starred/RuView
1
0 Fork 0
mirror of https://github.com/ruvnet/RuView.git synced 2026-10-21 22:22:33 -05:00
Code Issues Packages Projects Releases 10 Wiki Activity
Files
v2613
RuView/v2/Cargo.toml
T
rUv e5bf0d4892 feat(forecast): add independent Rust multivariate training stack (#1766) 
* feat(forecast): add independent Rust training stack

Signed-off-by: Codex <codex@openai.com>

* docs(forecast): record informal HPO exploration note (unaccepted)

Records an exploratory Darwin Mode numeric-genome hyperparameter search
against the tiny_ci synthetic accuracy protocol: WQL improved 0.257 -> 0.161
-> 0.153 over two search rounds, staying ahead of both baselines throughout.
Explicitly scoped as informal/exploratory (not a frozen leakage-free report,
not eligible for the evidence ledger, not validated for large_linux or any
real dataset) per this doc's own evidence-tagging discipline.

* feat(forecast-core): add per-horizon weighted quantile loss

weighted_quantile_loss collapses the whole horizon into one aggregate
number, hiding whether error grows with lead time. Add a sibling
function using the identical per-cell pinball formula and domain
checks, just reduced per horizon step instead of globally. A unit
test reconstructs the aggregate from the per-step numerators/
denominators to prove the two functions agree exactly, not just
approximately.

* feat(forecast-model): add evaluation-only local activation

Adds activate_for_evaluation and build_eval_input, gated behind the
cpu feature. activate_for_evaluation self-signs an unsigned candidate
with a fixed, publicly-known, non-secret Ed25519 key so a CLI operator
can run inference against their own just-trained candidate without a
real release signature -- explicitly never a production trust path,
documented as such in the module doc comment. The schema-digest check
in ArtifactActivationPolicy still applies in full; only signing is
relaxed. build_eval_input constructs a single-window ModelInput for
CPU inference from raw context-major values/mask arrays, matching the
training batch builders exact time-feature/descriptor encoding.

Six unit tests cover: malformed-candidate rejection, digest-mismatch
rejection (proving the schema check has teeth even on this relaxed
path), getting past the policy gate with a matching digest, and
eval-input shape validation.

* feat(forecast-train): add evaluate and prepare-synthetic-dataset CLI

Turns this sessions throwaway HPO scripts into real, tested CLI
surfaces, replacing two example binaries that bypassed the CLI/TOML
training path entirely.

prepare-synthetic-dataset generates a larger, configurable synthetic
training shard (default 24 windows) plus a matching train-local.toml
with the given OptimizerSpec hyperparameters baked in, and a separate
held-out test.jsonl -- the same synthetic-only, local-validation-only
posture as prepare-local-example, just bigger and configurable.

evaluate scores a trained (unsigned) candidate against held-out
windows and the LastValue/SeasonalNaive baselines: overall weighted
quantile loss, a per-horizon breakdown, 80% interval coverage, and
missingness, for the model and both baselines. Explicitly lists what
docs/benchmarks/ruforecast.mds full accuracy protocol additionally
asks for that this does not cover (abstention coverage, selective
risk, site/device slices, interference regime, RuVector-retrieval
ablation) rather than silently omitting them -- each needs
infrastructure a single-entity synthetic fixture does not have.

Together the three real commands (prepare-synthetic-dataset,
train-local, evaluate) reproduce the exact WQL numbers the old
library-bypassing example scripts produced for the same
hyperparameters, confirmed by re-running the harness/ruview HPO
dry-run end to end against the new CLI path.

New integration test trains a real candidate via smoke, evaluates it
against held-out synthetic windows, and asserts the full report shape
and an empty-input rejection.

