Files
rUv 17471e93ff ADR-152: WiFi-Pose SOTA 2026 intake — WiFlow-STD benchmark, Rust integrations, ADR-153 802.11bf layer, efficiency frontier (#1008)
* feat(calibration): NodeGeometry transceiver-geometry recording (ADR-152 §2.1.1)

PerceptAlign-motivated geometry capture at enrollment: per-node optional
records (position, antenna orientation, inter-node distances, acquisition
method) — recorded when known, never required. Event-sourced via
EnrollmentEvent::GeometryRecorded (latest recording wins); persisted on
SpecialistBank with serde defaults so pre-ADR-152 bank JSON loads cleanly
(fixture-proven, and geometry-free banks serialize byte-shape-identical
to the old schema); threaded through MultiNodeMixture as data only — the
learned geometry embeddings and algorithmic fusion use are §2.1.2,
deliberately deferred until the ADR-151 P6 LoRA heads exist.

Geometry recorded from now on means banks captured today remain usable
for layout-conditioned training later — you can't retroactively add
geometry to data you didn't record.

8 new tests (3 geometry, 2 anchor, 2 bank, 1 multistatic) + full-loop
extension (2-node geometry, one tape-measured + one unknown, surviving
the bank JSON round-trip the runtime loads from). 50/50 calibration
(both feature configs) + 23 CLI tests green.

Co-Authored-By: RuFlo <ruv@ruv.net>

* feat(training): two-checkerboard camera↔room calibration for ADR-079 labels (ADR-152 §2.1.3)

Defends the camera-supervised pipeline against PerceptAlign's
"coordinate overfitting": MediaPipe keypoints were emitted in raw camera
coordinates with no shared frame and no transceiver-geometry metadata —
the exact label shape that memorizes deployment layout and collapses
cross-layout.

- scripts/calibrate-camera-room.py + calibration_lib.py: OpenCV
  two-checkerboard calibration → versioned bundle JSON (intrinsics,
  camera→room extrinsics, checkerboard spec, transceiver geometry,
  sha256 calibration_id). Intrinsics resolve from file > cache >
  multi-view computation > loud-warning 2-view fallback.
- collect-ground-truth.py --calibration <bundle>: every sample gains
  keypoints_room (unit bearing rays from the camera center in the room
  frame — documented projective alignment; raw image coords preserved
  so training chooses), camera_origin_room, calibration_id, and the
  transceiver geometry stamp. Without the flag, output is byte-identical
  to before (tested) + a one-line ADR-152 warning.

Design finding (recorded for ADR-152): a single planar checkerboard's
corner grid is centrosymmetric — the reversed corner ordering fits a
ghost camera pose with IDENTICAL reprojection error, so per-board flip
disambiguation is mathematically ill-posed. solve_two_board_extrinsics
solves the joint wall+floor set over all 4 flip combinations, where the
minimum is unique — an independent reason the TWO-checkerboard method is
required, beyond what PerceptAlign states.

15 headless pytest tests green (synthetic corners: extrinsics recovery
incl. ghost resolution, bundle round-trip + hash stability, ray
transforms w/ distortion + cross-resolution, no-calibration byte
identity).

Co-Authored-By: RuFlo <ruv@ruv.net>

* feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2)

Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization);
6 reproducibility defects documented (broken imports, corrupted dataset
tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable
test phase). After repairs, retraining with upstream defaults reproduces
96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on
RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs
verified. Third-party code/weights/data stay out of tree (gitignored).

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat: ADR-152 Rust integrations + ADR-153 802.11bf protocol model

- calibration: GeometryEmbedding — 32-slot permutation-invariant NodeGeometry
  featurization for future LoRA-head conditioning (ADR-152 §2.1.2); derived
  SpecialistBank::geometry_embedding() accessor; 59 tests
- train: MaePretrainConfig + patchify/random-mask with UNSW measured recipe
  (80% masking, (30,3) patches; ADR-152 §2.3, arXiv 2511.18792); strict
  no-truncate/no-NaN policy; proptest properties
- train: WiFlowStdModel — tch-gated port of the verified ~96%-PCK@20
  WiFlow-STD architecture (ADR-152 §2.2 beyond-SOTA); ungated param formula
  pinned to 2,225,042; 15/17-keypoint support; 239 crate tests
- hardware: ieee80211bf forward-compatibility protocol model (ADR-153):
  SpecProfile gates, SensingCapabilities negotiation, required ConsentMode,
  session FSM, SensingTransport + SimTransport + OpportunisticCsiBridge;
  full acceptance checklist covered; 156+4 tests
- deps: ruvector bumps per ADR-152 §2.6 survey (mincut/solver 2.0.6,
  attention 2.1.0, gnn 2.2.0); vendor/ruvector synced to a083bd77f
- docs: ADR-153 accepted; ADR-152 §2.2 status, §2.4 amendment, §2.6 added

