_LATEST,
+    CollectorRegistry,
+    Counter,
+    Gauge,
+    Histogram,
+    generate_latest,
+)
 from pydantic import BaseModel, Field
 
 from qnnbench.env import resolve_device
+from qnnbench.logs import configure_logging
 from qnnbench.models import QNN
 
+log = logging.getLogger("qnnbench.serve")
+
+
+@dataclass(frozen=True)
+class Settings:
+    """Server configuration; each field is read from ``QNNBENCH_<NAME>``."""
+
+    checkpoint: str | None = None
+    device: str = "auto"
+    fuse: int = 5
+    max_batch: int = 64
+    max_wait_ms: float = 5.0
+    max_queue: int = 256
+    request_timeout_ms: float = 2000.0
+    drain_timeout_s: float = 10.0
+    # CPU only. Leave a core for the event loop: if torch's threads take every
+    # core, HTTP parsing stalls, requests reach the batcher late, and batches
+    # stay small (measured in the README's serving results).
+    torch_threads: int | None = None
+
+    @classmethod
+    def from_env(cls, env=os.environ) -> Settings:
+        values = {}
+        for f in fields(cls):
+            raw = env.get(f"QNNBENCH_{f.name.upper()}")
+            if raw is None or raw == "":
+                continue
+            kind = str(f.type)
+            values[f.name] = (
+                int(raw) if "int" in kind else float(raw) if "float" in kind else raw
+            )  # fmt: skip
+        s = cls(**values)
+        if s.max_batch < 1 or s.max_queue < 1 or s.max_wait_ms < 0 or s.request_timeout_ms <= 0:
+            raise ValueError(f"invalid server settings: {s}")
+        return s
+
+
+class Metrics:
+    """Prometheus metrics on a per-app registry (so tests can build several apps)."""
+
+    def __init__(self):
+        self.registry = CollectorRegistry()
+        self.requests = Counter(
+            "qnnbench_requests", "Prediction requests by outcome", ["outcome"],
+            registry=self.registry,
+        )  # fmt: skip
+        self.latency = Histogram(
+            "qnnbench_request_latency_seconds", "End-to-end latency of successful requests",
+            buckets=(0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5),
+            registry=self.registry,
+        )  # fmt: skip
+        self.batch_size = Histogram(
+            "qnnbench_batch_size", "Requests per inference batch",
+            buckets=(1, 2, 4, 8, 16, 32, 64, 128), registry=self.registry,
+        )  # fmt: skip
+        self.inference = Histogram(
+            "qnnbench_inference_seconds", "Model time per batch",
+            buckets=(0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1),
+            registry=self.registry,
+        )  # fmt: skip
+        self.queue_depth = Gauge(
+            "qnnbench_queue_depth", "Requests waiting for a batch", registry=self.registry
+        )
+        self.expired = Counter(
+            "qnnbench_expired", "Requests dropped before inference because their deadline passed",
+            registry=self.registry,
+        )  # fmt: skip
+
+
+class Overloaded(Exception):
+    """The queue is full; the caller should shed the request."""
+
 
 class MicroBatcher:
     """Collects concurrent requests into batches for one inference worker thread."""
 
     def __init__(
-        self, fn: Callable[[torch.Tensor], torch.Tensor], max_batch: int, max_wait_ms: float
+        self,
+        fn: Callable[[torch.Tensor], torch.Tensor],
+        max_batch: int,
+        max_wait_ms: float,
+        max_queue: int = 0,  # 0 = unbounded
+        metrics: Metrics | None = None,
     ):
         self.fn, self.max_batch, self.max_wait = fn, max_batch, max_wait_ms / 1e3
-        self.queue: asyncio.Queue = asyncio.Queue()
+        self.queue: asyncio.Queue = asyncio.Queue(maxsize=max_queue)
+        self.metrics = metrics or Metrics()
         # One thread: batches run back to back, and torch releases the GIL inside
         # kernels, so the event loop keeps accepting requests meanwhile.
         self.executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="infer")
-        self.batch_sizes: deque[int] = deque(maxlen=10_000)
         self._task: asyncio.Task | None = None
 
     def start(self):
         self._task = asyncio.get_running_loop().create_task(self._run())
 
+    async def drain(self, timeout: float):
+        """Wait until queued work is finished, up to ``timeout`` seconds."""
+        deadline = time.monotonic() + timeout
+        while not self.queue.empty() and time.monotonic() < deadline:
+            await asyncio.sleep(0.01)
+
     async def stop(self):
         if self._task:
             self._task.cancel()
         self.executor.shutdown(wait=True)
 
-    async def submit(self, x: torch.Tensor) -> float:
-        fut = asyncio.get_running_loop().create_future()
-        await self.queue.put((x, fut))
-        return await fut
+    async def submit(self, x: torch.Tensor, timeout: float | None = None) -> float:
+        """Queue one input; raises Overloaded if full, TimeoutError past ``timeout`` s."""
+        loop = asyncio.get_running_loop()
+        fut = loop.create_future()
+        deadline = loop.time() + timeout if timeout else float("inf")
+        try:
+            self.queue.put_nowait((x, fut, deadline))
+        except asyncio.QueueFull:
+            raise Overloaded from None
+        self.metrics.queue_depth.set(self.queue.qsize())
+        # shield: a timeout cancels our wait, not the queued future; the
+        # worker sees the expired deadline and skips it.
+        return await asyncio.wait_for(asyncio.shield(fut), timeout)
+
+    async def _next_batch(self, loop) -> list:
+        batch = [await self.queue.get()]
+        deadline = loop.time() + self.max_wait
+        while len(batch) < self.max_batch:
+            remaining = deadline - loop.time()
+            if remaining <= 0:
+                break
+            try:
+                batch.append(await asyncio.wait_for(self.queue.get(), remaining))
+            except asyncio.TimeoutError:
+                break
+        self.metrics.queue_depth.set(self.queue.qsize())
+        return batch
 
     async def _run(self):
         loop = asyncio.get_running_loop()
         while True:
-            batch = [await self.queue.get()]
-            deadline = loop.time() + self.max_wait
-            while len(batch) < self.max_batch:
-                remaining = deadline - loop.time()
-                if remaining <= 0:
-                    break
-                try:
-                    batch.append(await asyncio.wait_for(self.queue.get(), remaining))
-                except asyncio.TimeoutError:
-                    break
-            xs = torch.stack([x for x, _ in batch])
+            batch = await self._next_batch(loop)
+            now = loop.time()
+            live = [(x, fut) for x, fut, deadline in batch if deadline > now and not fut.done()]
+            if expired := len(batch) - len(live):
+                self.metrics.expired.inc(expired)
+                for _, fut, deadline in batch:
+                    if deadline <= now and not fut.done():
+                        fut.set_exception(TimeoutError())
+            if not live:
+                continue
+            xs = torch.stack([x for x, _ in live])
+            t0 = time.perf_counter()
             try:
                 out = await loop.run_in_executor(self.executor, self.fn, xs)
             except Exception as e:  # fail this batch's requests, keep serving
-                for _, fut in batch:
+                log.exception("inference batch failed", extra={"batch_size": len(live)})
+                for _, fut in live:
                     if not fut.done():
                         fut.set_exception(e)
                 continue
-            self.batch_sizes.append(len(batch))
-            for (_, fut), y in zip(batch, out.tolist(), strict=True):
+            self.metrics.inference.observe(time.perf_counter() - t0)
+            self.metrics.batch_size.observe(len(live))
+            for (_, fut), y in zip(live, out.tolist(), strict=True):
                 if not fut.done():  # client may have disconnected
                     fut.set_result(y)
 
@@ -96,45 +225,49 @@ class PredictRequest(BaseModel):
 class PredictResponse(BaseModel):
     digit: int
     score: float  # <Z> in [-1, 1]; positive means "3"
+    model_version: str
 
 
-def load_model(checkpoint: str | None, device: torch.device, fuse: int) -> QNN:
+def load_model(checkpoint: str | None, device: torch.device, fuse: int) -> tuple[QNN, str]:
+    """Load weights; the version is a content hash, so replicas can be compared."""
     model = QNN(fuse=fuse, grad_method="autograd")
+    version = "untrained"
     if checkpoint:
+        with open(checkpoint, "rb") as f:
+            version = hashlib.sha256(f.read()).hexdigest()[:12]
         state = torch.load(checkpoint, map_location="cpu")
         model.load_state_dict(state["state_dict"])
-    return model.to(device).eval()
-
-
-class Stats:
-    def __init__(self):
-        self.latencies_ms: deque[float] = deque(maxlen=10_000)
-        self.requests = 0
-        self.errors = 0
+    return model.to(device).eval(), version
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    device = resolve_device(os.environ.get("QNNBENCH_DEVICE", "auto"))
-    if threads := os.environ.get("QNNBENCH_TORCH_THREADS"):
-        torch.set_num_threads(int(threads))
-    model = load_model(
-        os.environ.get("QNNBENCH_CHECKPOINT"), device, int(os.environ.get("QNNBENCH_FUSE", 5))
-    )
+    configure_logging()
+    settings = Settings.from_env()
+    device = resolve_device(settings.device)
+    if settings.torch_threads:
+        torch.set_num_threads(settings.torch_threads)
+    model, version = load_model(settings.checkpoint, device, settings.fuse)
 
     @torch.no_grad()
     def infer(xs: torch.Tensor) -> torch.Tensor:
         return model(xs.to(device)).cpu()
 
+    metrics = Metrics()
     batcher = MicroBatcher(
-        infer,
-        max_batch=int(os.environ.get("QNNBENCH_MAX_BATCH", 64)),
-        max_wait_ms=float(os.environ.get("QNNBENCH_MAX_WAIT_MS", 5)),
+        infer, settings.max_batch, settings.max_wait_ms, settings.max_queue, metrics
     )
     batcher.start()
-    app.state.batcher, app.state.stats, app.state.device = batcher, Stats(), device
+    app.state.batcher, app.state.metrics, app.state.settings = batcher, metrics, settings
+    app.state.device, app.state.model_version, app.state.draining = device, version, False
+    configured = {k: v for k, v in vars(settings).items() if v is not None}
+    log.info("server ready", extra={"device": str(device), "model_version": version, **configured})
     yield
+    app.state.draining = True
+    log.info("draining", extra={"queued": batcher.queue.qsize()})
+    await batcher.drain(settings.drain_timeout_s)
     await batcher.stop()
+    log.info("stopped")
 
 
 app = FastAPI(title="qnnbench", lifespan=lifespan)
@@ -142,43 +275,55 @@ def infer(xs: torch.Tensor) -> torch.Tensor:
 
 @app.get("/healthz")
 async def healthz():
-    return {"status": "ok", "device": str(app.state.device)}
+    """Liveness: the process is up and the event loop responds."""
+    return {"status": "ok"}
+
+
+@app.get("/readyz")
+async def readyz(response: Response):
+    """Readiness: accepting traffic. 503 while draining, so load balancers route away."""
+    if app.state.draining:
+        response.status_code = 503
+        return {"status": "draining"}
+    return {
+        "status": "ready",
+        "device": str(app.state.device),
+        "model_version": app.state.model_version,
+        "queue_depth": app.state.batcher.queue.qsize(),
+    }
 
 
 @app.post("/predict", response_model=PredictResponse)
-async def predict(req: PredictRequest):
+async def predict(req: PredictRequest, response: Response):
+    m: Metrics = app.state.metrics
     if any(p not in (0.0, 1.0) for p in req.pixels):
+        m.requests.labels("invalid").inc()
         raise HTTPException(422, "pixels must be binary (0 or 1)")
-    stats: Stats = app.state.stats
+    if app.state.draining:
+        m.requests.labels("draining").inc()
+        raise HTTPException(503, "server is shutting down", headers={"Retry-After": "1"})
     t0 = time.perf_counter()
     try:
-        score = await app.state.batcher.submit(torch.tensor(req.pixels))
+        score = await app.state.batcher.submit(
+            torch.tensor(req.pixels), timeout=app.state.settings.request_timeout_ms / 1e3
+        )
+    except Overloaded:
+        m.requests.labels("shed").inc()
+        raise HTTPException(503, "overloaded, retry later", headers={"Retry-After": "1"}) from None
+    except (asyncio.TimeoutError, TimeoutError):
+        m.requests.labels("timeout").inc()
+        raise HTTPException(504, "request deadline exceeded") from None
     except Exception as e:
-        stats.errors += 1
+        m.requests.labels("error").inc()
         raise HTTPException(500, f"inference failed: {type(e).__name__}") from e
-    stats.requests += 1
-    stats.latencies_ms.append((time.perf_counter() - t0) * 1e3)
-    return PredictResponse(digit=3 if score > 0 else 6, score=score)
+    m.requests.labels("ok").inc()
+    m.latency.observe(time.perf_counter() - t0)
+    response.headers["X-Model-Version"] = app.state.model_version
+    return PredictResponse(
+        digit=3 if score > 0 else 6, score=score, model_version=app.state.model_version
+    )
 
 
-@app.get("/metrics", response_class=PlainTextResponse)
+@app.get("/metrics")
 async def metrics():
-    """Prometheus text format: request counts, latency and batch-size quantiles."""
-    stats: Stats = app.state.stats
-    sizes = sorted(app.state.batcher.batch_sizes)
-    lat = sorted(stats.latencies_ms)
-    lines = [
-        f"qnnbench_requests_total {stats.requests}",
-        f"qnnbench_errors_total {stats.errors}",
-        f"qnnbench_batches_total {len(sizes)}",
-    ]
-    for q in (0.5, 0.95, 0.99):
-        if lat:
-            lines.append(
-                f'qnnbench_latency_ms{{quantile="{q}"}} {lat[int(q * (len(lat) - 1))]:.3f}'
-            )
-        if sizes:
-            lines.append(
-                f'qnnbench_batch_size{{quantile="{q}"}} {sizes[int(q * (len(sizes) - 1))]}'
-            )
-    return "\n".join(lines) + "\n"
+    return Response(generate_latest(app.state.metrics.registry), media_type=CONTENT_TYPE_LATEST)
diff --git a/tests/test_serve.py b/tests/test_serve.py
index 8afea7c..7f0b99e 100644
--- a/tests/test_serve.py
+++ b/tests/test_serve.py
@@ -1,12 +1,14 @@
 import asyncio
+import time
 
 import pytest
 import torch
 
 pytest.importorskip("fastapi")
+pytest.importorskip("prometheus_client")
 from fastapi.testclient import TestClient  # noqa: E402
 
-from qnnbench.serve import MicroBatcher, app  # noqa: E402
+from qnnbench.serve import MicroBatcher, Overloaded, Settings, app  # noqa: E402
 
 
 def test_micro_batcher_groups_concurrent_requests():
@@ -51,14 +53,82 @@ async def scenario():
     assert asyncio.run(scenario()) == 2.0
 