* feat(forecast-train): add prepare-synthetic-dataset --seed + Autogenous bridge

--seed (default 0, backward compatible) makes prepare-synthetic-dataset's
generator deterministic per offset: same seed -> byte-identical corpus
across runs (needed to train two genomes on the SAME corpus for a fair
comparison); different seeds -> genuinely independent corpora (needed for
honest multi-seed/multi-judge evaluation upstream, both in
harness/ruview/flywheel/ruforecast/gate.mjs's fitness function and in the
new v2/crates/ruforecast-autogenous-bridge crate added here).

ruforecast-autogenous-bridge is a LOCAL-DEV-ONLY (excluded from the v2
workspace, publish = false) bridge from this crate's real evaluate CLI
into ruvnet/autogenous's new regression-candidate promotion path (separate,
unpushed branch feat/regression-candidate-kind): runs N independent
train+evaluate judges on genuinely distinct synthetic corpora, signs
receipts, and gets a real cryptographically-verified PROMOTE/REJECT
decision -- defense in depth on top of, not a replacement for, Darwin's own
promotion gate. Path-depends on a sibling autogenous checkout that does not
exist in CI; build/run directly via `cargo build --manifest-path
crates/ruforecast-autogenous-bridge/Cargo.toml`.

cargo test -p ruview-forecast-train --no-default-features --features
cpu,cli: all green (unaffected existing tests + this backward-compatible
addition).

* docs(forecast): retract round-2 HPO result, record honest multi-seed finding

The earlier "Informal HPO exploration note" claimed a real improvement
(WQL 0.257 -> 0.153) from a 3-round hyperparameter search. That search
evaluated every candidate against one fixed synthetic corpus (seed 0) for
every round -- textbook overfitting. Independent verification against two
fresh corpora (via the new ruforecast-autogenous-bridge crate) showed the
"winner" losing to the baseline on both.

Fixed the root cause in harness/ruview/flywheel/ruforecast/gate.mjs:
candidates are now scored against three independent corpora, worst-case
across them, not one fixed corpus.

Re-ran the search under the fix. It found a new winner that genuinely beat
baseline on all three of its own search seeds -- and that winner ALSO lost
independent verification on fresh seeds. Two independent search rounds,
pre- and post-fix, both produced an illusory "winner." The honest reading:
at this dataset scale (24 synthetic windows) held-out WQL varies enormously
by which corpus is drawn, regardless of hyperparameters -- confirmed
directly by the baseline genome's own primary swinging from 0.83 to a full
regression across the three fixed search seeds with unchanged
hyperparameters. No RuForecast hyperparameter configuration has been shown
to reliably beat the trivial baselines out-of-sample at this scale.

Also fixes a real design inconsistency surfaced by this exploration: the
Autogenous regression-candidate promotion verifier
(envelope::regression::verify_regression_promotion, separate unpushed
ruvnet/autogenous branch feat/regression-candidate-kind) required all
judges to share one corpus_id, which conflicts with this kind's
intentional cross-corpus judge design. Corrected there (commit bfa4c48);
did not change either REJECT verdict, which were already driven by the
real NotBetterThanParent signal on their own.

Append-only: the original round-2 row is kept, read together with this
new amendment section, per this doc's own evidence-ledger discipline.

* refactor(forecast): extract RuForecast to ruvnet/RuForecast submodule

Mirrors the v2/crates/worldgraph pattern: RuForecast becomes its own
independent public repo/workspace (ruforecast-core/model/train), mounted
at v2/crates/ruforecast as a git submodule, with v2/Cargo.toml path-depping
into its sub-crates and excluding the submodule from the v2 workspace.

Full v2 --workspace check passes; the ruforecast CLI builds and runs
correctly from its new location. Real git history for the extracted
crates (7 commits) was preserved via git-filter-repo, not squashed.

* feat(autogenous-bridge): real-data independent verification tool

real_data_verify.rs: signs two real, temporally-independent judge
measurements (different train/test split boundaries on the same real
household vitals corpus, not synthetic seeds) through the real
Autogenous regression-candidate promotion path, and gets a genuine
cryptographically-checked PROMOTE/REJECT verdict.