Workspace: 162 test suites green (--no-default-features); Python proof PASS.
Known pre-existing flake: homecore-api env_empty_falls_back_to_defaults
(unserialized env-var mutation) — untouched, follow-up.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: CHANGELOG + CLAUDE.md entries for ADR-152 integrations and ADR-153

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix(train): repair tch-backend bit-rot — gated path compiles and tests run again

Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from
(+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() /
divide_scalar_mode(floor) — the latter fixed a silent true-division bug in
heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar
overflow panic); petgraph EdgeRef import; missing EvalMetrics and
verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test
uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11
Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged.
Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof
seed race under parallel threads — documented, deliberate follow-ups.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(train): WiFlow-STD PyTorch->tch weight import + numerical parity proof

export_to_safetensors.py maps the retrained checkpoint (295 tensors -> 248
mapped, param sum exactly 2,225,042; num_batches_tracked dropped) into a
tch-loadable safetensors plus a deterministic parity fixture. Gated #[ignore]
integration test loads it strictly and asserts forward-pass agreement:
max abs diff 1.192e-7 on the seed-42 fixture. dump_variable_names test makes
the tch name layout authoritative. Zero architecture discrepancies found.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: workflow-review findings — BN gamma init, ThresholdParams serde, init docs

Concurrent validation workflow (2 review lanes + adversarial verification,
13 agents): 5 confirmed findings, 3 refuted. Fixes:
- wiflow_std: pin BatchNorm gamma to 1.0 (tch default draws Uniform(0,1) —
  silently halves activations in from-scratch training; loaded checkpoints
  unaffected, parity re-verified after the change)
- wiflow_std: document the conv-init divergences vs the reference's
  effective kaiming_normal(fan_out) re-init (from-scratch dynamics only)
- ieee80211bf: ThresholdParams deserialization validates via try_from so
  the <=100 invariant holds for untrusted payloads (+ rejection test)

Benchmarks (release, ruvzen): GeometryEmbedding 1.84us/call (542k/s),
MAE tokenization 7.38us/window (135k/s), 802.11bf FSM 8.9M events/s —
nothing suspicious.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs(adr): ADR-152 §2.1.4 gate resolved — PerceptAlign repo MIT, dataset on HF

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction

Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU
latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT
dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via
conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch
dynamic quant converts 0% of this conv-only model; fp16 halves storage free
but is slower on CPU.

Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist
(stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS
the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not
torso) threshold, 69 near-static frames — mean predictor scores 100% under
that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations
removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule
extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired
session + torso-PCK re-baseline.

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs(user-guide): corrected camera-supervised collection tutorial

Step 0 CSI-rate check + session-length math (window yield = frames/20 —
the May session's 8x under-delivery was a ~12 Hz CSI rate, not an aligner
bug); two-checkerboard calibration step (ADR-152 §2.1.3); pose-variety and
confidence guidance; torso-normalized PCK + temporal-split + pred-variance
eval protocol (lessons from the 92.9% retraction); scale presets re-keyed
to realistic window counts.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(benchmarks): static PTQ int8 (calibrated) results + overnight capture script

Conv-only static QDQ beats dynamic int8 on accuracy (PCK@20 96.61-96.63%
vs 96.52%, MPJPE +10% vs +18% over fp32) at ~equal size/latency; all-ops
QDQ strictly worse (int8 activations through attention glue). Entropy
calibration verified bit-identical to MinMax on this data. Deployment:
ONNX fp32 for speed (3.2ms), static conv-only QDQ for smallest (2.53MB).

Also: scripts/overnight-empty-capture.py — segmented UDP CSI recorder for
empty-room baselines (no glob collisions, detach-safe).

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(benchmarks): measurement (b) MEASURED — optimization transfer only, mean-pose baseline wins

WiFlow-STD fine-tuned on 2,046 fresh single-room ESP32 paired windows
(temporal 70/15/15, 70->540 adapter, K=17): pretrained-init 65% PCK@20 vs
scratch 0% (optimization transfer) but frozen-trunk ~0% (no feature
transfer), and NOTHING beats the mean-pose baseline (95.9% PCK@20 —
single subject, near-static normalized coords). Honesty gates held: pred
std 0.0113 (non-constant model) but mean-baseline dominance means no
citable CSI->pose capability from this data. ADR-152 open question 1
answered partially; definitive answer needs multi-subject/position data.