 
-def test_predict_endpoint_and_metrics(monkeypatch):
+def test_full_queue_sheds_instead_of_waiting():
+    def slow(xs):
+        time.sleep(0.2)
+        return xs.sum(dim=1)
+
+    async def scenario():
+        batcher = MicroBatcher(slow, max_batch=1, max_wait_ms=0, max_queue=2)
+        batcher.start()
+        first = asyncio.ensure_future(batcher.submit(torch.ones(2)))
+        await asyncio.sleep(0.05)  # worker is now busy with `first`
+        queued = [asyncio.ensure_future(batcher.submit(torch.ones(2))) for _ in range(2)]
+        await asyncio.sleep(0)
+        with pytest.raises(Overloaded):
+            await batcher.submit(torch.ones(2))
+        results = await asyncio.gather(first, *queued)
+        await batcher.stop()
+        return results
+
+    assert asyncio.run(scenario()) == [2.0, 2.0, 2.0]
+
+
+def test_expired_requests_are_dropped_before_inference():
+    ran = []
+
+    def slow(xs):
+        ran.append(len(xs))
+        time.sleep(0.2)
+        return xs.sum(dim=1)
+
+    async def scenario():
+        batcher = MicroBatcher(slow, max_batch=1, max_wait_ms=0)
+        batcher.start()
+        busy = asyncio.ensure_future(batcher.submit(torch.ones(2)))
+        await asyncio.sleep(0.05)
+        with pytest.raises(asyncio.TimeoutError):
+            await batcher.submit(torch.ones(2), timeout=0.05)  # expires while queued
+        await busy
+        await asyncio.sleep(0.05)
+        await batcher.stop()
+        return batcher.metrics.expired._value.get()
+
+    expired = asyncio.run(scenario())
+    assert ran == [1] and expired == 1  # the expired request never reached the model
+
+
+def test_settings_from_env_parses_and_validates():
+    env = {
+        "QNNBENCH_MAX_BATCH": "8",
+        "QNNBENCH_MAX_WAIT_MS": "2.5",
+        "QNNBENCH_TORCH_THREADS": "3",
+        "QNNBENCH_CHECKPOINT": "w.pt",
+    }
+    s = Settings.from_env(env)
+    assert (s.max_batch, s.max_wait_ms, s.torch_threads, s.checkpoint) == (8, 2.5, 3, "w.pt")
+    with pytest.raises(ValueError):
+        Settings.from_env({"QNNBENCH_MAX_BATCH": "0"})
+
+
+def test_endpoints_metrics_and_draining(monkeypatch):
     monkeypatch.setenv("QNNBENCH_DEVICE", "cpu")
     with TestClient(app) as client:
         assert client.get("/healthz").json()["status"] == "ok"
+        assert client.get("/readyz").json()["model_version"] == "untrained"
         r = client.post("/predict", json={"pixels": [1, 0] * 8})
-        assert r.status_code == 200
+        assert r.status_code == 200 and r.headers["X-Model-Version"] == "untrained"
         body = r.json()
         assert body["digit"] in (3, 6) and -1 <= body["score"] <= 1
         assert client.post("/predict", json={"pixels": [0.5] * 16}).status_code == 422
         assert client.post("/predict", json={"pixels": [1] * 15}).status_code == 422
-        assert "qnnbench_requests_total 1" in client.get("/metrics").text
+        text = client.get("/metrics").text
+        assert 'qnnbench_requests_total{outcome="ok"} 1.0' in text
+        assert 'qnnbench_requests_total{outcome="invalid"} 1.0' in text
+        assert "qnnbench_batch_size_bucket" in text
+
+        app.state.draining = True
+        assert client.get("/readyz").status_code == 503
+        r = client.post("/predict", json={"pixels": [1, 0] * 8})
+        assert r.status_code == 503 and r.headers["Retry-After"] == "1"
+        app.state.draining = False

From 0da353325f5d46c87fac406114b4451ea77507db Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 13:43:45 +0200
Subject: [PATCH 03/13] Load test: connection-level limits and per-status
 counts

Overload runs showed client p99 far above the server's 2 s request
deadline with no 504s: the waiting happened in uvicorn's connection
backlog, before a request reaches the app, where application-level
shedding cannot see it. loadtest --limit-concurrency passes uvicorn's
limit through so excess connections are refused with 503 before any
parsing work. Results now include raw status counts, and the startup
log shows the resolved device rather than 'auto'.
---
 src/qnnbench/loadtest.py | 13 +++++++++++--
 src/qnnbench/serve.py    |  2 +-
 2 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/src/qnnbench/loadtest.py b/src/qnnbench/loadtest.py
index 48707cb..c79ce10 100644
--- a/src/qnnbench/loadtest.py
+++ b/src/qnnbench/loadtest.py
@@ -61,6 +61,7 @@ async def client(http: httpx.AsyncClient):
         "concurrency": concurrency,
         "requests": len(lat),
         "errors": errors,
+        "status_counts": status_counts,
         "shed_503": status_counts.get(503, 0),
         "timeout_504": status_counts.get(504, 0),
         "throughput_rps": len(lat) / elapsed,
@@ -93,6 +94,11 @@ def main(argv=None):
     p.add_argument("--requests", type=int, default=1000)
     p.add_argument("--torch-threads", type=int, help="QNNBENCH_TORCH_THREADS for spawned servers")
     p.add_argument("--max-queue", type=int, help="QNNBENCH_MAX_QUEUE for spawned servers")
+    p.add_argument(
+        "--limit-concurrency",
+        type=int,
+        help="uvicorn --limit-concurrency: reject with 503 at the connection layer, before parsing",
+    )
     p.add_argument("--out", type=Path)
     args = p.parse_args(argv)
 
@@ -108,7 +114,9 @@ def main(argv=None):
                 env["QNNBENCH_MAX_QUEUE"] = str(args.max_queue)
             proc = subprocess.Popen(
                 [sys.executable, "-m", "uvicorn", "qnnbench.serve:app", "--port", port,
-                 "--log-level", "warning"],
+                 "--log-level", "warning",
+                 *(["--limit-concurrency", str(args.limit_concurrency)]
+                   if args.limit_concurrency else [])],
                 env=env,
             )  # fmt: skip
             _wait_ready(args.url, proc)
@@ -121,7 +129,8 @@ def main(argv=None):
                     f"max_batch={max_batch} concurrency={c:>3}: "
                     f"{r['throughput_rps']:7.1f} req/s  p50={r['p50_ms']:7.1f} ms  "
                     f"p95={r['p95_ms']:7.1f} ms  p99={r['p99_ms']:7.1f} ms  "
-                    f"shed={r['shed_503']} timeout={r['timeout_504']} errors={r['errors']}"
+                    f"shed={r['shed_503']} timeout={r['timeout_504']} errors={r['errors']} "
+                    f"statuses={r['status_counts']}"
                 )
         finally:
             if proc:
diff --git a/src/qnnbench/serve.py b/src/qnnbench/serve.py
index 6e067ba..6e20d7d 100644
--- a/src/qnnbench/serve.py
+++ b/src/qnnbench/serve.py
@@ -261,7 +261,7 @@ def infer(xs: torch.Tensor) -> torch.Tensor:
     app.state.batcher, app.state.metrics, app.state.settings = batcher, metrics, settings
     app.state.device, app.state.model_version, app.state.draining = device, version, False
     configured = {k: v for k, v in vars(settings).items() if v is not None}
-    log.info("server ready", extra={"device": str(device), "model_version": version, **configured})
+    log.info("server ready", extra={**configured, "device": str(device), "model_version": version})
     yield
     app.state.draining = True
     log.info("draining", extra={"queued": batcher.queue.qsize()})

From 73a9199cc4eac2eeaf41e3f083d50b590e54de57 Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 13:45:28 +0200
Subject: [PATCH 04/13] Add torch.profiler command with named circuit-block
 ranges

python -m qnnbench.profile records training steps (wait/warmup/active
schedule), exports a Chrome/Perfetto trace, a key_averages summary and
run metadata. Gates and fused blocks become named ranges, and --nvtx
also emits NVTX ranges for Nsight Systems on CUDA. Labels are off by
default so normal runs and torch.compile graphs are unchanged.
---
 Makefile                        |  5 +-
 src/qnnbench/profile.py         | 97 +++++++++++++++++++++++++++++++++
 src/qnnbench/sim/statevector.py | 36 ++++++++++--
 tests/test_profile.py           | 15 +++++
 4 files changed, 148 insertions(+), 5 deletions(-)
 create mode 100644 src/qnnbench/profile.py
 create mode 100644 tests/test_profile.py

diff --git a/Makefile b/Makefile
index 0dbc766..0c61b4c 100644
--- a/Makefile
+++ b/Makefile
@@ -3,7 +3,7 @@
 TORCH_INDEX ?= https://download.pytorch.org/whl/cpu
 PY := .venv/bin/python
 
-.PHONY: install test test-all lint format bench bench-quick experiments hybrid serve loadtest docker legacy
+.PHONY: install test test-all lint format bench bench-quick profile experiments hybrid serve loadtest docker legacy
 
 install:
 	uv venv --python-preference only-managed --python 3.12 .venv
@@ -30,6 +30,9 @@ bench:            ## all suites on the default device; results/ <suite>/<device>
 bench-quick:
 	$(PY) -m qnnbench.bench all --quick
 
+profile:          ## torch.profiler trace of a training step -> runs/profile/trace.json
+	$(PY) -m qnnbench.profile
+
 experiments:      ## 5-seed accuracy comparison (paper protocol)
 	$(PY) -m qnnbench.experiments --seeds 5 --epochs 3
 
diff --git a/src/qnnbench/profile.py b/src/qnnbench/profile.py
new file mode 100644
index 0000000..8787775
--- /dev/null
+++ b/src/qnnbench/profile.py
@@ -0,0 +1,97 @@
+"""Profile QNN training steps with torch.profiler and export a timeline trace.
+
+    python -m qnnbench.profile --n-qubits 17 --fuse 5 --out runs/profile
+    # open runs/profile/trace.json in https://ui.perfetto.dev or chrome://tracing
+
+Each gate and fused block is a named range ("fused_dense:k=5,4gates",
+"fused_diag:16gates", ...), so the trace shows where a step's time goes per
+circuit block, with the aten kernels underneath. On CUDA the trace also has
+the GPU stream: gaps between kernels there are launch overhead or host
+synchronisation. ``--nvtx`` additionally emits NVTX ranges for Nsight Systems:
+
+    nsys profile -t cuda,nvtx python -m qnnbench.profile --device cuda --nvtx
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import torch
+from torch.profiler import ProfilerActivity, profile, schedule
+
+from qnnbench.env import env_info, resolve_device
+from qnnbench.models import QNN
+from qnnbench.sim import statevector
+
+
+def run(
+    n_qubits: int = 17,
+    batch: int = 32,
+    fuse: int = 5,
+    grad_method: str = "autograd",
+    device: str = "auto",
+    steps: int = 3,
+    nvtx: bool = False,
+    out: Path = Path("runs/profile"),
+    row_limit: int = 15,
+) -> dict:
+    dev = resolve_device(device)
+    torch.manual_seed(0)
+    model = QNN(n_data=n_qubits - 1, fuse=fuse, grad_method=grad_method).to(dev)
+    x = (torch.rand(batch, n_qubits - 1) > 0.5).float().to(dev)
+
+    activities = [ProfilerActivity.CPU]
+    if dev.type == "cuda":
+        activities.append(ProfilerActivity.CUDA)
+
+    statevector.LABELS.update(enabled=True, nvtx=nvtx and dev.type == "cuda")
+    try:
+        # wait 1 step, warm up 1 (profiler running, results discarded), record `steps`.
+        with profile(
+            activities=activities,
+            schedule=schedule(wait=1, warmup=1, active=steps),
+            record_shapes=True,
+            profile_memory=True,
+        ) as prof:
+            for _ in range(steps + 2):
+                with torch.profiler.record_function("train_step"):
+                    model.zero_grad(set_to_none=True)
+                    model(x).sum().backward()
+                prof.step()
+    finally:
+        statevector.LABELS.update(enabled=False, nvtx=False)
+
+    out.mkdir(parents=True, exist_ok=True)
+    trace = out / "trace.json"
+    prof.export_chrome_trace(str(trace))
+    sort = "self_cuda_time_total" if dev.type == "cuda" else "self_cpu_time_total"
+    table = prof.key_averages().table(sort_by=sort, row_limit=row_limit)
+    (out / "summary.txt").write_text(table)
+
+    config = dict(n_qubits=n_qubits, batch=batch, fuse=fuse, grad_method=grad_method,
+                  steps=steps)  # fmt: skip
+    meta = {"config": config, "env": env_info(dev), "trace": str(trace)}
+    (out / "meta.json").write_text(json.dumps(meta, indent=2))
+    print(table)
+    print(f"-> {trace} (open in https://ui.perfetto.dev)")
+    return meta
+
+
+def main(argv=None):
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawTextHelpFormatter)
+    p.add_argument("--n-qubits", type=int, default=17)
+    p.add_argument("--batch", type=int, default=32)
+    p.add_argument("--fuse", type=int, default=5)
+    p.add_argument("--grad-method", default="autograd")
+    p.add_argument("--device", default="auto")
+    p.add_argument("--steps", type=int, default=3)
+    p.add_argument("--nvtx", action="store_true", help="emit NVTX ranges (CUDA only)")
+    p.add_argument("--out", type=Path, default=Path("runs/profile"))
+    args = p.parse_args(argv)
+    run(**vars(args))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/qnnbench/sim/statevector.py b/src/qnnbench/sim/statevector.py
index b84991c..a369775 100644
--- a/src/qnnbench/sim/statevector.py
+++ b/src/qnnbench/sim/statevector.py
@@ -14,6 +14,8 @@
 
 from __future__ import annotations
 
+import contextlib
+
 import torch
 
 from qnnbench.sim.circuit import Circuit, Op
@@ -22,6 +24,28 @@
 
 Tensor = torch.Tensor
 
+# Named profiler ranges per gate / fused block. Off by default so normal runs
+# and torch.compile graphs are untouched; `qnnbench.profile` switches it on.
+# With NVTX on, the same names appear in Nsight Systems timelines on CUDA.
+LABELS = {"enabled": False, "nvtx": False}
+
+
+@contextlib.contextmanager
+def _labelled(name: str):
+    with torch.profiler.record_function(name):
+        if LABELS["nvtx"]:
+            torch.cuda.nvtx.range_push(name)
+            try:
+                yield
+            finally:
+                torch.cuda.nvtx.range_pop()
+        else:
+            yield
+
+
+def region(name: str):
+    return _labelled(name) if LABELS["enabled"] else contextlib.nullcontext()
+
 
 # --------------------------------------------------------------------------- state prep
 
@@ -134,16 +158,20 @@ def simulate(
     n = circuit.n_qubits
     if plan is None:
         for op, mat in zip(circuit.ops, mats, strict=True):
-            state = apply(state, mat, op, n)
+            with region(f"gate:{op.gate}{list(op.qubits)}"):
+                state = apply(state, mat, op, n)
         return state
     for block in plan:
         if len(block.ops) == 1:
             i = block.ops[0]
-            state = apply(state, mats[i], circuit.ops[i], n)
+            with region(f"gate:{circuit.ops[i].gate}{list(circuit.ops[i].qubits)}"):
+                state = apply(state, mats[i], circuit.ops[i], n)
         elif block.diagonal:
-            state = state * _fused_diagonal(circuit, mats, block, state)
+            with region(f"fused_diag:{len(block.ops)}gates"):
+                state = state * _fused_diagonal(circuit, mats, block, state)
         else:
-            state = apply_kq(state, _fused_unitary(circuit, mats, block), block.qubits, n)
+            with region(f"fused_dense:k={len(block.qubits)},{len(block.ops)}gates"):
+                state = apply_kq(state, _fused_unitary(circuit, mats, block), block.qubits, n)
     return state
 
 
diff --git a/tests/test_profile.py b/tests/test_profile.py
new file mode 100644
index 0000000..fc3baf9
--- /dev/null
+++ b/tests/test_profile.py
@@ -0,0 +1,15 @@
+import json
+
+from qnnbench.profile import run
+from qnnbench.sim import statevector
+
+
+def test_profile_writes_trace_with_named_blocks(tmp_path):
+    meta = run(n_qubits=7, batch=4, steps=1, out=tmp_path, device="cpu")
+    events = json.loads((tmp_path / "trace.json").read_text())["traceEvents"]
+    names = {e.get("name", "") for e in events}
+    assert "train_step" in names
+    assert any(n.startswith("fused_") for n in names)
+    assert (tmp_path / "summary.txt").read_text().strip()
+    assert meta["config"]["n_qubits"] == 7
+    assert statevector.LABELS["enabled"] is False  # labels switched back off