Result recorded (see docs/benchmarks/ruforecast.md): REJECT. One judge
nominally beat the trivial baseline but by less than the 0.01
non-inferiority margin; the other judge lost outright. Consistent with
every synthetic-data search this session -- no configuration has yet
been shown to reliably beat trivial baselines out-of-sample, now
including a real 6390-sample household corpus.

Bumps the ruforecast submodule to pick up the evaluate real-gap fix.

* docs(forecast): record real-household-data result (REJECT, signed)

6390 real vital-signs samples, two independent temporal splits,
independently verified via the real Autogenous regression-candidate
promotion path. Signed verdict: REJECT -- neither split cleared the
non-inferiority margin. Same conclusion as every synthetic search this
session, now confirmed with real data too: more real data is the
credible next lever, not further search on this scale of fixture.

* chore(forecast): point ruforecast submodule at published main (post gap-tolerance fix)

* ci(forecast): fix ruforecast-ci.yml for the new v2/crates/ruforecast submodule layout

The RuForecast crates were extracted into a standalone submodule (v2/crates/ruforecast, mirroring the existing v2/crates/worldgraph pattern) and are no longer members of the v2 workspace. The CI workflow still referenced the old in-tree package names/paths (ruview-forecast-core/model/train under v2/), which broke every forecast job with "cannot specify features for packages outside of workspace".

Fixed every job to target the submodule: working-directory -> v2/crates/ruforecast, -p ruview-forecast-* -> -p ruforecast-*, all hardcoded Cargo.toml/source paths in the Python assertion and clean-room-scan blocks, the Swatinem/rust-cache workspaces input, the artifact-tree output paths, and the two levels of relative path (../ -> ../../) in the informational-benchmark steps whose working directory moved one level deeper.

Also collapsed the paths: trigger filters internal-crate globs (v2/crates/ruview-forecast-*/**) down to the single v2/crates/ruforecast path, since GitHub Actions path filters only ever see a submodule gitlink change as one entry in the parent tree, never its internal file paths -- the old globs could never have matched anything.

Includes a real cargo fmt fix (submodule commit d9902f9) for formatting drift left over from the earlier real-data gap-tolerance fix, which the contract job's fmt --check step would otherwise have failed on.

All four forecast CI jobs' real commands verified locally before push: cargo test/check for CPU, CUDA/WGPU compile-check, the contract job's Python assertion block, the hosted-boundaries clean-room scan, and cargo fmt --check.

* docs(forecast): record real BIDMC cross-entity holdout result (REJECT)

Two independent real-patient partitions (34/16 and 24/26 splits, 53 total
ICU patients from PhysioNet BIDMC, Open Data Commons Attribution License
v1.0) both show the model losing to trivial baselines by a decisive margin
(+69% and +601% worse WQL). First genuine cross-entity real-data test this
project has run; same conclusion as every prior synthetic and single-household
test. Full Autogenous signed verification was not run for this entry -
flagged explicitly in the note.

* chore(forecast): point ruforecast submodule at published main (example feature-gate fix)

* chore(forecast): point ruforecast submodule at published main (clippy fix)