Two new aligner findings: heterogeneous csi_shape with silent zero-padding
(~20%), and extractCsiMatrix's transposed shape label (frame-major data,
[nSc, nFrames] label) — fixes pending.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(benchmarks): efficiency sweep MEASURED — half model dominates full reference

Compact WiFlow-STD variants on the same data/split/protocol: half (843,834
params, 0.38x) strictly dominates the 2.23M reference (PCK@20 96.62 vs
96.61, PCK@50 99.47 vs 99.11, MPJPE 0.00898 vs 0.0094) — the published
architecture is over-parameterized for its own benchmark. quarter (338k)
96.05%; tiny (56,290 params, 1/39.5) holds 94.11% — a ~220KB fp32 edge
candidate. In-domain caveats recorded; cross-domain untested.

Co-Authored-By: claude-flow <ruv@ruv.net>

* feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152)

WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep
exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived
stride schedule and decoder-mid (all default to upstream behavior; legacy
serde JSON unaffected). Param formulas pin to trained ground truth first
try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity
unchanged. 248 tests green.

Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win
(~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity
1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE,
-16% size, slower) — recorded as negative result. Export note: width-16
breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact
mean+matmul equivalent, proven by parity.

Co-Authored-By: claude-flow <ruv@ruv.net>

* fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified)

wiflow_std: min_feature_width (default 15) replaces the keypoints->stride
coupling — for_keypoints(17) now provably builds the trained [2,2,2,2]
graph and pools 15->17, matching the validated Python protocol (pinned by
tests); param_count() total on invalid configs; random_mask returns Result
and rejects non-finite/out-of-range ratios; trainer checkpoints switched
to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11).

ieee80211bf: SBP proxy now re-triggers instances and relays reports via
Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via
their existing path); missed_instances reset on success = consecutive
semantics; SessionTable gains a guarded SBP entry point + unknown-id drop
counter; initiator-role sessions reject inbound setup/SBP requests
(RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp
outside Idle return InvalidStateForCommand; SBP validation unified
through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping.
events.rs split out to honor the 500-line cap.

calibration/cli: enrollment geometry now actually reaches trained banks —
both production call sites attach .with_geometry; --geometry flag on
train-room and POST /enroll/geometry + train-body geometry on
calibrate-serve give production a recording surface; geometry-free banks
log the ADR-152 §2.1.2 note.

benchmarks: corruption masks committed as ground truth (unregenerable
after in-place cleaning; verified bit-identical regeneration from the
pristine copy) + generate_corruption_masks.py producer; _bench_common.py
dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20
re-verified equal to the last digit); remote scripts get the mmap patch;
tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer
executes (and overwrote) the export — artifact restored byte-exact.

Workspace: 2,963 tests passed, 0 failed; Python proof PASS.

Co-Authored-By: claude-flow <ruv@ruv.net>

* ci: build workspace tests without debuginfo — runner disk exhaustion

The combined 38-crate debug target exceeds the GitHub runner's disk
('final link failed: No space left on device'); the same tree measured
151GB locally with full debuginfo. CARGO_PROFILE_{DEV,TEST}_DEBUG=0
shrinks the target ~5-10x; debuginfo serves no purpose in CI test runs.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-06-11 17:02:23 -04:00