From 223da5ca91d5ca2b5de277ab280fde9cd76fefca Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 13:54:26 +0200
Subject: [PATCH 05/13] Type-check with mypy and gate coverage at 70%

- mypy (check_untyped_defs) passes. Fixes it found: Callable instead
  of the builtin callable in train.build_model; a clear error instead
  of indexing params=None for a parameterised gate; explicit invariants
  for derivatives and shift rules; DDP code keeps separate references
  to the network and its wrapper instead of swapping one variable.
- New tests for the experiment summary, every plot, the load test
  (driving the real app in-process via an injectable transport) and
  the log formatters. Coverage 65% -> 75%; the floor is 70% because
  CUDA-only branches can't run on CPU runners.
---
 pyproject.toml                  | 21 ++++++++
 src/qnnbench/env.py             |  2 +-
 src/qnnbench/experiments.py     | 28 ++++++----
 src/qnnbench/hybrid.py          | 25 +++++----
 src/qnnbench/loadtest.py        |  7 ++-
 src/qnnbench/serve.py           |  3 +-
 src/qnnbench/sim/statevector.py |  8 ++-
 src/qnnbench/train.py           | 10 ++--
 tests/test_tools.py             | 95 +++++++++++++++++++++++++++++++++
 9 files changed, 171 insertions(+), 28 deletions(-)
 create mode 100644 tests/test_tools.py

diff --git a/pyproject.toml b/pyproject.toml
index 8c251dc..1f793bf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,6 +17,8 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
     "pytest>=8",
+    "pytest-cov>=5",
+    "mypy>=1.10",
     "ruff>=0.5",
     "cirq-core>=1.3",   # reference simulator for correctness tests
 ]
@@ -47,6 +49,25 @@ select = ["E", "F", "I", "B", "UP", "SIM"]
 # Suites build closures over loop variables and call them within the same iteration.
 "src/qnnbench/bench/suites.py" = ["B023"]
 
+[tool.mypy]
+python_version = "3.10"
+files = ["src"]
+check_untyped_defs = true
+warn_unused_ignores = true
+warn_redundant_casts = true
+ignore_missing_imports = true  # cirq, pennylane, prometheus_client ship partial or no stubs
+
+[tool.coverage.run]
+source = ["qnnbench"]
+branch = true
+omit = ["*/__main__.py"]
+
+[tool.coverage.report]
+# CUDA-only branches (streams, CUDA events, NCCL) cannot run on CPU CI runners.
+fail_under = 70
+show_missing = true
+skip_covered = true
+
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 addopts = "-ra"
diff --git a/src/qnnbench/env.py b/src/qnnbench/env.py
index 997a283..f8ca290 100644
--- a/src/qnnbench/env.py
+++ b/src/qnnbench/env.py
@@ -47,7 +47,7 @@ def device_name(device: torch.device) -> str:
 
 def env_info(device: torch.device) -> dict:
     """Everything needed to tell whether two benchmark results are comparable."""
-    info = {
+    info: dict[str, object] = {
         "timestamp": datetime.now(timezone.utc).isoformat(timespec="seconds"),
         "git_sha": git_sha(),
         "python": platform.python_version(),
diff --git a/src/qnnbench/experiments.py b/src/qnnbench/experiments.py
index c1b014d..b717bbc 100644
--- a/src/qnnbench/experiments.py
+++ b/src/qnnbench/experiments.py
@@ -14,11 +14,12 @@
 import statistics
 from dataclasses import asdict
 from pathlib import Path
+from typing import Any
 
 from qnnbench.data import prepare_binary_mnist
 from qnnbench.train import TrainConfig, lookup_table_accuracy, train
 
-RUNS = {
+RUNS: dict[str, dict[str, Any]] = {
     "QNN (500 examples)": dict(model="qnn", num_train=500),
     "QNN": dict(model="qnn"),
     "Fair MLP": dict(model="mlp"),
@@ -46,6 +47,20 @@ def main(argv=None):
             cfg = TrainConfig(epochs=args.epochs, seed=seed, device=args.device, **overrides)
             runs[name].append(asdict(train(cfg, data)))
 
+    summary, table = summarize(runs, ceiling, args.seeds, args.epochs)
+    print("\n" + table)
+
+    args.out.mkdir(parents=True, exist_ok=True)
+    (args.out / f"accuracy_{args.epochs}ep.json").write_text(
+        json.dumps({"summary": summary, "ceiling": ceiling, "runs": runs}, indent=2)
+    )
+    (args.out / f"accuracy_{args.epochs}ep.md").write_text(table + "\n")
+
+
+def summarize(
+    runs: dict[str, list[dict]], ceiling: dict, seeds: int, epochs: int
+) -> tuple[dict, str]:
+    """Per-model accuracy statistics and the markdown table for the README."""
     summary = {}
     for name, results in runs.items():
         accs = [r["history"][-1]["test_acc"] for r in results]
@@ -61,7 +76,7 @@ def main(argv=None):
         }
 
     lines = [
-        f"| Model | Params | Test accuracy ({args.seeds} seeds, {args.epochs} epochs) | Range |",
+        f"| Model | Params | Test accuracy ({seeds} seeds, {epochs} epochs) | Range |",
         "|---|---|---|---|",
     ]
     for name, s in summary.items():
@@ -74,14 +89,7 @@ def main(argv=None):
         f"| Lookup table (Bayes ceiling) | {ceiling['n_patterns']} patterns | "
         f"{100 * ceiling['test_acc']:.1f}% | deterministic |"
     )
-    table = "\n".join(lines)
-    print("\n" + table)
-
-    args.out.mkdir(parents=True, exist_ok=True)
-    (args.out / f"accuracy_{args.epochs}ep.json").write_text(
-        json.dumps({"summary": summary, "ceiling": ceiling, "runs": runs}, indent=2)
-    )
-    (args.out / f"accuracy_{args.epochs}ep.md").write_text(table + "\n")
+    return summary, "\n".join(lines)
 
 
 if __name__ == "__main__":
diff --git a/src/qnnbench/hybrid.py b/src/qnnbench/hybrid.py
index 3eb141d..f5ae087 100644
--- a/src/qnnbench/hybrid.py
+++ b/src/qnnbench/hybrid.py
@@ -169,7 +169,9 @@ def train(cfg: HybridConfig, log=print) -> dict:
     if world > 1 and is_main:
         dist.barrier()
 
-    sampler = DistributedSampler(train_ds, seed=cfg.seed) if world > 1 else None
+    sampler: DistributedSampler | None = (
+        DistributedSampler(train_ds, seed=cfg.seed) if world > 1 else None
+    )
     pin = device.type == "cuda"
     loader = DataLoader(
         train_ds,
@@ -182,11 +184,15 @@ def train(cfg: HybridConfig, log=print) -> dict:
         drop_last=True,
     )
 
-    model = build_model(cfg.features).to(device)
-    if world > 1:
-        model = nn.parallel.DistributedDataParallel(
-            model, device_ids=[device.index] if device.type == "cuda" else None
+    net = build_model(cfg.features).to(device)
+    ddp = (
+        nn.parallel.DistributedDataParallel(
+            net, device_ids=[device.index] if device.type == "cuda" else None
         )
+        if world > 1
+        else None
+    )
+    model: nn.Module = ddp or net  # what we train; `net` stays the unwrapped module
     forward = torch.compile(model) if cfg.compile else model
     params = [p for p in model.parameters() if p.requires_grad]
     opt = torch.optim.AdamW(params, lr=cfg.lr)
@@ -210,7 +216,7 @@ def train(cfg: HybridConfig, log=print) -> dict:
             yb = yb.to(device, non_blocking=pin)
             last_micro = (step + 1) % cfg.accum_steps == 0
             # Skip DDP's gradient all-reduce on all but the last micro-batch.
-            sync = contextlib.nullcontext() if last_micro or world == 1 else model.no_sync()
+            sync = ddp.no_sync() if ddp is not None and not last_micro else contextlib.nullcontext()
             with sync:
                 with torch.autocast(device.type, dtype=dtype or torch.float32, enabled=bool(dtype)):
                     loss = F.cross_entropy(forward(xb), yb)
@@ -251,8 +257,7 @@ def train(cfg: HybridConfig, log=print) -> dict:
         "env": env_info(device),
     }
     if is_main and cfg.quantize:
-        plain = model.module if world > 1 else model
-        result["quantization"] = quantize_and_compare(plain, test_ds, cfg)
+        result["quantization"] = quantize_and_compare(net, test_ds, cfg)
         say(f"[{cfg.features}] int8: {json.dumps(result['quantization'])}")
     if world > 1:
         dist.destroy_process_group()
@@ -325,11 +330,11 @@ def main(argv=None):
     p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawTextHelpFormatter)
     for f in HybridConfig.__dataclass_fields__.values():
         flag = f"--{f.name.replace('_', '-')}"
-        if f.type == "bool":
+        if str(f.type) == "bool":
             p.add_argument(flag, action=argparse.BooleanOptionalAction, default=f.default)
         else:
             p.add_argument(
-                flag, type={"int": int, "float": float}.get(f.type, str), default=f.default
+                flag, type={"int": int, "float": float}.get(str(f.type), str), default=f.default
             )
     p.add_argument("--out", type=Path, help="write the run as JSON (rank 0)")
     args = vars(p.parse_args(argv))
diff --git a/src/qnnbench/loadtest.py b/src/qnnbench/loadtest.py
index c79ce10..615ab61 100644
--- a/src/qnnbench/loadtest.py
+++ b/src/qnnbench/loadtest.py
@@ -24,7 +24,10 @@
 import httpx
 
 
-async def run_load(url: str, concurrency: int, n_requests: int) -> dict:
+async def run_load(
+    url: str, concurrency: int, n_requests: int, transport: httpx.AsyncBaseTransport | None = None
+) -> dict:
+    """Closed loop: ``concurrency`` clients, each sending its next request on a response."""
     latencies: list[float] = []
     errors = 0
     status_counts: dict[int, int] = {}
@@ -49,7 +52,7 @@ async def client(http: httpx.AsyncClient):
                 errors += 1
 
     limits = httpx.Limits(max_connections=concurrency)
-    async with httpx.AsyncClient(timeout=60, limits=limits) as http:
+    async with httpx.AsyncClient(timeout=60, limits=limits, transport=transport) as http:
         t0 = time.perf_counter()
         await asyncio.gather(*(client(http) for _ in range(concurrency)))
         elapsed = time.perf_counter() - t0
diff --git a/src/qnnbench/serve.py b/src/qnnbench/serve.py
index 6e20d7d..b5b53a8 100644
--- a/src/qnnbench/serve.py
+++ b/src/qnnbench/serve.py
@@ -37,6 +37,7 @@
 from concurrent.futures import ThreadPoolExecutor
 from contextlib import asynccontextmanager
 from dataclasses import dataclass, fields
+from typing import Any
 
 import torch
 from fastapi import FastAPI, HTTPException, Response
@@ -76,7 +77,7 @@ class Settings:
 
     @classmethod
     def from_env(cls, env=os.environ) -> Settings:
-        values = {}
+        values: dict[str, Any] = {}
         for f in fields(cls):
             raw = env.get(f"QNNBENCH_{f.name.upper()}")
             if raw is None or raw == "":
diff --git a/src/qnnbench/sim/statevector.py b/src/qnnbench/sim/statevector.py
index a369775..17cda65 100644
--- a/src/qnnbench/sim/statevector.py
+++ b/src/qnnbench/sim/statevector.py
@@ -117,7 +117,7 @@ def apply(state: Tensor, mat: Tensor, op: Op, n: int) -> Tensor:
     diagonal = GATES[op.gate].diagonal
     if len(op.qubits) == 1:
         return apply_1q(state, mat, op.qubits[0], n, diagonal)
-    return apply_2q(state, mat, op.qubits, n, diagonal)
+    return apply_2q(state, mat, (op.qubits[0], op.qubits[1]), n, diagonal)
 
 
 def dagger(mat: Tensor, diagonal: bool) -> Tensor:
@@ -129,6 +129,10 @@ def dagger(mat: Tensor, diagonal: bool) -> Tensor:
 
 def _param(op: Op, params: Tensor | None, real_dtype) -> Tensor | None:
     if op.param is not None:
+        if params is None:
+            raise ValueError(
+                f"{op.gate} on {op.qubits} needs parameter {op.param}, got params=None"
+            )
         return params[op.param]
     if op.value is not None:
         return torch.tensor(op.value, dtype=real_dtype)
@@ -247,6 +251,7 @@ def backward(ctx, grad_out):
                 gate = GATES[op.gate]
                 psi = apply(psi, dagger(mat, gate.diagonal), op, n)
                 if op.param is not None:
+                    assert gate.derivative is not None  # Op() rejects params on fixed gates
                     dmat = gate.derivative(params[op.param], dtype, device)
                     mu = apply(psi, dmat, op, n)
                     grad[:, op.param] += 2 * (lam.conj() * mu).real.sum(dim=1)
@@ -283,6 +288,7 @@ def backward(ctx, grad_out):
                 if op.param is None:
                     continue
                 gate = GATES[op.gate]
+                assert gate.shift is not None and gate.shift_coeff is not None
                 evals = []
                 for sign in (1, -1):
                     # Shift only this occurrence, so shared parameters sum correctly.
diff --git a/src/qnnbench/train.py b/src/qnnbench/train.py
index 7253990..9cb1fbc 100644
--- a/src/qnnbench/train.py
+++ b/src/qnnbench/train.py
@@ -10,6 +10,7 @@
 import argparse
 import json
 import time
+from collections.abc import Callable
 from dataclasses import asdict, dataclass, field
 from pathlib import Path
 
@@ -64,7 +65,10 @@ def hinge_loss(pred: torch.Tensor, y_pm1: torch.Tensor) -> torch.Tensor:
     return torch.clamp(1 - pred * y_pm1, min=0).mean()
 