---------

Signed-off-by: Codex <codex@openai.com>
2026-09-01 23:35:42 -04:00

309 lines
15 KiB
TOML
Raw Permalink Blame History

[workspace]
resolver = "2"
members = [
"crates/wifi-densepose-core",
"crates/wifi-densepose-physics", # ADR-323 bounded pose assessment/correction
"crates/wifi-densepose-signal",
"crates/wifi-densepose-nn",
# wifi-densepose-api / -db / -config: removed in #578.
# The crate names were reserved early for an envisioned REST/database/config
# split, but no implementation followed and no code referenced them. The
# functionality they would provide is covered today by:
# - REST/WS: `wifi-densepose-sensing-server` (Axum)
# - Config: per-crate config + CLI args in `wifi-densepose-sensing-server`
# and `wifi-densepose-desktop`
# - DB: no persistent state; system is real-time
# If we ever need any of these as a published surface, they can be
# reintroduced with a real implementation.
"crates/wifi-densepose-hardware",
"crates/wifi-densepose-wasm",
"crates/wifi-densepose-cli",
"crates/wifi-densepose-mat",
"crates/wifi-densepose-train",
"crates/wifi-densepose-sensing-server",
"crates/wifi-densepose-aether", # ADR-185 §13 — AETHER pure-compute leaf (std-only)
"crates/wifi-densepose-privshield", # ADR-291 — VEIL privacy shield (compliant-waveform anti-sensing; std-only leaf)
"crates/wifi-densepose-wifiscan",
"crates/wifi-densepose-vitals",
"crates/wifi-densepose-ruvector",
"crates/wifi-densepose-desktop",
"crates/wifi-densepose-pointcloud",
# geo + worldgraph extracted to ruvnet/worldgraph submodule (see crates/worldgraph)
"crates/wifi-densepose-engine", # ADR-135..146 integration/composition layer
"crates/wifi-densepose-calibration", # ADR-151 — per-room calibration & specialist training
# ADR-271 — Cognitum OAuth access-token verification. RuView as an OAuth
# RESOURCE SERVER: offline ES256/JWKS verification of tokens issued by
# auth.cognitum.one, so a user signs in to their own sensing server with
# their Cognitum identity instead of a shared static bearer. No login flow
# and no outbound Cognitum API calls live here — verification only.
"crates/ruview-auth",
"crates/ruview-cognitum-spaces", # ADR-325 Cognitum Spaces read client
"crates/nvsim",
"crates/nvsim-server",
"crates/homecore", # ADR-127 — HOMECORE state machine
"crates/homecore-plugins", # ADR-128 — HOMECORE-PLUGINS WASM runtime (P1 scaffold)
"crates/homecore-api", # ADR-130 — HOMECORE REST + WS API
"crates/homecore-automation", # ADR-129 — HOMECORE automation engine
"crates/homecore-recorder", # ADR-132 — HOMECORE state recorder
"crates/homecore-migrate", # ADR-134 — HOMECORE migration from Python HA
# ADR-100/ADR-101: Cognitum Cog packaging — first Cog from this repo.
# Ships the wifi-densepose pose-estimation model as a signed binary +
# JSONL manifest installable by the Cognitum V0 appliance (cognitum-v0,
# cognitum-cluster-*, ruvultra). The companion appliance-side crate
# lives in cognitum-one/v0-appliance as `cognitum-pose-estimation`.
"crates/cog-pose-estimation",
# ADR-103: Learned multi-person counter (SOTA path) — replaces the
# PR #491 slot heuristic with a Candle network + Stoer-Wagner fusion.
# Motivated by #499 ghost-skeleton reports.
"crates/cog-person-count",
# ADR-116: Home Assistant + Matter Cognitum Seed cog. Wraps the
# ADR-115 MQTT publisher as a Seed-installable artifact with
# mDNS, embedded broker, RuVector thresholds, Ed25519 witness.
"crates/cog-ha-matter",
# ADR-118: BFLD — Beamforming Feedback Layer for Detection. The
# privacy/safety layer that measures and gates identity leakage from
# WiFi BFI captures. Sub-ADRs: 119 (frame), 120 (privacy class),
# 121 (identity risk), 122 (HA/Matter), 123 (capture path).
"crates/wifi-densepose-bfld",