487 lines
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YAML

name: Continuous Integration
on:
push:
branches: [ main, develop, 'feature/*', 'feat/*', 'hotfix/*' ]
pull_request:
branches: [ main, develop ]
workflow_dispatch:
env:
PYTHON_VERSION: '3.11'
NODE_VERSION: '18'
REGISTRY: ghcr.io
IMAGE_NAME: ${{ github.repository }}
jobs:
# Code Quality and Security Checks
# The Python codebase moved to `archive/v1/` when the runtime was rewritten in
# Rust under `v2/`. The lint/format/type/scan checks below still run against
# the archive for hygiene, but with `continue-on-error: true` everywhere — the
# archive is frozen reference code, not active development, so a stale lint
# rule shouldn't gate PRs to the Rust workspace.
code-quality:
name: Code Quality & Security
runs-on: ubuntu-latest
continue-on-error: true
steps:
- name: Checkout code
continue-on-error: true
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
continue-on-error: true
uses: actions/setup-python@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
cache: 'pip'
- name: Install dependencies
continue-on-error: true
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install black flake8 mypy bandit safety
- name: Code formatting check (Black)
continue-on-error: true
run: black --check --diff archive/v1/src archive/v1/tests
- name: Linting (Flake8)
continue-on-error: true
run: flake8 archive/v1/src archive/v1/tests --max-line-length=88 --extend-ignore=E203,W503
- name: Type checking (MyPy)
continue-on-error: true
run: mypy archive/v1/src --ignore-missing-imports
- name: Security scan (Bandit)
run: bandit -r archive/v1/src -f json -o bandit-report.json
continue-on-error: true
- name: Dependency vulnerability scan (Safety)
run: safety check --json --output safety-report.json
continue-on-error: true
- name: Upload security reports
continue-on-error: true
uses: actions/upload-artifact@v4
if: always()
with:
name: security-reports
path: |
bandit-report.json
safety-report.json
# Rust Workspace Tests
rust-tests:
name: Rust Workspace Tests
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
# `wifi-densepose-desktop` is a Tauri v2 app — `glib-sys`, `gtk-sys`,
# `webkit2gtk-sys`, etc. need the Linux dev libraries via pkg-config or the
# workspace test fails at the build step before any test runs (every recent
# main CI run has been red on this for exactly this reason). Install the
# standard Tauri-on-Ubuntu set.
- name: Install Tauri / GTK / serial system dev libraries
run: |
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
libglib2.0-dev \
libgtk-3-dev \
libsoup-3.0-dev \
libjavascriptcoregtk-4.1-dev \
libwebkit2gtk-4.1-dev \
libayatana-appindicator3-dev \
librsvg2-dev \
libxdo-dev \
libudev-dev \
libdbus-1-dev \
libssl-dev \
pkg-config
- name: Install Rust toolchain
uses: dtolnay/rust-toolchain@stable
# Swatinem/rust-cache replaces a naive `actions/cache` of the whole
# `v2/target`. That manual cache of a 38-crate target dir (multi-GB) was an
# intermittent failure source — several CI runs this cycle died at the
# cache/setup step (after toolchain install, before "Run Rust tests"),
# needing a rerun. rust-cache is purpose-built for Rust: it caches the
# registry + git + a pruned target, evicts stale deps, and restores far more
# reliably (and faster) on large workspaces. `workspaces: v2` points it at
# the v2/ cargo workspace (keys on v2/Cargo.lock, caches v2/target).
- name: Cache cargo (Swatinem/rust-cache)
uses: Swatinem/rust-cache@v2
with:
workspaces: v2
# The 38-crate workspace debug build exhausts the runner's disk when built
# with full debuginfo (observed: "final link failed: No space left on
# device" once the engine/benchmark crates landed; the same tree's local
# debug target measured 151 GB). Debuginfo is useless in CI — tests either
# pass or print their failure — so build without it; target shrinks ~5-10x.
- name: Run Rust tests
working-directory: v2
env:
CARGO_PROFILE_DEV_DEBUG: "0"
CARGO_PROFILE_TEST_DEBUG: "0"
run: cargo test --workspace --no-default-features
- name: Run ADR-147 worldmodel tests
working-directory: v2
env:
CARGO_PROFILE_DEV_DEBUG: "0"
CARGO_PROFILE_TEST_DEBUG: "0"
run: cargo test -p wifi-densepose-worldmodel --no-default-features
# ADR-134 CIR tests are behind the `cir` feature so the bench dependency
# (Criterion) only pulls when actually exercised. Run them as a separate
# step so a CIR-only regression is unambiguously attributable.
- name: Run ADR-134 CIR tests
working-directory: v2
run: cargo test -p wifi-densepose-signal --no-default-features --features cir --tests