 
-def build_model(cfg: TrainConfig) -> tuple[nn.Module, callable]:
+LossFn = Callable[[torch.Tensor, torch.Tensor], torch.Tensor]
+
+
+def build_model(cfg: TrainConfig) -> tuple[nn.Module, LossFn]:
     """Return the model and its loss; both models take labels in {-1, +1}."""
     if cfg.model == "qnn":
         model = QNN(grad_method=cfg.grad_method, dtype=getattr(torch, cfg.dtype), fuse=cfg.fuse)
@@ -161,10 +165,10 @@ def lookup_table_accuracy(data=None) -> dict:
 def main(argv=None):
     p = argparse.ArgumentParser(description="Train the QNN or the fair MLP baseline.")
     for f in TrainConfig.__dataclass_fields__.values():
-        if f.type == "bool":
+        if str(f.type) == "bool":
             p.add_argument(f"--{f.name.replace('_', '-')}", action="store_true")
         else:
-            caster = {"int": int, "float": float, "int | None": int}.get(f.type, str)
+            caster = {"int": int, "float": float, "int | None": int}.get(str(f.type), str)
             p.add_argument(f"--{f.name.replace('_', '-')}", type=caster, default=f.default)
     p.add_argument("--out", type=Path, help="write the run (config, history, env) as JSON")
     p.add_argument("--save", type=Path, help="save the trained weights (state_dict)")
diff --git a/tests/test_tools.py b/tests/test_tools.py
new file mode 100644
index 0000000..1239523
--- /dev/null
+++ b/tests/test_tools.py
@@ -0,0 +1,95 @@
+"""Tests for the reporting and operations tooling: experiments, plots, load test, logging."""
+
+import asyncio
+import json
+import logging
+
+import pytest
+
+from qnnbench.experiments import summarize
+from qnnbench.logs import JsonFormatter, TextFormatter, configure_logging
+
+
+def _run(acc: float, n_params: int = 32) -> dict:
+    return {"n_params": n_params, "history": [{"test_acc": acc, "samples_per_s": 100.0}]}
+
+
+def test_summarize_reports_mean_std_range_and_ceiling():
+    runs = {"QNN": [_run(0.8), _run(0.9)], "MLP": [_run(0.91, 37)]}
+    ceiling = {"n_patterns": 193, "test_acc": 0.914}
+    summary, table = summarize(runs, ceiling, seeds=2, epochs=3)
+    assert summary["QNN"]["test_acc_mean"] == pytest.approx(0.85)
+    assert summary["QNN"]["test_acc_std"] > 0
+    assert summary["MLP"]["test_acc_std"] == 0.0  # single run
+    assert "| QNN | 32 | 85.0% ± 7.1 | 80.0–90.0% |" in table
+    assert "Bayes ceiling" in table and "91.4%" in table
+
+
+def _result_file(tmp_path, suite: str, measurements: list[dict]):
+    env = {"device": "cpu", "device_name": "Intel(R) Core(TM) i7-1065G7 CPU @ 1.30GHz"}
+    path = tmp_path / suite / "cpu.json"
+    path.parent.mkdir(parents=True)
+    path.write_text(json.dumps({"suite": suite, "env": env, "measurements": measurements}))
+
+
+def test_plot_renders_every_suite(tmp_path):
+    pytest.importorskip("matplotlib")
+    from qnnbench.bench.plot import main
+
+    def m(case, p50, **extra):
+        return {"case": case, "p50_ms": p50, "throughput": 32 / p50, "extra": extra}
+
+    _result_file(tmp_path, "qubits", [m({"n_qubits": n, "fuse": f}, n * (2 - f / 5))
+                                      for n in (7, 9) for f in (0, 5)])  # fmt: skip
+    _result_file(tmp_path, "fusion", [m({"n_qubits": 9, "fuse": k, "pass": p}, 10 - k)
+                                      for k in (0, 2) for p in ("forward", "fwd+bwd")])  # fmt: skip
+    grad = [m({"n_qubits": n, "method": meth, "fuse": 5}, n, saved_MB=n)
+            for n in (9, 13) for meth in ("autograd", "adjoint")]  # fmt: skip
+    _result_file(tmp_path, "grad", grad)
+    _result_file(tmp_path, "frameworks", [m({"n_qubits": n, "framework": "pennylane"}, n)
+                                          for n in (9, 13)])  # fmt: skip
+    _result_file(tmp_path, "batch", [m({"n_qubits": 9, "batch": b}, b) for b in (1, 2, 4)])
+    main(["--results", str(tmp_path), "--out", str(tmp_path / "figs")])
+    names = sorted(p.name for p in (tmp_path / "figs").glob("*.png"))
+    assert names == [f"{s}-intel-i7-1065g7-cpu.png"
+                     for s in ("batch", "frameworks", "fusion", "grad", "qubits")]  # fmt: skip
+
+
+def test_load_test_against_in_process_app(monkeypatch):
+    pytest.importorskip("fastapi")
+    import httpx
+
+    from qnnbench.loadtest import run_load
+    from qnnbench.serve import app
+
+    monkeypatch.setenv("QNNBENCH_DEVICE", "cpu")
+
+    async def scenario():
+        async with app.router.lifespan_context(app):
+            transport = httpx.ASGITransport(app=app)
+            return await run_load("http://test", concurrency=4, n_requests=12, transport=transport)
+
+    r = asyncio.run(scenario())
+    assert r["requests"] == 12 and r["errors"] == 0 and r["status_counts"] == {200: 12}
+    assert r["p50_ms"] > 0 and "qnnbench_batch_size_bucket" in r["server_metrics"]
+
+
+def _record(**extra) -> logging.LogRecord:
+    record = logging.makeLogRecord({"name": "qnnbench.t", "levelname": "INFO", "msg": "done"})
+    record.__dict__.update(extra)
+    return record
+
+
+def test_json_formatter_lifts_extra_fields():
+    out = json.loads(JsonFormatter().format(_record(epoch=3, test_acc=0.91)))
+    assert out["msg"] == "done" and out["level"] == "info"
+    assert out["epoch"] == 3 and out["test_acc"] == 0.91
+
+
+def test_text_formatter_and_configure():
+    assert TextFormatter().format(_record(epoch=3)) == "I qnnbench.t: done  epoch=3"
+    configure_logging("json", "DEBUG")
+    assert isinstance(logging.getLogger("qnnbench").handlers[0].formatter, JsonFormatter)
+    with pytest.raises(ValueError):
+        configure_logging("xml")
+    configure_logging("text", "INFO")

From cda1496906d08eb7c9b04c96a183cb6afddfa08d Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 13:58:53 +0200
Subject: [PATCH 06/13] Lock dependencies; harden CI with mypy, coverage, audit
 and pinned actions

- uv.lock pins all 94 packages; locked installs route torch to the
  PyTorch CPU index (uv sources), and GPU machines install with
  --no-sources to get the CUDA build. CI uses uv sync --locked, which
  also fails if the lock is stale.
- CI: mypy in lint, coverage floor in tests (XML artifact), a profiler
  smoke run, and pip-audit over the locked set (torch audited by its
  release version, since +cpu builds are not on PyPI).
- Every action is pinned to a full commit SHA with its tag noted.
- Docker workflow: builds on PRs that touch the image, pushes
  sha-<commit>/main/semver tags to GHCR from main and tags, and
  uploads a Trivy scan to the Security tab (report-only until the
  base image's CVE baseline is reviewed).
- Dependabot: weekly grouped updates for uv, actions and the base
  image, with PyTorch in its own PR so it can be benchmarked.
- pre-commit: large-file guard (the old committed venv), YAML/TOML
  checks, private-key detection, and ruff/mypy/lock checks run at
  the locked versions; legacy/ is excluded.
- Result JSON files end with a newline.
---
 .github/dependabot.yml                        |   35 +
 .github/workflows/ci.yml                      |   76 +-
 .github/workflows/docker.yml                  |   71 +
 .gitignore                                    |    2 +
 .pre-commit-config.yaml                       |   41 +
 Makefile                                      |   23 +-
 pyproject.toml                                |   13 +
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 results/experiments/accuracy_3ep.json         |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 results/hybrid/learned.json                   |    2 +-
 results/hybrid/quanv.json                     |    2 +-
 results/hybrid/random.json                    |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 ...intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json |    2 +-
 results/serve/loadtest-cpu.json               |    2 +-
 src/qnnbench/bench/harness.py                 |    2 +-
 src/qnnbench/experiments.py                   |    2 +-
 src/qnnbench/hybrid.py                        |    2 +-
 src/qnnbench/loadtest.py                      |    2 +-
 src/qnnbench/profile.py                       |    2 +-
 src/qnnbench/train.py                         |    2 +-
 uv.lock                                       | 3477 +++++++++++++++++
 30 files changed, 3715 insertions(+), 67 deletions(-)
 create mode 100644 .github/dependabot.yml
 create mode 100644 .github/workflows/docker.yml
 create mode 100644 .pre-commit-config.yaml
 create mode 100644 uv.lock

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000..807675a
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,35 @@
+# Weekly, grouped dependency updates: a handful of PRs instead of dozens.
+# The legacy TFQ stack (legacy/tfq/requirements.txt) is pinned on purpose
+# for reproducibility and is deliberately not listed here.
+version: 2
+updates:
+  - package-ecosystem: uv
+    directory: /
+    schedule: {interval: weekly, day: monday}
+    open-pull-requests-limit: 5
+    labels: [dependencies]
+    groups:
+      # Separate PR: a PyTorch upgrade can change kernel performance, so check
+      # it against the benchmark baselines (qnnbench.bench.compare), not just tests.
+      pytorch:
+        patterns: ["torch"]
+      quantum-baselines:
+        patterns: ["cirq*", "pennylane*"]
+      dev-tools:
+        patterns: ["pytest*", "ruff", "mypy", "coverage"]
+      runtime:
+        patterns: ["*"]
+        update-types: [minor, patch]
+
+  - package-ecosystem: github-actions
+    directory: /
+    schedule: {interval: weekly, day: monday}
+    labels: [dependencies, ci]
+    groups:
+      actions:
+        patterns: ["*"]
+
+  - package-ecosystem: docker
+    directory: /
+    schedule: {interval: weekly, day: monday}
+    labels: [dependencies, docker]
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 3b0cf2e..f1cc7c2 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -9,18 +9,22 @@ concurrency:
   group: ci-${{ github.ref }}
   cancel-in-progress: true
 
-env:
-  # CPU wheels: CI has no GPU, and the CUDA wheels are ~2 GB larger.
-  TORCH_INDEX: https://download.pytorch.org/whl/cpu
+permissions:
+  contents: read
 
+# Actions are pinned to commit SHAs (tags can be moved); Dependabot bumps them.
 jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
-      - uses: astral-sh/setup-uv@v5
-      - run: uvx ruff check src tests
-      - run: uvx ruff format --check src tests
+      - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5
+      - uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
+        with:
+          python-version: "3.12"
+      - run: uv sync --locked --extra dev --extra serve --extra bench
+      - run: uv run ruff check src tests
+      - run: uv run ruff format --check src tests
+      - run: uv run mypy
 
   test:
     runs-on: ubuntu-latest
@@ -29,51 +33,47 @@ jobs:
       matrix:
         python: ["3.10", "3.12"]
     steps:
-      - uses: actions/checkout@v5
-      - uses: astral-sh/setup-uv@v5
+      - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5
+      - uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
         with:
           python-version: ${{ matrix.python }}
-          activate-environment: true  # setup-uv creates and activates .venv
-      - name: Install
-        run: |
-          uv pip install torch --index-url "$TORCH_INDEX"
-          uv pip install -e ".[dev,serve]"
+      # --locked fails if uv.lock is out of date with pyproject.toml.
+      - run: uv sync --locked --extra dev --extra serve --extra bench
       - name: Cache MNIST
-        uses: actions/cache@v4
+        uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830 # v4
         with:
           path: data/mnist.npz
           key: mnist-731c5ac6
-      - name: Tests
-        run: python -m pytest -m "not slow"
-      - name: Slow tests (quick benchmark suites, hybrid training)
-        run: python -m pytest -m slow
+      - name: Tests with coverage (fails under the floor in pyproject.toml)
+        run: uv run pytest --cov --cov-report=term --cov-report=xml
+      - uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4
+        if: always()
+        with:
+          name: coverage-${{ matrix.python }}
+          path: coverage.xml
 
   bench-smoke:
     # Catches benchmark code that crashes. Timings on shared runners are too
     # noisy to gate on; use `qnnbench.bench.compare` on dedicated hardware.
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
-      - uses: astral-sh/setup-uv@v5
+      - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5
+      - uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
         with:
           python-version: "3.12"
-          activate-environment: true
-      - name: Install
-        run: |
-          uv pip install torch --index-url "$TORCH_INDEX"
-          uv pip install -e ".[dev,bench]"
-      - run: python -m qnnbench.bench qubits batch fusion grad dtype quanv --quick
+      - run: uv sync --locked --extra dev --extra bench
+      - run: uv run python -m qnnbench.bench qubits batch fusion grad dtype quanv --quick
+      - run: uv run python -m qnnbench.profile --n-qubits 9 --steps 1 --out runs/profile
 
-  docker:
-    if: github.event_name == 'push'
+  audit:
+    # Known vulnerabilities in the locked dependency set.
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v5
-      - uses: docker/setup-buildx-action@v3
-      - uses: docker/build-push-action@v6
-        with:
-          context: .
-          push: false
-          tags: qnnbench:ci
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
+      - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5
+      - uses: astral-sh/setup-uv@d4b2f3b6ecc6e67c4457f6d3e41ec42d3d0fcb86 # v5
+      - name: Export locked requirements
+        # "+cpu" local versions are not on PyPI; audit the same torch release instead.
+        run: |
+          uv export --frozen --no-hashes --no-emit-project \
+            --extra dev --extra serve --extra bench | sed 's/+cpu//' > requirements-audit.txt
+      - run: uvx pip-audit -r requirements-audit.txt --no-deps --disable-pip --progress-spinner off
diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
new file mode 100644
index 0000000..30250c9
--- /dev/null
+++ b/.github/workflows/docker.yml
@@ -0,0 +1,71 @@
+name: Docker
+
+on:
+  push:
+    branches: [main]
+    tags: ["v*"]
+  pull_request:
+    paths: [Dockerfile, .dockerignore, pyproject.toml, "src/**", .github/workflows/docker.yml]
+
+concurrency:
+  group: docker-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+env:
+  IMAGE: ghcr.io/${{ github.repository_owner }}/qnnbench
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write          # push to GHCR (main and tags only)
+      security-events: write   # upload the Trivy report
+    steps:
+      - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5
+      - uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f # v3
+      - uses: docker/login-action@c94ce9fb468520275223c153574b00df6fe4bcc9 # v3
+        if: github.event_name == 'push'
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+      - id: meta
+        uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5
+        with:
+          images: ${{ env.IMAGE }}
+          # Immutable sha-<commit> tags for deployments; `main` and semver for humans.
+          tags: |
+            type=sha,format=short
+            type=ref,event=branch
+            type=semver,pattern={{version}}
+      - uses: docker/build-push-action@10e90e3645eae34f1e60eeb005ba3a3d33f178e8 # v6
+        with:
+          context: .
+          push: ${{ github.event_name == 'push' }}
+          load: ${{ github.event_name != 'push' }}
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+      - name: Scan image
+        # Report-only for now: the CUDA base image's OS packages carry CVEs we
+        # don't control. Findings go to the Security tab; tighten to a gate
+        # (exit-code: 1) once the baseline is reviewed.
+        uses: aquasecurity/trivy-action@ed142fd0673e97e23eac54620cfb913e5ce36c25 # v0.36.0
+        with:
+          image-ref: ${{ fromJSON(steps.meta.outputs.json).tags[0] }}  # the sha-<commit> tag
+          scanners: vuln
+          severity: HIGH,CRITICAL
+          ignore-unfixed: true
+          format: sarif
+          output: trivy.sarif
+        continue-on-error: true
+      - uses: github/codeql-action/upload-sarif@1190a975f95ce23525efb6a3fc21ea29567c1b52 # v3
+        if: always() && hashFiles('trivy.sarif') != ''
+        with:
+          sarif_file: trivy.sarif
+          category: trivy
diff --git a/.gitignore b/.gitignore
index 578f4d4..2d2c4ea 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,6 +10,7 @@ __pycache__/
 .pytest_cache/
 .ruff_cache/
 .mypy_cache/
+coverage.xml
 build/
 dist/
 
@@ -26,3 +27,4 @@ runs/
 .vscode/
 .idea/
 .DS_Store
+.coverage
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..7fa1d66
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,41 @@
+# pre-commit install   # then these run on every `git commit`
+# legacy/ is the original code, kept as it was for reproducibility.
+exclude: ^legacy/
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: check-added-large-files   # the original repo had a committed virtualenv
+        args: [--maxkb=1024]
+        exclude: ^uv\.lock$
+      - id: check-yaml
+        args: [--allow-multiple-documents]  # k8s manifests
+      - id: check-toml
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: detect-private-key
+  # Local hooks run the tool versions pinned in uv.lock, so they agree with CI.
+  - repo: local
+    hooks:
+      - id: ruff
+        name: ruff check
+        entry: uv run ruff check --fix --force-exclude
+        language: system
+        types: [python]
+      - id: ruff-format
+        name: ruff format
+        entry: uv run ruff format --force-exclude
+        language: system
+        types: [python]
+      - id: uv-lock
+        name: uv.lock matches pyproject.toml
+        entry: uv lock --check
+        language: system
+        files: ^(pyproject\.toml|uv\.lock)$
+        pass_filenames: false
+      - id: mypy
+        name: mypy
+        entry: uv run mypy
+        language: system
+        types: [python]
+        pass_filenames: false
diff --git a/Makefile b/Makefile
index 0c61b4c..05a2dce 100644
--- a/Makefile
+++ b/Makefile
@@ -1,24 +1,33 @@
-# Common tasks. CPU-only torch keeps the local install small; on a GPU box,
-# install torch from PyPI (CUDA wheels) instead: `make install TORCH_INDEX=`.
-TORCH_INDEX ?= https://download.pytorch.org/whl/cpu
+# Common tasks. `make install` uses uv.lock (CPU-only PyTorch, as in CI).
+# On a GPU machine use `make install-gpu`: same packages, CUDA PyTorch from PyPI.
 PY := .venv/bin/python
+EXTRAS := --extra dev --extra bench --extra serve
 