# ADR-147: OccWorld thin-client bridge — WorldGraph PersonTrack history →
# OccWorld Python subprocess → TrajectoryPrior injection into pose tracker.
# worldmodel extracted to ruvnet/worldgraph submodule (consumed via path dep)
# ADR-147 (Phase 5): OccWorld TransVQVAE ported to Candle — native Rust
# inference without Python/IPC overhead. Loaded alongside the Python bridge
# as a faster alternative once Phase-5 weights are available.
"crates/wifi-densepose-occworld-candle",
# rvCSI — edge RF sensing runtime (ADR-095 platform, ADR-096 FFI/crate layout):
# lives in its own repo (https://github.com/ruvnet/rvcsi), vendored here as
# `vendor/rvcsi` and published to crates.io as `rvcsi-*` 0.3.x. Depend on the
# published crates (or the submodule's `crates/rvcsi-*` paths) — not as v2
# workspace members, since `vendor/rvcsi/Cargo.toml` is its own workspace.
"crates/homecore-hap", # ADR-125 — Apple Home HomeKit Accessory Protocol bridge
"crates/homecore-assist", # ADR-133 — HOMECORE voice assistant + ruflo bridge
"crates/homecore-server", # iter-9 — HOMECORE integration binary (all 8 crates wired together)
"crates/ruview-swarm", # ADR-148 — drone swarm control system
# ADR-273..277 — unified RF spatial world model: canonical RF tensor +
# hardware adapters, universal foundation encoder (masked-reconstruction
# pretraining, ≤1% task adapters), RF-aware Gaussian spatial memory,
# physics-guided synthetic RF worlds, edge sensing control plane.
"crates/ruview-unified",
# ADR-262 P1 — anti-corruption bridge converting RuView WiFi-CSI sensing
# output into signed RuField FieldEvents. Path-deps the `vendor/rufield`
# submodule crates (rufield-core/-provenance/-privacy/-fusion); single
# coupling point between RuView and the standalone RuField MFS spec.
"crates/wifi-densepose-rufield",
# ADR-287 — coherent wideband RF tomography research crate: synthetic
# stepped-frequency multi-position measurement simulation + delay-and-sum
# backprojection reconstruction. Standalone leaf (nvsim pattern), zero
# hardware coupling, every number SYNTHETIC/L0 until real wideband RF
# hardware exists.
"crates/wifi-densepose-sar",
# ADR-300 phase 1 — perception substrate spine (new first-party crates):
"crates/ruview-ontology", # ADR-306 canonical spatial ontology (Site..Event)
"crates/ruview-attest", # ADR-305 authenticated sensor identity / RF chain of custody
"crates/ruview-evidence", # ADR-304 evidence engine (per-room/device/subject ledger)
# ADR-300 phase 1 — dependent wave (build on the spine roots above):
"crates/ruview-ood", # ADR-302 OOD KNOWN/DEGRADED/UNKNOWN gating
"crates/ruview-witness", # ADR-319 witness chain (staged signed provenance)
"crates/ruview-certify", # ADR-318 capability certificate
"crates/ruview-scorecard", # ADR-317 multi-domain benchmark scorecard
"crates/ruview-policy", # ADR-321 decision policy / action authorization
# ADR-300 phase 2 — unified world-model core:
"crates/ruview-hal", # ADR-320 sensor HAL (any modality -> Observation)
"crates/ruview-groundtruth",# ADR-303 ground-truth synchronization / validation plane
"crates/ruview-track", # ADR-307 persistent privacy-preserving tracking
"crates/ruview-fusion", # ADR-311 uncertainty-aware fusion -> one world state
# ADR-300 phase 3 — higher-ceiling primitives (on the fused world state):
"crates/ruview-twin", # ADR-315 digital RF twin (per-deployment model)
"crates/ruview-placement", # ADR-308 sensor placement optimizer
"crates/ruview-memory", # ADR-312 long-term spatial memory / anomaly
"crates/ruview-spatial-memory",# ADR-326 tenant-scoped Cognitum Spaces history