# ADR-134 + ADR-028 witness guard. The CIR proof runner produces a
# bit-deterministic SHA-256 over CirEstimator output on the synthetic
# reference signal. Any algorithmic regression — changes to ISTA
# convergence, sensing matrix construction, soft-thresholding, or input
# padding — breaks the hash and fails the build. To regenerate after an
# *intentional* change:
# cd v2 && cargo run -p wifi-densepose-signal --bin cir_proof_runner \
# --release --no-default-features -- --generate-hash \
# > ../archive/v1/data/proof/expected_cir_features.sha256
- name: ADR-134 CIR witness proof (determinism guard)
run: bash scripts/verify-cir-proof.sh
- name: ADR-135 calibration witness proof (determinism guard)
run: bash scripts/verify-calibration-proof.sh
# Unit and Integration Tests
# Python pytest matrix — runs against the archived v1 Python tree.
# `continue-on-error: true` for the same reason as code-quality above:
# the archive is frozen reference, not blocking the Rust workspace PRs.
test:
name: Tests
runs-on: ubuntu-latest
continue-on-error: true
strategy:
fail-fast: false
matrix:
python-version: ['3.10', '3.11', '3.12']
services:
postgres:
image: postgres:15
env:
POSTGRES_PASSWORD: postgres
POSTGRES_DB: test_wifi_densepose
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
ports:
- 5432:5432
redis:
image: redis:7
options: >-
--health-cmd "redis-cli ping"
--health-interval 10s
--health-timeout 5s
--health-retries 5
ports:
- 6379:6379
steps:
- name: Checkout code
continue-on-error: true
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
continue-on-error: true
uses: actions/setup-python@v6
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
- name: Install dependencies
continue-on-error: true
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install pytest-cov pytest-xdist
- name: Run unit tests
continue-on-error: true
env:
DATABASE_URL: postgresql://postgres:postgres@localhost:5432/test_wifi_densepose
REDIS_URL: redis://localhost:6379/0
ENVIRONMENT: test
run: |
pytest archive/v1/tests/unit/ -v --cov=archive/v1/src --cov-report=xml --cov-report=html --junitxml=junit.xml
- name: Run integration tests
continue-on-error: true
env:
DATABASE_URL: postgresql://postgres:postgres@localhost:5432/test_wifi_densepose
REDIS_URL: redis://localhost:6379/0
ENVIRONMENT: test
run: |
pytest archive/v1/tests/integration/ -v --junitxml=integration-junit.xml
- name: Upload coverage reports
continue-on-error: true
uses: codecov/codecov-action@v6
with:
file: ./coverage.xml
flags: unittests
name: codecov-umbrella
- name: Upload test results
continue-on-error: true
uses: actions/upload-artifact@v4
if: always()
with:
name: test-results-${{ matrix.python-version }}
path: |
junit.xml
integration-junit.xml
htmlcov/
# Performance and Load Tests
# NOTE: tests/performance/locustfile.py and the src.api.main app path both
# predate the v1→archive/v1 reorganisation. continue-on-error: true until a
# proper locust suite is added under archive/v1/tests/performance/.
performance-test:
name: Performance Tests
runs-on: ubuntu-latest
needs: [test]
continue-on-error: true
if: github.event_name == 'push' && github.ref == 'refs/heads/main'
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
cache: 'pip'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install pytest # the perf suite is pytest, not locust
# No "Start application" step: the gated test (test_frame_budget.py) drives
# the CSIProcessor pipeline in-process and makes no HTTP calls, so the old
# uvicorn server + `sleep 10` were dead weight — they only existed for the
# now-excluded api_throughput/inference_speed tests, and on every run dumped
# ~50 misleading "router requires hardware setup" ERROR lines for a server
# no test touched. MOCK_POSE_DATA is server-only and unused here.
- name: Run performance tests
working-directory: archive/v1
run: |
# Gate only on the genuine, deterministic perf guard:
# test_frame_budget.py times the *real* CSIProcessor pipeline against
# the ADR 50 ms per-frame budget (single-frame, p95 over 100 frames,
# +Doppler) — a true regression signal.
#
# test_api_throughput.py / test_inference_speed.py are excluded: every
# test there is a TDD red-phase stub (suffix `_should_fail_initially`)
# that times a *mock that sleeps* — meaningless as a perf signal, with
# machine-dependent wall-clock asserts (e.g. `actual_rps >= 40`,
# `batch_time < individual_time`) that are inherently flaky on shared
# CI runners, plus a cross-class fixture-scope bug. Forcing them green
# would be manufacturing a false signal; they stay in-repo for local
# TDD but do not gate CI until the underlying features are implemented.
#
# `python -m pytest` (not the bare `pytest` script) puts the cwd
# (archive/v1) on sys.path so `from src.core...` resolves — the bare
# script omits cwd and raises ModuleNotFoundError: No module named 'src'.
# -o addopts="" drops the root pyproject's --cov/--cov-fail-under=100.
python -m pytest tests/performance/test_frame_budget.py \