-.PHONY: install test test-all lint format bench bench-quick profile experiments hybrid serve loadtest docker legacy
+.PHONY: install install-gpu hooks test coverage test-all lint format bench bench-quick profile experiments hybrid serve loadtest docker legacy
 
 install:
-	uv venv --python-preference only-managed --python 3.12 .venv
-	uv pip install --python $(PY) torch $(if $(TORCH_INDEX),--index-url $(TORCH_INDEX))
-	uv pip install --python $(PY) -e ".[dev,bench,serve]"
+	uv sync --locked $(EXTRAS)
+
+install-gpu:
+	uv venv --python 3.12 .venv
+	uv pip install --python $(PY) --no-sources -e ".[dev,bench,serve]"
+
+hooks:
+	uv run pre-commit install
 
 test:
 	$(PY) -m pytest -m "not slow"
 
+coverage:
+	$(PY) -m pytest --cov --cov-report=term --cov-report=html
+
 test-all:
 	$(PY) -m pytest
 
 lint:
 	$(PY) -m ruff check src tests
 	$(PY) -m ruff format --check src tests
+	$(PY) -m mypy
 
 format:
 	$(PY) -m ruff format src tests
diff --git a/pyproject.toml b/pyproject.toml
index 1f793bf..fd31065 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,6 +19,7 @@ dev = [
     "pytest>=8",
     "pytest-cov>=5",
     "mypy>=1.10",
+    "pre-commit>=3.7",
     "ruff>=0.5",
     "cirq-core>=1.3",   # reference simulator for correctness tests
 ]
@@ -75,3 +76,15 @@ markers = [
     "network: downloads MNIST (skipped when offline)",
     "slow: takes more than a few seconds",
 ]
+
+# Locked installs (`uv sync`, CI) use CPU-only PyTorch: no GPU there, and the
+# CUDA wheels are ~2 GB larger. On a GPU machine install with
+# `uv pip install -e ".[...]" --no-sources` (or use the Docker image) to get
+# the CUDA build from PyPI.
+[tool.uv.sources]
+torch = [{ index = "pytorch-cpu", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
diff --git a/results/batch/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/batch/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index 21d3b2b..a4346f5 100644
--- a/results/batch/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/batch/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -263,4 +263,4 @@
       "std_ms": 66.99792776180145
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/dtype/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/dtype/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index 1e7ee53..d5038e1 100644
--- a/results/dtype/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/dtype/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -185,4 +185,4 @@
       "std_ms": 141.80604124712156
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/exec/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/exec/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index fd96d00..eaf8765 100644
--- a/results/exec/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/exec/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -235,4 +235,4 @@
       "std_ms": 9.713866519106626
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/experiments/accuracy_3ep.json b/results/experiments/accuracy_3ep.json
index 2f625ad..7f47c13 100644
--- a/results/experiments/accuracy_3ep.json
+++ b/results/experiments/accuracy_3ep.json
@@ -848,4 +848,4 @@
       }
     ]
   }
-}
\ No newline at end of file
+}
diff --git a/results/frameworks/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/frameworks/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index e90b2f1..b298609 100644
--- a/results/frameworks/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/frameworks/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -242,4 +242,4 @@
       "std_ms": 88.44703331159452
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/frameworks/tfq-cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/frameworks/tfq-cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index f44aec1..b4e077b 100644
--- a/results/frameworks/tfq-cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/frameworks/tfq-cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -86,4 +86,4 @@
       "throughput": 221.2735372836883
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/fusion/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/fusion/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index e23f2a6..fd66b92 100644
--- a/results/fusion/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/fusion/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -417,4 +417,4 @@
       "std_ms": 27.328019531273824
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/grad/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/grad/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index 925863f..6762094 100644
--- a/results/grad/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/grad/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -496,4 +496,4 @@
       "std_ms": 465.88718486495924
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/grad/tfq-cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/grad/tfq-cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index 8870fd5..dfb09c1 100644
--- a/results/grad/tfq-cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/grad/tfq-cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -89,4 +89,4 @@
       "throughput": 37.12128079770896
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/hybrid/learned.json b/results/hybrid/learned.json
index f1bb9dc..d62d1f9 100644
--- a/results/hybrid/learned.json
+++ b/results/hybrid/learned.json
@@ -79,4 +79,4 @@
       "cpu_latency_ms_b256": 11.820903149964579
     }
   }
-}
\ No newline at end of file
+}
diff --git a/results/hybrid/quanv.json b/results/hybrid/quanv.json
index 2b85a6a..3e9c95b 100644
--- a/results/hybrid/quanv.json
+++ b/results/hybrid/quanv.json
@@ -79,4 +79,4 @@
       "cpu_latency_ms_b256": 13.717942549919826
     }
   }
-}
\ No newline at end of file
+}
diff --git a/results/hybrid/random.json b/results/hybrid/random.json
index e3e69ab..9a07eeb 100644
--- a/results/hybrid/random.json
+++ b/results/hybrid/random.json
@@ -79,4 +79,4 @@
       "cpu_latency_ms_b256": 10.627692100024433
     }
   }
-}
\ No newline at end of file
+}
diff --git a/results/kernels/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/kernels/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index 2be188b..e0cc29d 100644
--- a/results/kernels/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/kernels/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -151,4 +151,4 @@
       "std_ms": 0.7073713064128166
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/quanv/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/quanv/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index 0ac8c97..19eb919 100644
--- a/results/quanv/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/quanv/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -107,4 +107,4 @@
       "std_ms": 28.979278109784346
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/qubits/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json b/results/qubits/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
index ae48e5e..024aff3 100644
--- a/results/qubits/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
+++ b/results/qubits/cpu-intel-r-core-tm-i7-1065g7-cpu-1-30ghz.json
@@ -468,4 +468,4 @@
       "std_ms": 149.84988577767186
     }
   ]
-}
\ No newline at end of file
+}
diff --git a/results/serve/loadtest-cpu.json b/results/serve/loadtest-cpu.json
index f8068b0..d0b9580 100644
--- a/results/serve/loadtest-cpu.json
+++ b/results/serve/loadtest-cpu.json
@@ -65,4 +65,4 @@
     "server_metrics": "qnnbench_requests_total 1800\nqnnbench_errors_total 0\nqnnbench_batches_total 960\nqnnbench_latency_ms{quantile=\"0.5\"} 27.487\nqnnbench_batch_size{quantile=\"0.5\"} 1\nqnnbench_latency_ms{quantile=\"0.95\"} 319.793\nqnnbench_batch_size{quantile=\"0.95\"} 8\nqnnbench_latency_ms{quantile=\"0.99\"} 445.533\nqnnbench_batch_size{quantile=\"0.99\"} 8\n",
     "max_batch": 64
   }
-]
\ No newline at end of file
+]
diff --git a/src/qnnbench/bench/harness.py b/src/qnnbench/bench/harness.py
index e3d886d..2af6618 100644
--- a/src/qnnbench/bench/harness.py
+++ b/src/qnnbench/bench/harness.py
@@ -141,7 +141,7 @@ def save_results(
     path = Path(out_dir) / suite / f"{device_slug(env)}.json"
     path.parent.mkdir(parents=True, exist_ok=True)
     payload = {"suite": suite, "env": env, "measurements": [m.to_dict() for m in measurements]}
-    path.write_text(json.dumps(payload, indent=2))
+    path.write_text(json.dumps(payload, indent=2) + "\n")
     return path
 
 
diff --git a/src/qnnbench/experiments.py b/src/qnnbench/experiments.py
index b717bbc..062cfeb 100644
--- a/src/qnnbench/experiments.py
+++ b/src/qnnbench/experiments.py
@@ -52,7 +52,7 @@ def main(argv=None):
 
     args.out.mkdir(parents=True, exist_ok=True)
     (args.out / f"accuracy_{args.epochs}ep.json").write_text(
-        json.dumps({"summary": summary, "ceiling": ceiling, "runs": runs}, indent=2)
+        json.dumps({"summary": summary, "ceiling": ceiling, "runs": runs}, indent=2) + "\n"
     )
     (args.out / f"accuracy_{args.epochs}ep.md").write_text(table + "\n")
 
diff --git a/src/qnnbench/hybrid.py b/src/qnnbench/hybrid.py
index f5ae087..162fe8a 100644
--- a/src/qnnbench/hybrid.py
+++ b/src/qnnbench/hybrid.py
@@ -342,7 +342,7 @@ def main(argv=None):
     result = train(HybridConfig(**args))
     if out and int(os.environ.get("RANK", 0)) == 0:
         out.parent.mkdir(parents=True, exist_ok=True)
-        out.write_text(json.dumps(result, indent=2))
+        out.write_text(json.dumps(result, indent=2) + "\n")
 
 
 if __name__ == "__main__":
diff --git a/src/qnnbench/loadtest.py b/src/qnnbench/loadtest.py
index 615ab61..5a3d9ae 100644
--- a/src/qnnbench/loadtest.py
+++ b/src/qnnbench/loadtest.py
@@ -141,7 +141,7 @@ def main(argv=None):
                 proc.wait(timeout=30)
     if args.out:
         args.out.parent.mkdir(parents=True, exist_ok=True)
-        args.out.write_text(json.dumps(rows, indent=2))
+        args.out.write_text(json.dumps(rows, indent=2) + "\n")
 
 
 if __name__ == "__main__":
diff --git a/src/qnnbench/profile.py b/src/qnnbench/profile.py
index 8787775..8d9376e 100644
--- a/src/qnnbench/profile.py
+++ b/src/qnnbench/profile.py
@@ -73,7 +73,7 @@ def run(
     config = dict(n_qubits=n_qubits, batch=batch, fuse=fuse, grad_method=grad_method,
                   steps=steps)  # fmt: skip
     meta = {"config": config, "env": env_info(dev), "trace": str(trace)}
-    (out / "meta.json").write_text(json.dumps(meta, indent=2))
+    (out / "meta.json").write_text(json.dumps(meta, indent=2) + "\n")
     print(table)
     print(f"-> {trace} (open in https://ui.perfetto.dev)")
     return meta
diff --git a/src/qnnbench/train.py b/src/qnnbench/train.py
index 9cb1fbc..332e6c7 100644
--- a/src/qnnbench/train.py
+++ b/src/qnnbench/train.py
@@ -178,7 +178,7 @@ def main(argv=None):
     result = train(cfg, save_path=save)
     if out:
         out.parent.mkdir(parents=True, exist_ok=True)
-        out.write_text(json.dumps(asdict(result), indent=2))
+        out.write_text(json.dumps(asdict(result), indent=2) + "\n")
     return result
 
 
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..180874e
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,3477 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "(python_full_version >= '3.15' and sys_platform == 'linux') or (python_full_version >= '3.15' and sys_platform == 'win32')",
+    "(python_full_version == '3.14.*' and sys_platform == 'linux') or (python_full_version == '3.14.*' and sys_platform == 'win32')",
+    "(python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.14' and sys_platform == 'win32')",
+    "(python_full_version == '3.11.*' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform == 'win32')",
+    "(python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'win32')",
+    "python_full_version >= '3.15' and sys_platform != 'linux' and sys_platform != 'win32'",
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From 3d5ceca811ab8f014839d8442ad7f003538b8c21 Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 14:07:19 +0200
Subject: [PATCH 07/13] Kubernetes: pinned image tags, graceful drain, PDB,
 queue-depth HPA, PyTorchJob

- Manifests pin an immutable release tag instead of :latest; a
  kustomization lets deploys switch to a sha-<commit> build.
- Serving: 2 replicas with a PodDisruptionBudget, zero-unavailability
  rollouts, startup/readiness(/readyz)/liveness probes, a preStop pause
  so endpoints drop the pod before the app drains, uvicorn connection
  limits, JSON logs, Prometheus scrape annotations, and a non-root,
  read-only, capability-free security context.
- HPA scales on the app's queue-depth metric rather than CPU, which
  says little about a GPU-bound service.
- Multi-node DDP as a Kubeflow PyTorchJob; same training code.
- k8s/validate.sh checks everything with kubeconform -strict, including
  the PyTorchJob against the Training Operator's CRD schema; CI runs it.
---
 .github/workflows/ci.yml | 13 +++++++
 k8s/README.md            | 45 +++++++++++++++++++++
 k8s/bench-job.yaml       |  2 +-
 k8s/ddp-job.yaml         |  2 +-
 k8s/kustomization.yaml   | 10 +++++
 k8s/pytorchjob.yaml      | 55 ++++++++++++++++++++++++++
 k8s/serve.yaml           | 84 +++++++++++++++++++++++++++++++++++++---
 k8s/validate.sh          | 28 ++++++++++++++
 8 files changed, 231 insertions(+), 8 deletions(-)
 create mode 100644 k8s/README.md
 create mode 100644 k8s/kustomization.yaml
 create mode 100644 k8s/pytorchjob.yaml
 create mode 100755 k8s/validate.sh