"crates/ruview-counterfactual",# ADR-313 counterfactual spatial inference
"crates/ruview-infogain", # ADR-314 information-gain scheduler
"crates/ruview-active", # ADR-309 active sensing control
# ADR-324 — clean-room Kooima off-axis (head-coupled perspective) projection.
# Dependency-free native core; wasm-bindgen surface only on wasm32.
"crates/ruview-offaxis",
# ADR-348 — RuForecast extracted to the ruvnet/RuForecast submodule
# (see crates/ruforecast in the exclude list below); path-deps only.
]
# ADR-040: WASM edge crate targets wasm32-unknown-unknown (no_std),
# excluded from workspace to avoid breaking `cargo test --workspace`.
# Build separately: cargo build -p wifi-densepose-wasm-edge --target wasm32-unknown-unknown --release
#
# ADR-128 P2: example WASM plugin — also wasm32-only (no_std, cdylib),
# excluded for the same reason. Build separately:
# cargo build --target wasm32-unknown-unknown --release -p homecore-plugin-example
exclude = [
"crates/wifi-densepose-wasm-edge",
"crates/homecore-plugin-example",
"crates/worldgraph", # ruvnet/worldgraph submodule — its own workspace (geo/worldgraph/worldmodel)
"crates/ruforecast", # ruvnet/RuForecast submodule — its own workspace (ruforecast-core/model/train)
# LOCAL-DEV-ONLY: path-depends on an absolute, unpublished ruvnet/autogenous
# checkout that does not exist in CI. Build directly with
# `cargo build --manifest-path crates/ruforecast-autogenous-bridge/Cargo.toml`
# on a host with that checkout present. See its Cargo.toml/README.
"crates/ruforecast-autogenous-bridge",
]
[workspace.package]
version = "0.3.1"
edition = "2021"
authors = ["rUv <ruv@ruv.net>", "WiFi-DensePose Contributors"]
license = "MIT OR Apache-2.0"
repository = "https://github.com/ruvnet/wifi-densepose"
documentation = "https://docs.rs/wifi-densepose"
keywords = ["wifi", "densepose", "csi", "pose-estimation", "rust"]
categories = ["science", "computer-vision", "wasm"]
[workspace.dependencies]
# Core utilities
thiserror = "2.0"
# WiFi Veil — compliant-waveform countermeasure against unauthorized WiFi
# sensing (ADR-294). Dependency-free, deterministic, SYNTHETIC-only leaf;
# pinned to an exact rev because the crate is consumed pre-crates.io-release.
wifi-veil = { git = "https://github.com/ruvnet/wifi-veil", rev = "018468b5d2bf41f35c552910f35659830af0eb91" }
anyhow = "1.0"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
serde_yaml = "0.9"
tokio = { version = "1.35", features = ["full"] }
tracing = "0.1"
tracing-subscriber = { version = "0.3", features = ["env-filter", "json"] }
nalgebra = "0.34"
smallvec = "1.15"
bitflags = "2.9"
rapier3d = { version = "0.33", default-features = false, features = ["std", "dim3", "f32"] }
burn-core = { version = "0.21", default-features = false, features = ["std"] }
burn-nn = { version = "0.21", default-features = false }
burn-ndarray = { version = "0.21", default-features = false, features = ["std"] }
burn-train = { version = "0.21", default-features = false }
burn-wgpu = { version = "0.21", default-features = false, features = ["std"] }
burn-cuda = { version = "0.21", default-features = false, features = ["std"] }
# Signal processing
ndarray = { version = "0.17", features = ["serde"] }
ndarray-linalg = { version = "0.18", features = ["openblas-static"] }
rustfft = "6.1"
num-complex = "0.4"
num-traits = "0.2"
# Neural network
tch = "0.24"
ort = { version = "2.0.0-rc.11" }
candle-core = "0.4"
candle-nn = "0.4"
# Web framework
axum = { version = "0.7", features = ["ws", "macros"] }
tower = { version = "0.4", features = ["full"] }
tower-http = { version = "0.6", features = ["cors", "trace", "compression-gzip"] }