-o addopts="" -v --junitxml=perf-junit.xml
- name: Upload performance results
if: always()
uses: actions/upload-artifact@v4
with:
name: performance-results
path: archive/v1/perf-junit.xml
# Docker Build and Test
# NOTE: the canonical Docker build for the sensing-server is now
# `.github/workflows/sensing-server-docker.yml` (multi-registry push, asset
# smoke tests, bearer-auth smoke tests — #520/#514/#443). This job predates
# that workflow, points at a non-existent root `Dockerfile` with a
# non-existent `target: production`, and pushes to a mis-cased image name —
# `continue-on-error: true` until it's deleted or rewired to call the new
# workflow, so it doesn't gate the rest of the pipeline.
docker-build:
name: Docker Build & Test
runs-on: ubuntu-latest
needs: [code-quality, test, rust-tests]
continue-on-error: true
steps:
- name: Checkout code
continue-on-error: true
uses: actions/checkout@v4
- name: Set up Docker Buildx
continue-on-error: true
uses: docker/setup-buildx-action@v3
- name: Log in to Container Registry
continue-on-error: true
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata
continue-on-error: true
id: meta
uses: docker/metadata-action@v6
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=branch
type=ref,event=pr
type=sha,prefix={{branch}}-
type=raw,value=latest,enable={{is_default_branch}}
- name: Build and push Docker image
continue-on-error: true
uses: docker/build-push-action@v7
with:
context: .
target: production
push: true
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
cache-from: type=gha
cache-to: type=gha,mode=max
platforms: linux/amd64,linux/arm64
- name: Test Docker image
continue-on-error: true
run: |
docker run --rm -d --name test-container -p 8000:8000 ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }}
sleep 10
curl -f http://localhost:8000/health || exit 1
docker stop test-container
- name: Run container security scan
continue-on-error: true
uses: aquasecurity/trivy-action@ed142fd0673e97e23eac54620cfb913e5ce36c25 # v0.36.0
with:
image-ref: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }}
format: 'sarif'
output: 'trivy-results.sarif'
- name: Upload Trivy scan results
continue-on-error: true
uses: github/codeql-action/upload-sarif@v3
if: always()
with:
sarif_file: 'trivy-results.sarif'
# API Documentation
docs:
name: API Documentation
runs-on: ubuntu-latest
needs: [docker-build]
if: github.ref == 'refs/heads/main'
permissions:
contents: write # gh-pages deploy needs write (GITHUB_TOKEN is read-only by default -> 403)
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v6
with:
python-version: ${{ env.PYTHON_VERSION }}
cache: 'pip'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Generate OpenAPI spec
working-directory: archive/v1
env:
MOCK_POSE_DATA: "true" # no CSI hardware in CI
run: |
python -c "
from src.api.main import app
import json
with open('openapi.json', 'w') as f:
json.dump(app.openapi(), f, indent=2)
"
- name: Deploy to GitHub Pages
uses: peaceiris/actions-gh-pages@v4
continue-on-error: true # openapi generation above is the real validation; deploy is best-effort (Pages may be disabled)
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
publish_dir: ./docs
destination_dir: api-docs
# Notification
notify:
name: Notify
runs-on: ubuntu-latest
needs: [code-quality, test, rust-tests, performance-test, docker-build, docs]
if: always()
permissions:
contents: write # required by softprops/action-gh-release
# GitHub Actions does not allow `secrets.X` directly in step-level `if:`
# expressions — only `env.X`. Promote the secret to env at job scope so
# the gating expression below is parseable.
env:
SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}
steps:
- name: Notify Slack on success
if: ${{ env.SLACK_WEBHOOK_URL != '' && needs.code-quality.result == 'success' && needs.test.result == 'success' && needs.docker-build.result == 'success' }}
uses: 8398a7/action-slack@v3
with:
status: success
channel: '#ci-cd'
text: '✅ CI pipeline completed successfully for ${{ github.ref }}'
- name: Notify Slack on failure
if: ${{ env.SLACK_WEBHOOK_URL != '' && (needs.code-quality.result == 'failure' || needs.test.result == 'failure' || needs.docker-build.result == 'failure') }}
uses: 8398a7/action-slack@v3
with:
status: failure
channel: '#ci-cd'
text: '❌ CI pipeline failed for ${{ github.ref }}'
- name: Create GitHub Release
if: github.ref == 'refs/heads/main' && needs.docker-build.result == 'success'
uses: softprops/action-gh-release@v2
with:
tag_name: v${{ github.run_number }}
name: Release v${{ github.run_number }}
body: |
Automated release from CI pipeline
**Changes:**
${{ github.event.head_commit.message }}
**Docker Image:**
`${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }}`
draft: false
prerelease: false