mirror of
https://github.com/ruvnet/RuView
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ruv
99fea9df9b
Root cause (investigated, same rigor as the MAT fix): `wifi-densepose-train` declared `wifi-densepose-nn` as a dependency but NEVER imported it anywhere in src/ or bin/ — a dead dependency. (The tch-backend model path uses the `tch` crate directly, not wifi-densepose-nn; `grep -r wifi_densepose_nn src/` returns nothing.) That dead dep pulled `ort` (ONNX Runtime) + reqwest/hyper into every downstream consumer, including the ADR-185 `[meridian]` wheel, which binds only train's pure-compute geometry / rapid_adapt / eval modules + signal::hardware_norm. `cargo tree -i wifi-densepose-nn` confirmed train was the SOLE path pulling nn into the wheel; after removal, the package is absent from the graph. Fix: remove the dead dep line from train's Cargo.toml (no code change, no feature-gating needed — nothing referenced it). Not a hoist: the coupling turned out to be nonexistent, not deep. Measured (maturin build --release --features meridian --strip): BEFORE: 1.8 MB AFTER: 1.7 MB (both already within the ADR-117 §5.4 <=5 MB budget). Honest note: unlike MAT — which instantiates OnnxSession and thus bundles the ort native runtime (8.4 MB) — train never references ort, so the native lib was dead-stripped and the wheel was already under budget. The real win is removing ort/reqwest/hyper from train's dependency graph and compile for ALL consumers, not a dramatic wheel shrink. Verified: cargo build -p wifi-densepose-train (default=[]) OK cargo tree -i wifi-densepose-nn -> no longer in the graph cargo test --features meridian --test meridian_parity 2/2 pass maturin develop --features meridian + pytest test_meridian 13/13 pass
110 lines
3.3 KiB
TOML
110 lines
3.3 KiB
TOML
[package]
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name = "wifi-densepose-train"
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version = "0.3.2"
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edition = "2021"
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authors = ["rUv <ruv@ruv.net>", "WiFi-DensePose Contributors"]
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license = "MIT OR Apache-2.0"
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description = "Training pipeline for WiFi-DensePose pose estimation"
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repository = "https://github.com/ruvnet/wifi-densepose"
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documentation = "https://docs.rs/wifi-densepose-train"
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keywords = ["wifi", "training", "pose-estimation", "deep-learning"]
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categories = ["science", "computer-vision"]
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readme = "README.md"
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[[bin]]
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name = "train"
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path = "src/bin/train.rs"
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[[bin]]
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name = "verify-training"
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path = "src/bin/verify_training.rs"
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required-features = ["tch-backend"]
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# AetherArena (ADR-149) deterministic score runner — the CI harness-gate entry
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# point. Pure ruview_metrics (ndarray + sha2), no torch, so it builds and runs
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# under --no-default-features for a fast, GPU-free PR gate.
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[[bin]]
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name = "aa_score_runner"
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path = "src/bin/aa_score_runner.rs"
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[features]
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default = []
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tch-backend = ["tch"]
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cuda = ["tch-backend"]
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[dependencies]
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# Internal crates
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wifi-densepose-signal = { version = "0.3.0", path = "../wifi-densepose-signal", default-features = false }
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# NOTE: `wifi-densepose-nn` was declared here but never imported anywhere in
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# this crate's src/ or bin/ (the tch-backend model path uses `tch` directly,
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# not this crate). It was a dead dependency that pulled `ort` (ONNX Runtime) +
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# reqwest/hyper into every downstream consumer — including the ADR-185
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# `[meridian]` wheel. Removed to slim the dependency graph. Inference at
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# serving time is done via `wifi-densepose-nn` by the binaries that actually
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# load models, which depend on it directly.
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# Core
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thiserror.workspace = true
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anyhow.workspace = true
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serde = { workspace = true, features = ["derive"] }
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serde_json.workspace = true
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# Tensor / math
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ndarray.workspace = true
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num-complex.workspace = true
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num-traits.workspace = true
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# PyTorch bindings (optional — only enabled by `tch-backend` feature)
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tch = { workspace = true, optional = true }
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# Graph algorithms (min-cut for optimal keypoint assignment)
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petgraph.workspace = true
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# ruvector integration (subpolynomial min-cut, sparse solvers, temporal compression, attention)
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ruvector-mincut = { workspace = true }
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ruvector-attn-mincut = { workspace = true }
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ruvector-temporal-tensor = { workspace = true }
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ruvector-solver = { workspace = true }
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ruvector-attention = { workspace = true }
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# Data loading
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ndarray-npy.workspace = true
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memmap2 = "0.9"
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walkdir.workspace = true
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# Serialization
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csv.workspace = true
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toml = "0.8"
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# Logging / progress
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tracing.workspace = true
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tracing-subscriber.workspace = true
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indicatif.workspace = true
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# Async (subset of features needed by training pipeline)
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tokio = { workspace = true, features = ["rt", "rt-multi-thread", "macros", "fs"] }
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# Crypto (for proof hash)
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sha2.workspace = true
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# CLI
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clap.workspace = true
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# Time
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chrono = { version = "0.4", features = ["serde"] }
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[dev-dependencies]
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criterion.workspace = true
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proptest.workspace = true
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tempfile = "3.10"
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approx = "0.5"
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# Used by tests/test_real_loader.rs to write .npy fixtures that exercise the
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# real MmFiDataset disk-loading path (the deterministic proof uses the
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# in-memory SyntheticCsiDataset, which bypasses .npy parsing).
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ndarray.workspace = true
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ndarray-npy.workspace = true
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[[bench]]
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name = "training_bench"
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harness = false
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