hyper = { version = "1.1", features = ["full"] }
# Database
sqlx = { version = "0.7", features = ["runtime-tokio", "postgres", "sqlite", "uuid", "chrono", "json"] }
redis = { version = "0.24", features = ["tokio-comp", "connection-manager"] }
# Configuration
config = "0.14"
dotenvy = "0.15"
envy = "0.4"
# WASM
wasm-bindgen = "0.2"
wasm-bindgen-futures = "0.4"
js-sys = "0.3"
web-sys = { version = "0.3", features = ["console", "Window", "WebSocket"] }
getrandom = { version = "0.2", features = ["js"] }
# Hardware
serialport = "4.3"
pcap = "1.1"
# Graph algorithms (for min-cut assignment in metrics)
petgraph = "0.6"
# Data loading
ndarray-npy = "0.10"
walkdir = "2.4"
# Hashing (for proof)
sha2 = "0.10"
ed25519-dalek = "2.1"
# CSV logging
csv = "1.3"
# Progress bars
indicatif = "0.17"
# CLI
clap = { version = "4.4", features = ["derive", "env"] }
# rvCSI: napi-rs (Rust -> Node bindings) + napi-c (C-shim build glue)
napi = { version = "2.16", default-features = false, features = ["napi8"] }
napi-derive = "2.16"
napi-build = "2.1"
cc = "1.0"
libc = "0.2"
# Testing
criterion = { version = "0.5", features = ["html_reports"] }
proptest = "1.4"
mockall = "0.12"
wiremock = "0.5"
# midstreamer integration (published on crates.io)
# 0.1.0 was yanked; upgrade to latest 0.3/0.2 releases which pull in
# quinn-proto >=0.11.14 (fixes RUSTSEC-2026-0037) and
# rustls-webpki >=0.103.13 (fixes RUSTSEC-2026-0049/0098/0099/0104).
midstreamer-quic = "0.3"
midstreamer-scheduler = "0.2"
midstreamer-temporal-compare = "0.2"
midstreamer-attractor = "0.2"
# ruvector integration (published on crates.io)
# Vendored at origin/main (a083bd77f) in vendor/ruvector; using crates.io versions
# until published. Bumps per ADR-152 §2.6 (2026-06-10 vendor sync survey).
ruvector-core = "2.3.0"
ruvector-mincut = "2.0.6"
ruvector-attn-mincut = "2.0.4"
ruvector-temporal-tensor = "2.0.6"
ruvector-solver = "2.0.6"
ruvector-attention = "2.1.0"
ruvector-crv = "0.1.1"
ruvector-gnn = { version = "2.2.0", default-features = false }
# Internal crates
wifi-densepose-core = { version = "0.3.0", path = "crates/wifi-densepose-core" }
wifi-densepose-physics = { version = "0.1.0", path = "crates/wifi-densepose-physics" }
wifi-densepose-signal = { version = "0.3.0", path = "crates/wifi-densepose-signal" }
wifi-densepose-nn = { version = "0.3.0", path = "crates/wifi-densepose-nn" }
wifi-densepose-api = { version = "0.3.0", path = "crates/wifi-densepose-api" }
wifi-densepose-db = { version = "0.3.0", path = "crates/wifi-densepose-db" }
wifi-densepose-config = { version = "0.3.0", path = "crates/wifi-densepose-config" }
wifi-densepose-hardware = { version = "0.3.0", path = "crates/wifi-densepose-hardware" }
wifi-densepose-wasm = { version = "0.3.0", path = "crates/wifi-densepose-wasm" }
wifi-densepose-mat = { version = "0.3.0", path = "crates/wifi-densepose-mat" }
wifi-densepose-ruvector = { version = "0.3.0", path = "crates/wifi-densepose-ruvector" }
wifi-densepose-worldmodel = { version = "0.3.0", path = "crates/worldgraph/wifi-densepose-worldmodel" }
ruforecast-core = { version = "0.1.0", path = "crates/ruforecast/crates/ruforecast-core" }
ruforecast-model = { version = "0.1.0", path = "crates/ruforecast/crates/ruforecast-model" }
ruforecast-train = { version = "0.1.0", path = "crates/ruforecast/crates/ruforecast-train" }
[profile.release]
lto = true
codegen-units = 1
panic = "abort"
strip = true
opt-level = 3
[profile.release-with-debug]
inherits = "release"
debug = true
strip = false
[profile.bench]
inherits = "release"
debug = true
Reference in new issue View Git Blame Copy Permalink