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index f1cc7c2..06002eb 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -77,3 +77,16 @@ jobs:
           uv export --frozen --no-hashes --no-emit-project \
             --extra dev --extra serve --extra bench | sed 's/+cpu//' > requirements-audit.txt
       - run: uvx pip-audit -r requirements-audit.txt --no-deps --disable-pip --progress-spinner off
+
+  k8s:
+    # Schema-validate every manifest (strict), including the kustomized stack
+    # and the Kubeflow PyTorchJob CRD.
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5
+      - name: Install kubeconform
+        run: |
+          curl -fsSL https://github.com/yannh/kubeconform/releases/download/v0.8.0/kubeconform-linux-amd64.tar.gz \
+            | tar xz -C /usr/local/bin kubeconform
+          python3 -m pip install --quiet pyyaml
+      - run: k8s/validate.sh
diff --git a/k8s/README.md b/k8s/README.md
new file mode 100644
index 0000000..47fbb15
--- /dev/null
+++ b/k8s/README.md
@@ -0,0 +1,45 @@
+# Kubernetes manifests
+
+| File | What it runs |
+|---|---|
+| [`serve.yaml`](serve.yaml) | Inference Deployment (2 replicas, GPU), Service, PodDisruptionBudget, queue-depth autoscaler. Applied with `kubectl apply -k k8s/` |
+| [`bench-job.yaml`](bench-job.yaml) | One run of every benchmark suite on a GPU node, results to a PVC |
+| [`ddp-job.yaml`](ddp-job.yaml) | Single-node, 2-GPU data-parallel training with `torchrun` |
+| [`pytorchjob.yaml`](pytorchjob.yaml) | Multi-node data-parallel training (needs the Kubeflow Training Operator) |
+
+## Image tags
+
+Manifests pin `ghcr.io/apayne185/qnnbench:0.1.0`, never `:latest`. A mutable tag
+means two pods of one Deployment can run different code, and a rollback cannot
+restore what actually ran. The Docker workflow publishes:
+
+- `sha-<commit>` for every push to `main`: immutable; deploy these
+- `main`: moves with the branch; for trying things out
+- `X.Y.Z` when a `vX.Y.Z` git tag is pushed
+
+To deploy a specific build of the serving stack:
+
+```bash
+cd k8s && kustomize edit set image ghcr.io/apayne185/qnnbench:sha-1a2b3c4
+kubectl apply -k .
+```
+
+## Serving behaviour
+
+- **Rollouts** keep full capacity (`maxUnavailable: 0`).
+- **Shutdown:** `preStop` waits 5 s so the pod leaves the Service endpoints,
+  then SIGTERM makes `/readyz` return 503 and the app drains its queue before
+  exiting. `terminationGracePeriodSeconds` covers all three steps.
+- **Overload:** uvicorn `--limit-concurrency` refuses excess connections with
+  503, the bounded app queue sheds with 503 + `Retry-After`, and requests past
+  their deadline get 504 without using the GPU.
+- **Autoscaling** uses queue depth from `/metrics` (requires
+  prometheus-adapter), not CPU, which says little about a GPU-bound service.
+- **Security:** non-root, read-only root filesystem (writable `/tmp` only for
+  compile caches), no privilege escalation, all capabilities dropped.
+
+## Validation
+
+`k8s/validate.sh` checks every manifest against the Kubernetes schemas with
+`kubeconform -strict` (no cluster needed), including the PyTorchJob CRD.
+CI runs it on every PR.
diff --git a/k8s/bench-job.yaml b/k8s/bench-job.yaml
index 5278dac..b69b930 100644
--- a/k8s/bench-job.yaml
+++ b/k8s/bench-job.yaml
@@ -21,7 +21,7 @@ spec:
       #   nvidia.com/gpu.product: NVIDIA-L4
       containers:
         - name: bench
-          image: ghcr.io/apayne185/qnnbench:latest
+          image: ghcr.io/apayne185/qnnbench:0.1.0  # immutable release tag; see k8s/README.md
           command: ["python", "-m", "qnnbench.bench", "all", "--out", "/results"]
           resources:
             requests: {cpu: "4", memory: 16Gi}
diff --git a/k8s/ddp-job.yaml b/k8s/ddp-job.yaml
index 3ab5ee1..5421d81 100644
--- a/k8s/ddp-job.yaml
+++ b/k8s/ddp-job.yaml
@@ -18,7 +18,7 @@ spec:
         - {key: nvidia.com/gpu, operator: Exists, effect: NoSchedule}
       containers:
         - name: train
-          image: ghcr.io/apayne185/qnnbench:latest
+          image: ghcr.io/apayne185/qnnbench:0.1.0  # immutable release tag; see k8s/README.md
           command:
             - torchrun
             - --standalone
diff --git a/k8s/kustomization.yaml b/k8s/kustomization.yaml
new file mode 100644
index 0000000..75c796d
--- /dev/null
+++ b/k8s/kustomization.yaml
@@ -0,0 +1,10 @@
+# Serving stack: kubectl apply -k k8s/
+# Jobs (bench-job, ddp-job, pytorchjob) are applied on demand with -f.
+apiVersion: kustomize.config.k8s.io/v1beta1
+kind: Kustomization
+resources:
+  - serve.yaml
+images:
+  # Deploy a specific build: kustomize edit set image ghcr.io/apayne185/qnnbench:sha-<commit>
+  - name: ghcr.io/apayne185/qnnbench
+    newTag: "0.1.0"
diff --git a/k8s/pytorchjob.yaml b/k8s/pytorchjob.yaml
new file mode 100644
index 0000000..524b2ec
--- /dev/null
+++ b/k8s/pytorchjob.yaml
@@ -0,0 +1,55 @@
+# Multi-node data-parallel training with the Kubeflow Training Operator.
+# The operator starts one master and N workers and sets torchrun's PET_*
+# variables (nnodes, node rank, master address) for the rendezvous; torchrun
+# then sets RANK / LOCAL_RANK / WORLD_SIZE per process, which is all
+# qnnbench.hybrid reads. Same code as the single-node torchrun job.
+# 2 nodes x 2 GPUs = 4 ranks, gradients all-reduced over NCCL.
+apiVersion: kubeflow.org/v1
+kind: PyTorchJob
+metadata:
+  name: qnnbench-hybrid-multinode
+spec:
+  nprocPerNode: "2"
+  pytorchReplicaSpecs:
+    Master:
+      replicas: 1
+      restartPolicy: OnFailure
+      template:
+        spec:
+          tolerations:
+            - {key: nvidia.com/gpu, operator: Exists, effect: NoSchedule}
+          containers:
+            - name: pytorch
+              image: ghcr.io/apayne185/qnnbench:0.1.0
+              command: [torchrun, -m, qnnbench.hybrid, --features=quanv, --epochs=5,
+                        --out=/results/hybrid-multinode.json]
+              env:
+                - {name: NCCL_DEBUG, value: WARN}
+              resources:
+                limits: {nvidia.com/gpu: 2, memory: 32Gi}
+              volumeMounts:
+                - {name: dshm, mountPath: /dev/shm}
+                - {name: results, mountPath: /results}
+          volumes:
+            - {name: dshm, emptyDir: {medium: Memory, sizeLimit: 2Gi}}
+            - name: results
+              persistentVolumeClaim: {claimName: qnnbench-results}
+    Worker:
+      replicas: 1
+      restartPolicy: OnFailure
+      template:
+        spec:
+          tolerations:
+            - {key: nvidia.com/gpu, operator: Exists, effect: NoSchedule}
+          containers:
+            - name: pytorch
+              image: ghcr.io/apayne185/qnnbench:0.1.0
+              command: [torchrun, -m, qnnbench.hybrid, --features=quanv, --epochs=5]
+              env:
+                - {name: NCCL_DEBUG, value: WARN}
+              resources:
+                limits: {nvidia.com/gpu: 2, memory: 32Gi}
+              volumeMounts:
+                - {name: dshm, mountPath: /dev/shm}
+          volumes:
+            - {name: dshm, emptyDir: {medium: Memory, sizeLimit: 2Gi}}
diff --git a/k8s/serve.yaml b/k8s/serve.yaml
index 0c1add0..10cbe40 100644
--- a/k8s/serve.yaml
+++ b/k8s/serve.yaml
@@ -5,41 +5,113 @@ metadata:
   name: qnnbench-serve
   labels: {app: qnnbench, component: serve}
 spec:
-  replicas: 1
+  replicas: 2   # with the PodDisruptionBudget, one stays up during node drains
   selector:
     matchLabels: {app: qnnbench, component: serve}
+  strategy:
+    rollingUpdate: {maxUnavailable: 0, maxSurge: 1}   # never drop below capacity on deploy
   template:
     metadata:
       labels: {app: qnnbench, component: serve}
+      annotations:
+        prometheus.io/scrape: "true"
+        prometheus.io/port: "8000"
+        prometheus.io/path: /metrics
     spec:
+      # SIGTERM -> app drains its queue (QNNBENCH_DRAIN_TIMEOUT_S); must exceed
+      # preStop + drain + uvicorn's graceful shutdown.
+      terminationGracePeriodSeconds: 30
       tolerations:
         - {key: nvidia.com/gpu, operator: Exists, effect: NoSchedule}
+      securityContext:
+        runAsNonRoot: true
+        runAsUser: 1000
+        seccompProfile: {type: RuntimeDefault}
       containers:
         - name: serve
-          image: ghcr.io/apayne185/qnnbench:latest
-          command: ["uvicorn", "qnnbench.serve:app", "--host", "0.0.0.0", "--port", "8000"]
+          image: ghcr.io/apayne185/qnnbench:0.1.0  # immutable release tag; see k8s/README.md
+          command:
+            - uvicorn
+            - qnnbench.serve:app
+            - --host=0.0.0.0
+            - --port=8000
+            # Refuse excess connections with 503 before parsing them: overload
+            # otherwise queues in the connection backlog, invisible to the app.
+            - --limit-concurrency=128
+            - --timeout-graceful-shutdown=15
           ports:
             - {containerPort: 8000, name: http}
           env:
             - {name: QNNBENCH_MAX_BATCH, value: "64"}
             - {name: QNNBENCH_MAX_WAIT_MS, value: "5"}
+            - {name: QNNBENCH_MAX_QUEUE, value: "256"}
+            - {name: QNNBENCH_REQUEST_TIMEOUT_MS, value: "2000"}
+            - {name: QNNBENCH_DRAIN_TIMEOUT_S, value: "10"}
+            - {name: QNNBENCH_LOG_FORMAT, value: json}
           resources:
             requests: {cpu: "2", memory: 4Gi}
             limits: {nvidia.com/gpu: 1, memory: 4Gi}
-          readinessProbe:
+          securityContext:
+            allowPrivilegeEscalation: false
+            readOnlyRootFilesystem: true
+            capabilities: {drop: [ALL]}
+          volumeMounts:
+            - {name: tmp, mountPath: /tmp}   # torch.compile / inductor caches
+          startupProbe:          # model load + CUDA init can take a while
             httpGet: {path: /healthz, port: http}
-            initialDelaySeconds: 5
+            periodSeconds: 2
+            failureThreshold: 60
+          readinessProbe:        # 503 while draining -> removed from the Service
+            httpGet: {path: /readyz, port: http}
             periodSeconds: 5
           livenessProbe:
             httpGet: {path: /healthz, port: http}
-            initialDelaySeconds: 30
             periodSeconds: 10
+          lifecycle:
+            # Give endpoints time to drop this pod before SIGTERM starts the drain,
+            # so no new requests arrive after it stops admitting work.
+            preStop:
+              sleep: {seconds: 5}
+      volumes:
+        - name: tmp
+          emptyDir: {}
 ---
 apiVersion: v1
 kind: Service
 metadata:
   name: qnnbench-serve
+  labels: {app: qnnbench, component: serve}
 spec:
   selector: {app: qnnbench, component: serve}
   ports:
     - {port: 80, targetPort: http, name: http}
+---
+apiVersion: policy/v1
+kind: PodDisruptionBudget
+metadata:
+  name: qnnbench-serve
+spec:
+  minAvailable: 1
+  selector:
+    matchLabels: {app: qnnbench, component: serve}
+---
+# Scale on queue depth, not CPU: this service is GPU-bound, so CPU utilisation
+# says little about load, while a growing batch queue is the direct signal that
+# requests are waiting. Needs prometheus-adapter exposing qnnbench_queue_depth
+# as a per-pod custom metric.
+apiVersion: autoscaling/v2
+kind: HorizontalPodAutoscaler
+metadata:
+  name: qnnbench-serve
+spec:
+  scaleTargetRef: {apiVersion: apps/v1, kind: Deployment, name: qnnbench-serve}
+  minReplicas: 2
+  maxReplicas: 8
+  metrics:
+    - type: Pods
+      pods:
+        metric: {name: qnnbench_queue_depth}
+        target: {type: AverageValue, averageValue: "32"}   # half of one full batch
+  behavior:
+    scaleDown:
+      stabilizationWindowSeconds: 300   # GPU pods are slow to start; don't flap
diff --git a/k8s/validate.sh b/k8s/validate.sh
new file mode 100755
index 0000000..c8bdac8
--- /dev/null
+++ b/k8s/validate.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+# Validate every manifest against the Kubernetes API schemas, offline from any cluster.
+#   KUBECONFORM=/path/to/kubeconform k8s/validate.sh
+# PyTorchJob is a Kubeflow CRD with no published JSON schema, so its schema is
+# extracted from the Training Operator's released CRD.
+set -euo pipefail
+cd "$(dirname "$0")"
+KUBECONFORM=${KUBECONFORM:-kubeconform}
+CRD_URL=https://raw.githubusercontent.com/kubeflow/training-operator/v1.8.1/manifests/base/crds/kubeflow.org_pytorchjobs.yaml
+
+schemas=$(mktemp -d)
+trap 'rm -rf "$schemas"' EXIT
+mkdir -p "$schemas/kubeflow.org"
+curl -fsSL "$CRD_URL" | python3 -c '
+import json, sys, yaml
+crd = yaml.safe_load(sys.stdin)
+for v in crd["spec"]["versions"]:
+    schema = v["schema"]["openAPIV3Schema"]
+    with open(sys.argv[1] + "/kubeflow.org/pytorchjob_" + v["name"] + ".json", "w") as f:
+        json.dump(schema, f)
+' "$schemas"
+
+"$KUBECONFORM" -strict -summary \
+  -schema-location default \
+  -schema-location "$schemas/{{.Group}}/{{.ResourceKind}}_{{.ResourceAPIVersion}}.json" \
+  bench-job.yaml ddp-job.yaml pytorchjob.yaml serve.yaml
+# The kustomized serving stack, as `kubectl apply -k` would send it.
+kubectl kustomize . | "$KUBECONFORM" -strict -summary -

From 9f1e1e351420947fb059bdda07db5947929fa7bd Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 14:07:36 +0200
Subject: [PATCH 08/13] Serve manifest: send compile caches to /tmp under a
 read-only root FS

---
 k8s/serve.yaml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/k8s/serve.yaml b/k8s/serve.yaml
index 10cbe40..31c1cd5 100644
--- a/k8s/serve.yaml
+++ b/k8s/serve.yaml
@@ -48,6 +48,10 @@ spec:
             - {name: QNNBENCH_REQUEST_TIMEOUT_MS, value: "2000"}
             - {name: QNNBENCH_DRAIN_TIMEOUT_S, value: "10"}
             - {name: QNNBENCH_LOG_FORMAT, value: json}
+            # Read-only root filesystem: point compile caches at the writable /tmp.
+            - {name: XDG_CACHE_HOME, value: /tmp/.cache}
+            - {name: TRITON_CACHE_DIR, value: /tmp/.triton}
+            - {name: TORCHINDUCTOR_CACHE_DIR, value: /tmp/torchinductor}
           resources:
             requests: {cpu: "2", memory: 4Gi}
             limits: {nvidia.com/gpu: 1, memory: 4Gi}

From ebc835fa495b729ddf0d0116c3a0561d8620e7c5 Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 14:12:29 +0200
Subject: [PATCH 09/13] Load run settings from TOML config files

--config takes a TOML file; precedence is dataclass defaults, then the
file, then explicit flags. Unknown keys are an error, so a typo can't
silently fall back to a default. Replaces the per-CLI flag loops with
one helper. configs/ holds the paper protocol for the QNN and MLP and
the hybrid runs, including a DDP + gradient accumulation example;
tests check every shipped config loads.
---
 configs/hybrid_ddp_accum.toml   |  7 ++++
 configs/hybrid_quanv.toml       |  7 ++++
 configs/paper_protocol_mlp.toml |  5 +++
 configs/paper_protocol_qnn.toml |  7 ++++
 pyproject.toml                  |  1 +
 src/qnnbench/config.py          | 59 +++++++++++++++++++++++++++++++++
 src/qnnbench/hybrid.py          | 16 +++------
 src/qnnbench/train.py           | 15 +++------
 tests/test_config.py            | 47 ++++++++++++++++++++++++++
 uv.lock                         |  2 ++
 10 files changed, 145 insertions(+), 21 deletions(-)
 create mode 100644 configs/hybrid_ddp_accum.toml
 create mode 100644 configs/hybrid_quanv.toml
 create mode 100644 configs/paper_protocol_mlp.toml
 create mode 100644 configs/paper_protocol_qnn.toml
 create mode 100644 src/qnnbench/config.py
 create mode 100644 tests/test_config.py

diff --git a/configs/hybrid_ddp_accum.toml b/configs/hybrid_ddp_accum.toml
new file mode 100644
index 0000000..815cdff
--- /dev/null
+++ b/configs/hybrid_ddp_accum.toml
@@ -0,0 +1,7 @@
+# Effective batch 512 from micro-batches of 64: 4 accumulation steps x 2 ranks.
+# torchrun --nproc-per-node 2 -m qnnbench.hybrid --config configs/hybrid_ddp_accum.toml
+features = "quanv"
+epochs = 5
+batch_size = 512
+accum_steps = 4
+lr = 4e-3
diff --git a/configs/hybrid_quanv.toml b/configs/hybrid_quanv.toml
new file mode 100644
index 0000000..d327328
--- /dev/null
+++ b/configs/hybrid_quanv.toml
@@ -0,0 +1,7 @@
+# Quanvolution features + CNN head on full 10-class MNIST.
+# Change `features` to "random" or "learned" for the classical controls.
+features = "quanv"
+epochs = 5
+batch_size = 128
+lr = 2e-3
+amp = "auto"
diff --git a/configs/paper_protocol_mlp.toml b/configs/paper_protocol_mlp.toml
new file mode 100644
index 0000000..966d104
--- /dev/null
+++ b/configs/paper_protocol_mlp.toml
@@ -0,0 +1,5 @@
+# The 37-parameter classical baseline under the same protocol as the QNN.
+model = "mlp"
+epochs = 3
+batch_size = 32
+lr = 1e-3
diff --git a/configs/paper_protocol_qnn.toml b/configs/paper_protocol_qnn.toml
new file mode 100644
index 0000000..dc39f7f
--- /dev/null
+++ b/configs/paper_protocol_qnn.toml
@@ -0,0 +1,7 @@
+# The paper's QNN protocol: 3 epochs, batch 32, Adam 1e-3, full training set.
+model = "qnn"
+epochs = 3
+batch_size = 32
+lr = 1e-3
+grad_method = "autograd"
+fuse = 5
diff --git a/pyproject.toml b/pyproject.toml
index fd31065..3edbdf1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,6 +12,7 @@ authors = [{ name = "Anna Payne" }]
 dependencies = [
     "torch>=2.3",
     "numpy>=1.24",
+    "tomli>=2; python_version < '3.11'",  # tomllib backport for config files
 ]
 
 [project.optional-dependencies]
diff --git a/src/qnnbench/config.py b/src/qnnbench/config.py
new file mode 100644
index 0000000..9d7b78f
--- /dev/null
+++ b/src/qnnbench/config.py
@@ -0,0 +1,59 @@
+"""Run configuration from TOML files, with command-line overrides.
+
+    python -m qnnbench.hybrid --config configs/hybrid_quanv.toml --epochs 2
+
+Precedence: dataclass defaults < config file < explicit command-line flags.
+Unknown keys in the file are an error, so a typo can't silently fall back to
+a default.
+"""
+
+from __future__ import annotations
+
+import argparse
+import dataclasses
+import sys
+from pathlib import Path
+from typing import Any
+
+if sys.version_info >= (3, 11):
+    import tomllib
+else:  # pragma: no cover
+    import tomli as tomllib
+
+
+def load_toml(path: Path | str, cls: type) -> dict[str, Any]:
+    """Read ``path`` and check every key is a field of the dataclass ``cls``."""
+    with open(path, "rb") as f:
+        values = tomllib.load(f)
+    known = {f.name for f in dataclasses.fields(cls)}
+    unknown = sorted(set(values) - known)
+    if unknown:
+        raise ValueError(f"{path}: unknown keys {unknown}; valid keys are {sorted(known)}")
+    return values
+
+
+def add_dataclass_args(parser: argparse.ArgumentParser, cls: type) -> None:
+    """One --flag per dataclass field, plus --config. Defaults are left unset
+    (None) so we can tell which flags were actually given."""
+    parser.add_argument("--config", type=Path, help="TOML file with any of the options below")
+    for f in dataclasses.fields(cls):
+        flag = f"--{f.name.replace('_', '-')}"
+        kind = str(f.type)
+        if kind == "bool":
+            parser.add_argument(flag, action=argparse.BooleanOptionalAction, default=None,
+                                help=f"default: {f.default}")  # fmt: skip
+        else:
+            caster = int if "int" in kind else float if "float" in kind else str
+            parser.add_argument(flag, type=caster, default=None, help=f"default: {f.default}")
+
+
+def build_config(cls: type, args: argparse.Namespace):
+    """Defaults, then the --config file, then flags that were given explicitly."""
+    values: dict[str, Any] = {}
+    if getattr(args, "config", None):
+        values.update(load_toml(args.config, cls))
+    for f in dataclasses.fields(cls):
+        given = getattr(args, f.name, None)
+        if given is not None:
+            values[f.name] = given
+    return cls(**values)
diff --git a/src/qnnbench/hybrid.py b/src/qnnbench/hybrid.py
index 162fe8a..1db3ec0 100644
--- a/src/qnnbench/hybrid.py
+++ b/src/qnnbench/hybrid.py
@@ -36,6 +36,7 @@
 from torch.utils.data import DataLoader, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
 
+from qnnbench.config import add_dataclass_args, build_config
 from qnnbench.data import load_mnist
 from qnnbench.env import env_info
 from qnnbench.quanv import QuanvConfig, cached_features
@@ -328,18 +329,11 @@ def latency_ms(m, batch):
 
 def main(argv=None):
     p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawTextHelpFormatter)
-    for f in HybridConfig.__dataclass_fields__.values():
-        flag = f"--{f.name.replace('_', '-')}"
-        if str(f.type) == "bool":
-            p.add_argument(flag, action=argparse.BooleanOptionalAction, default=f.default)
-        else:
-            p.add_argument(
-                flag, type={"int": int, "float": float}.get(str(f.type), str), default=f.default
-            )
+    add_dataclass_args(p, HybridConfig)
     p.add_argument("--out", type=Path, help="write the run as JSON (rank 0)")
-    args = vars(p.parse_args(argv))
-    out = args.pop("out")
-    result = train(HybridConfig(**args))
+    args = p.parse_args(argv)
+    result = train(build_config(HybridConfig, args))
+    out = args.out
     if out and int(os.environ.get("RANK", 0)) == 0:
         out.parent.mkdir(parents=True, exist_ok=True)
         out.write_text(json.dumps(result, indent=2) + "\n")
diff --git a/src/qnnbench/train.py b/src/qnnbench/train.py
index 332e6c7..4d94d3b 100644
--- a/src/qnnbench/train.py
+++ b/src/qnnbench/train.py
@@ -18,6 +18,7 @@
 import torch
 from torch import nn
 
+from qnnbench.config import add_dataclass_args, build_config
 from qnnbench.data import prepare_binary_mnist
 from qnnbench.env import env_info, resolve_device
 from qnnbench.models import QNN, FairMLP, LookupTable, n_params
@@ -164,18 +165,12 @@ def lookup_table_accuracy(data=None) -> dict:
 
 def main(argv=None):
     p = argparse.ArgumentParser(description="Train the QNN or the fair MLP baseline.")
-    for f in TrainConfig.__dataclass_fields__.values():
-        if str(f.type) == "bool":
-            p.add_argument(f"--{f.name.replace('_', '-')}", action="store_true")
-        else:
-            caster = {"int": int, "float": float, "int | None": int}.get(str(f.type), str)
-            p.add_argument(f"--{f.name.replace('_', '-')}", type=caster, default=f.default)
+    add_dataclass_args(p, TrainConfig)
     p.add_argument("--out", type=Path, help="write the run (config, history, env) as JSON")
     p.add_argument("--save", type=Path, help="save the trained weights (state_dict)")
-    args = vars(p.parse_args(argv))
-    out, save = args.pop("out"), args.pop("save")
-    cfg = TrainConfig(**args)
-    result = train(cfg, save_path=save)
+    args = p.parse_args(argv)
+    out = args.out
+    result = train(build_config(TrainConfig, args), save_path=args.save)
     if out:
         out.parent.mkdir(parents=True, exist_ok=True)
         out.write_text(json.dumps(asdict(result), indent=2) + "\n")
diff --git a/tests/test_config.py b/tests/test_config.py
new file mode 100644
index 0000000..84fbec5
--- /dev/null
+++ b/tests/test_config.py
@@ -0,0 +1,47 @@
+import argparse
+from pathlib import Path
+
+import pytest
+
+from qnnbench.config import add_dataclass_args, build_config, load_toml
+from qnnbench.hybrid import HybridConfig
+from qnnbench.train import TrainConfig
+
+CONFIGS = Path(__file__).parent.parent / "configs"
+
+
+def _parse(cls, argv):
+    p = argparse.ArgumentParser()
+    add_dataclass_args(p, cls)
+    return build_config(cls, p.parse_args(argv))
+
+
+def test_precedence_defaults_then_file_then_flags(tmp_path):
+    cfg_file = tmp_path / "run.toml"
+    cfg_file.write_text('model = "mlp"\nepochs = 7\nlr = 0.01\n')
+    cfg = _parse(TrainConfig, ["--config", str(cfg_file), "--epochs", "2"])
+    assert cfg.model == "mlp"  # from file
+    assert cfg.epochs == 2  # flag beats file
+    assert cfg.lr == 0.01  # from file
+    assert cfg.batch_size == TrainConfig().batch_size  # default
+
+
+def test_flags_without_file_and_bool_options():
+    cfg = _parse(HybridConfig, ["--features", "random", "--no-quantize", "--compile"])
+    assert (cfg.features, cfg.quantize, cfg.compile) == ("random", False, True)
+    assert _parse(TrainConfig, ["--num-train", "500"]).num_train == 500
+
+
+def test_unknown_keys_are_rejected(tmp_path):
+    bad = tmp_path / "bad.toml"
+    bad.write_text("epochz = 3\n")
+    with pytest.raises(ValueError, match="epochz"):
+        load_toml(bad, TrainConfig)
+
+
+@pytest.mark.parametrize("path", sorted(CONFIGS.glob("*.toml")), ids=lambda p: p.name)
+def test_shipped_configs_are_valid(path):
+    cls = HybridConfig if path.name.startswith("hybrid") else TrainConfig
+    cfg = cls(**load_toml(path, cls))
+    if isinstance(cfg, HybridConfig):
+        assert cfg.batch_size % cfg.accum_steps == 0
diff --git a/uv.lock b/uv.lock
index 180874e..a6b4b4d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2829,6 +2829,7 @@ dependencies = [
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
     { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.11.*'" },
     { name = "numpy", version = "2.5.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
     { name = "torch", version = "2.14.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
     { name = "torch", version = "2.14.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
@@ -2870,6 +2871,7 @@ requires-dist = [
     { name = "pytest", marker = "extra == 'dev'", specifier = ">=8" },
     { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=5" },
     { name = "ruff", marker = "extra == 'dev'", specifier = ">=0.5" },
+    { name = "tomli", marker = "python_full_version < '3.11'", specifier = ">=2" },
     { name = "torch", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = ">=2.3" },
     { name = "torch", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = ">=2.3", index = "https://download.pytorch.org/whl/cpu" },
     { name = "uvicorn", marker = "extra == 'serve'", specifier = ">=0.29" },

From e15540d6edb6a786073861275a217dbccc8acdfa Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 14:12:59 +0200
Subject: [PATCH 10/13] Add multi-seed hybrid experiment (quantum filter vs
 classical controls)

python -m qnnbench.experiments --suite hybrid runs the quantum, random
and learned 2x2 patch encoders over several seeds. The single-seed
results differed by a few tenths of a percent, too small to read
without seed variance.
---
 src/qnnbench/experiments.py | 59 ++++++++++++++++++++++++++++++++++++-
 tests/test_tools.py         |  9 ++++++
 2 files changed, 67 insertions(+), 1 deletion(-)

diff --git a/src/qnnbench/experiments.py b/src/qnnbench/experiments.py
index 062cfeb..eee57b6 100644
--- a/src/qnnbench/experiments.py
+++ b/src/qnnbench/experiments.py
@@ -1,10 +1,15 @@
 """Multi-seed accuracy comparison of the QNN, the fair MLP and the lookup-table ceiling.
 
     python -m qnnbench.experiments --seeds 5 --epochs 3
+    python -m qnnbench.experiments --suite hybrid --seeds 5 --epochs 5
 
-Reproduces the paper's protocol (3 epochs, batch 32, Adam 1e-3, the 500-example
+The default suite reproduces the paper's protocol (3 epochs, batch 32, Adam 1e-3, the 500-example
 "short" QNN) but with independent models, fixed seeds and mean +- std, and
 with the Bayes ceiling for the binarized 4x4 encoding reported alongside.
+
+The ``hybrid`` suite compares the quantum patch filter against its classical
+controls (random and learned 2x2 filters) on full MNIST over several seeds;
+a single seed cannot separate differences of a few tenths of a percent.
 """
 
 from __future__ import annotations
@@ -32,11 +37,14 @@ def _mean_std(values: list[float]) -> tuple[float, float]:
 
 def main(argv=None):
     p = argparse.ArgumentParser(description=__doc__)
+    p.add_argument("--suite", choices=["paper", "hybrid"], default="paper")
     p.add_argument("--seeds", type=int, default=5)
     p.add_argument("--epochs", type=int, default=3)
     p.add_argument("--device", default="auto")
     p.add_argument("--out", type=Path, default=Path("results/experiments"))
     args = p.parse_args(argv)
+    if args.suite == "hybrid":
+        return run_hybrid(args.seeds, args.epochs, args.out)
 
     data = prepare_binary_mnist()
     ceiling = lookup_table_accuracy(data)
@@ -92,5 +100,54 @@ def summarize(
     return summary, "\n".join(lines)
 
 
+HYBRID_FEATURES = {
+    "Quantum filter (quanvolution)": "quanv",
+    "Random classical 2x2 filter": "random",
+    "Learned 2x2 filter": "learned",
+}
+
+
+def run_hybrid(seeds: int, epochs: int, out: Path) -> dict:
+    from qnnbench.hybrid import HybridConfig
+    from qnnbench.hybrid import train as train_hybrid
+
+    runs: dict[str, list[dict]] = {}
+    for name, features in HYBRID_FEATURES.items():
+        runs[name] = [
+            train_hybrid(
+                HybridConfig(
+                    features=features, epochs=epochs, seed=seed, num_workers=0, quantize=False
+                )
+            )  # fmt: skip
+            for seed in range(seeds)
+        ]
+    summary, table = summarize_hybrid(runs, seeds, epochs)
+    print("\n" + table)
+    out.mkdir(parents=True, exist_ok=True)
+    (out / f"hybrid_{epochs}ep.json").write_text(
+        json.dumps({"summary": summary, "runs": runs}, indent=2) + "\n"
+    )
+    (out / f"hybrid_{epochs}ep.md").write_text(table + "\n")
+    return summary
+
+
+def summarize_hybrid(runs: dict[str, list[dict]], seeds: int, epochs: int) -> tuple[dict, str]:
+    summary = {}
+    lines = [
+        f"| Patch encoder | Test accuracy ({seeds} seeds, {epochs} epochs) | Range |",
+        "|---|---|---|",
+    ]
+    for name, results in runs.items():
+        accs = [r["history"][-1]["test_acc"] for r in results]
+        mean, std = _mean_std(accs)
+        summary[name] = {"test_acc_mean": mean, "test_acc_std": std,
+                         "test_acc_min": min(accs), "test_acc_max": max(accs)}  # fmt: skip
+        lines.append(
+            f"| {name} | {100 * mean:.2f}% ± {100 * std:.2f} | "
+            f"{100 * min(accs):.2f}–{100 * max(accs):.2f}% |"
+        )
+    return summary, "\n".join(lines)
+
+
 if __name__ == "__main__":
     main()
diff --git a/tests/test_tools.py b/tests/test_tools.py
index 1239523..624a777 100644
--- a/tests/test_tools.py
+++ b/tests/test_tools.py
@@ -93,3 +93,12 @@ def test_text_formatter_and_configure():
     with pytest.raises(ValueError):
         configure_logging("xml")
     configure_logging("text", "INFO")
+
+
+def test_summarize_hybrid_table():
+    from qnnbench.experiments import summarize_hybrid
+
+    runs = {"Quantum filter": [_run(0.988), _run(0.990)], "Random": [_run(0.989)]}
+    summary, table = summarize_hybrid(runs, seeds=2, epochs=5)
+    assert summary["Quantum filter"]["test_acc_mean"] == pytest.approx(0.989)
+    assert "| Quantum filter | 98.90% ± 0.14 | 98.80–99.00% |" in table

From 714126fe03d697f57e233e5f88c5dea5d66c1335 Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 14:13:28 +0200
Subject: [PATCH 11/13] README: document serving overload design,
 observability, profiling, configs and CI gates

---
 README.md | 64 +++++++++++++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 60 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index a826b23..a041ad9 100644
--- a/README.md
+++ b/README.md
@@ -50,7 +50,8 @@ environment metadata are in [`results/`](results).
   tails. Server metrics showed batches capped at about 8 and client p99 10× the
   server-side p99: the event loop, the load generator and torch's threads were
   fighting over 4 cores. Capping torch threads helped a little. This needs more
-  compute, not batch tuning.
+  compute, not batch tuning, and it led to the server's overload design: see
+  [Serving under overload](#serving-under-overload).
 - **int8 dynamic quantization of the hybrid model's Linear layers:** 2.4× smaller
   weights, same accuracy, but batch-1 CPU latency went *up* (0.24 → 0.35 ms).
   The model is small and conv-dominated, so quantizing activations on every call
@@ -97,21 +98,32 @@ types.
 | [`src/qnnbench/bench/`](src/qnnbench/bench) | Benchmark harness, 9 suites, regression checker, plots |
 | [`src/qnnbench/train.py`](src/qnnbench/train.py), [`experiments.py`](src/qnnbench/experiments.py) | QNN and baseline training; multi-seed accuracy comparison |
 | [`src/qnnbench/quanv.py`](src/qnnbench/quanv.py), [`hybrid.py`](src/qnnbench/hybrid.py) | Quanvolutional preprocessing and a hybrid CNN trainer (AMP, gradient accumulation, DDP, int8) |
-| [`src/qnnbench/serve.py`](src/qnnbench/serve.py), [`loadtest.py`](src/qnnbench/loadtest.py) | FastAPI inference with dynamic micro-batching; closed-loop load test |
+| [`src/qnnbench/serve.py`](src/qnnbench/serve.py), [`loadtest.py`](src/qnnbench/loadtest.py) | FastAPI inference: micro-batching, load shedding, deadlines, graceful drain, Prometheus metrics; load test |
+| [`src/qnnbench/profile.py`](src/qnnbench/profile.py) | `torch.profiler` traces of a training step with named circuit blocks (NVTX on CUDA) |
+| [`configs/`](configs), [`src/qnnbench/config.py`](src/qnnbench/config.py) | TOML run configs (paper protocol, hybrid, DDP + accumulation) |
 | [`tests/`](tests) | Simulator checked against Cirq; gradients against finite differences |
-| [`Dockerfile`](Dockerfile), [`k8s/`](k8s), [`.github/workflows/`](.github/workflows) | GPU image, benchmark/DDP/serving manifests, CI |
+| [`Dockerfile`](Dockerfile), [`k8s/`](k8s), [`.github/`](.github) | GPU image; serving, benchmark and DDP/multi-node manifests ([notes](k8s/README.md)); CI, image publishing, Dependabot |
 | [`legacy/tfq/`](legacy/tfq) | The original TensorFlow Quantum code, with the bugs fixed and documented |
 
 ## Quick start
 
 ```bash
-make install        # uv venv + CPU PyTorch; on a GPU box: make install TORCH_INDEX=
+make install        # locked deps (uv.lock), CPU PyTorch; on a GPU machine: make install-gpu
+make hooks          # pre-commit: ruff, mypy, lockfile and large-file checks on each commit
 make test           # under a minute on a laptop CPU
 make bench-quick    # every suite at small sizes
 make bench          # full suites -> results/<suite>/<device>.json
+make profile        # trace of one training step -> runs/profile/trace.json (Perfetto)
 python -m qnnbench.bench.plot   # -> docs/figures/
 ```
 
+Runs are configured with TOML files; flags override the file:
+
+```bash
+python -m qnnbench.train --config configs/paper_protocol_qnn.toml --seed 3
+torchrun --nproc-per-node 2 -m qnnbench.hybrid --config configs/hybrid_ddp_accum.toml
+```
+
 Run one suite on a GPU and compare two machines or commits:
 
 ```bash
@@ -119,6 +131,50 @@ python -m qnnbench.bench grad fusion --device cuda
 python -m qnnbench.bench.compare results/grad/old.json results/grad/new.json --tolerance 0.15
 ```
 
+## Engineering practices
+
+### Serving under overload
+
+Past saturation, every extra client only adds queueing delay, so an unbounded
+queue turns overload into unbounded latency at flat throughput. That's what the
+64-client CPU load test showed. The server now:
+
+- refuses excess connections at the HTTP layer (`uvicorn --limit-concurrency`),
+  where most of the waiting actually happened, before any parsing work;
+- bounds its batch queue and answers `503` + `Retry-After` immediately when it
+  is full;
+- gives each request a deadline and drops expired requests *before* inference
+  (`504`), so no GPU time goes to answers nobody is waiting for;
+- drains on shutdown: `/readyz` turns `503`, queued work finishes, then the
+  worker stops. Kubernetes pairs this with a `preStop` pause and a grace period
+  ([k8s notes](k8s/README.md)).
+
+### Observability
+
+JSON-lines logs (`QNNBENCH_LOG_FORMAT=json`) with structured fields; Prometheus
+histograms for request latency, batch size and per-batch inference time, plus
+outcome-labelled request counters (`ok`, `shed`, `timeout`, `invalid`, `error`)
+and a queue-depth gauge. The autoscaler scales on queue depth, not CPU.
+`python -m qnnbench.profile` produces a `torch.profiler` timeline with each gate
+and fused block as a named range (`--nvtx` for Nsight Systems on CUDA).
+
+### Quality gates (CI, on every PR)
+
+| Check | What it catches |
+|---|---|
+| ruff, `mypy --check-untyped-defs` | lint, formatting and type errors |
+| pytest on Python 3.10 and 3.12, coverage floor 70% | regressions; the simulator is checked against Cirq |
+| `uv sync --locked` | a lockfile out of date with `pyproject.toml` |
+| `pip-audit` on the locked set | dependencies with known vulnerabilities |
+| benchmark and profiler smoke runs | benchmark code that crashes (timings are gated on dedicated hardware with `qnnbench.bench.compare`, not on shared runners) |
+| `kubeconform -strict` | invalid Kubernetes manifests, including the Kubeflow CRD |
+| Docker build + Trivy | image build failures; image CVEs reported to the Security tab |
+
+**Supply chain:** dependencies are locked (`uv.lock`); GitHub Actions are
+pinned to commit SHAs; Dependabot opens weekly grouped updates, with PyTorch in
+its own PR so it can be benchmarked before merging. Images are published to GHCR
+as immutable `sha-<commit>` tags, and manifests pin a version, never `:latest`.
+
 ## Design notes
 
 **Gate kernels.** A batch of states is a `(B, 2**n)` complex tensor. A gate on

From 76e86adba162a67591eb40c8d356f26aa5dbcef3 Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 14:13:35 +0200
Subject: [PATCH 12/13] Makefile: add experiments-hybrid target

---
 Makefile | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/Makefile b/Makefile
index 05a2dce..3ec0368 100644
--- a/Makefile
+++ b/Makefile
@@ -3,7 +3,7 @@
 PY := .venv/bin/python
 EXTRAS := --extra dev --extra bench --extra serve
 
-.PHONY: install install-gpu hooks test coverage test-all lint format bench bench-quick profile experiments hybrid serve loadtest docker legacy
+.PHONY: install install-gpu hooks test coverage test-all lint format bench bench-quick profile experiments experiments-hybrid hybrid serve loadtest docker legacy
 
 install:
 	uv sync --locked $(EXTRAS)
@@ -45,7 +45,10 @@ profile:          ## torch.profiler trace of a training step -> runs/profile/tra
 experiments:      ## 5-seed accuracy comparison (paper protocol)
 	$(PY) -m qnnbench.experiments --seeds 5 --epochs 3
 
-hybrid:           ## quanvolution vs classical controls on full MNIST
+experiments-hybrid: ## quantum filter vs classical controls, 5 seeds
+	$(PY) -m qnnbench.experiments --suite hybrid --seeds 5 --epochs 5
+
+hybrid:           ## quanvolution vs classical controls on full MNIST, one seed
 	for f in quanv random learned; do \
 		$(PY) -m qnnbench.hybrid --features $$f --epochs 5 --out results/hybrid/$$f.json; \
 	done

From f68243133febe8074bb14b9a74fc204fe9ae2145 Mon Sep 17 00:00:00 2001
From: apayne185 <apayne2022@student.ie.edu>
Date: Sat, 3 Oct 2026 14:22:10 +0200
Subject: [PATCH 13/13] Add 5-seed hybrid results: no significant difference
 between patch encoders

Quantum filter 98.90% +- 0.19, random classical 98.69% +- 0.25,
learned 98.83% +- 0.23 (Welch p >= 0.17 for every pair). The earlier
single-seed run had the order reversed; both are inside seed noise.
---
 README.md                           |   19 +-
 results/experiments/hybrid_5ep.json | 1050 +++++++++++++++++++++++++++
 results/experiments/hybrid_5ep.md   |    5 +
 3 files changed, 1069 insertions(+), 5 deletions(-)
 create mode 100644 results/experiments/hybrid_5ep.json
 create mode 100644 results/experiments/hybrid_5ep.md

diff --git a/README.md b/README.md
index a041ad9..1aa3679 100644
--- a/README.md
+++ b/README.md
@@ -73,11 +73,20 @@ is the best any model can do on this input. The MLP reaches that ceiling; the QN
 doesn't, and it varies more across seeds. This reverses the paper's single-run
 headline (see [below](#original-research-and-what-changed)).
 
-**Quanvolutional preprocessing** (full 10-class MNIST, 5 epochs, same CNN head,
-single seed): quantum filter **98.80%**, random classical 2x2 filter **98.93%**,
-learned 2x2 filter **98.97%**. The random classical control is the important
-row: here, the quantum filter adds nothing over a random projection of the same
-shape.
+**Quanvolutional preprocessing** (full 10-class MNIST, 5 epochs, same CNN head;
+only the 2x2 patch encoder differs):
+
+| Patch encoder | Test accuracy (5 seeds) | Range |
+|---|---|---|
+| Quantum filter (quanvolution) | 98.90% ± 0.19 | 98.66–99.13% |
+| Random classical 2x2 filter | 98.69% ± 0.25 | 98.40–98.99% |
+| Learned 2x2 filter | 98.83% ± 0.23 | 98.47–99.00% |
+
+No difference is statistically significant (Welch's t-test: quantum vs random
+p = 0.17, quantum vs learned p = 0.61). The random classical filter is the
+control that matters: with it, the quantum filter adds nothing measurable. A
+single seed is misleading here. The first single-seed run put the random filter
+*ahead* of the quantum one, and five seeds put it behind, both inside the noise.
 
 ## Running on a GPU
 
diff --git a/results/experiments/hybrid_5ep.json b/results/experiments/hybrid_5ep.json
new file mode 100644
index 0000000..6240912
--- /dev/null
+++ b/results/experiments/hybrid_5ep.json
@@ -0,0 +1,1050 @@
+{
+  "summary": {
+    "Quantum filter (quanvolution)": {
+      "test_acc_mean": 0.988979983329773,
+      "test_acc_std": 0.0019070915711111682,
+      "test_acc_min": 0.9865999817848206,
+      "test_acc_max": 0.9912999868392944
+    },
+    "Random classical 2x2 filter": {
+      "test_acc_mean": 0.9868600010871887,
+      "test_acc_std": 0.0024885732017810706,
+      "test_acc_min": 0.984000027179718,
+      "test_acc_max": 0.9898999929428101
+    },
+    "Learned 2x2 filter": {
+      "test_acc_mean": 0.9882800102233886,
+      "test_acc_std": 0.0022785967006698554,
+      "test_acc_min": 0.9847000241279602,
+      "test_acc_max": 0.9900000095367432
+    }
+  },
+  "runs": {
+    "Quantum filter (quanvolution)": [
+      {
+        "config": {
+          "features": "quanv",
+          "epochs": 5,
+          "batch_size": 128,
+          "accum_steps": 1,
+          "lr": 0.002,
+          "train_size": 60000,
+          "amp": "auto",
+          "compile": false,
+          "num_workers": 0,
+          "seed": 0,
+          "quantize": false
+        },
+        "world_size": 1,
+        "amp_dtype": "None",
+        "history": [
+          {
+            "epoch": 1,
+            "train_loss": 0.21920724213123322,
+            "test_acc": 0.9847999811172485,
+            "epoch_time_s": 4.581357294000043,
+            "samples_per_s": 13075.600996772953,
+            "peak_mem_MB": null
+          },
+          {
+            "epoch": 2,
+            "train_loss": 0.05642542615532875,
+            "test_acc": 0.9847000241279602,
+            "epoch_time_s": 4.308452268999986,
+            "samples_per_s": 13903.83280581266,
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+            "test_acc": 0.9883000254631042,
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+            "peak_mem_MB": null
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+        "env": {
+          "timestamp": "2026-10-03T12:13:35+00:00",
+          "git_sha": "76e86ad",
+          "python": "3.12.13",
+          "torch": "2.14.1+cpu",
+          "device": "cpu",
+          "device_name": "Intel(R) Core(TM) i7-1065G7 CPU @ 1.30GHz",
+          "cpu_threads": 4
+        }
+      },
+      {
+        "config": {
+          "features": "quanv",
+          "epochs": 5,
+          "batch_size": 128,
+          "accum_steps": 1,
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+          "git_sha": "76e86ad",
+          "python": "3.12.13",
+          "torch": "2.14.1+cpu",
+          "device": "cpu",
+          "device_name": "Intel(R) Core(TM) i7-1065G7 CPU @ 1.30GHz",
+          "cpu_threads": 4
+        }
+      }
+    ],
+    "Random classical 2x2 filter": [
+      {
+        "config": {
+          "features": "random",
+          "epochs": 5,
+          "batch_size": 128,
+          "accum_steps": 1,
+          "lr": 0.002,
+          "train_size": 60000,
+          "amp": "auto",
+          "compile": false,
+          "num_workers": 0,
+          "seed": 0,
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+            "train_loss": 0.034313637763261795,
+            "test_acc": 0.9890999794006348,
+            "epoch_time_s": 7.273086777000003,
+            "samples_per_s": 8236.392859966556,
+            "peak_mem_MB": null
+          },
+          {
+            "epoch": 5,
+            "train_loss": 0.026602372527122498,
+            "test_acc": 0.9900000095367432,
+            "epoch_time_s": 8.577676396000015,
+            "samples_per_s": 6983.709484299819,
+            "peak_mem_MB": null
+          }
+        ],
+        "env": {
+          "timestamp": "2026-10-03T12:21:47+00:00",
+          "git_sha": "76e86ad",
+          "python": "3.12.13",
+          "torch": "2.14.1+cpu",
+          "device": "cpu",
+          "device_name": "Intel(R) Core(TM) i7-1065G7 CPU @ 1.30GHz",
+          "cpu_threads": 4
+        }
+      }
+    ]
+  }
+}
diff --git a/results/experiments/hybrid_5ep.md b/results/experiments/hybrid_5ep.md
new file mode 100644
index 0000000..3f3c666
--- /dev/null
+++ b/results/experiments/hybrid_5ep.md
@@ -0,0 +1,5 @@
+| Patch encoder | Test accuracy (5 seeds, 5 epochs) | Range |
+|---|---|---|
+| Quantum filter (quanvolution) | 98.90% ± 0.19 | 98.66–99.13% |
+| Random classical 2x2 filter | 98.69% ± 0.25 | 98.40–98.99% |
+| Learned 2x2 filter | 98.83% ± 0.23 | 98.47–99.00% |