Runtime dashboard now features a TUI option and an interface for LLM tool use. Removed some bloat from stats collecting and replaced with a hook function to dump runtime stats into.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Make mailbox push infallible and replace the locked tick-timing buffer with a
lock-free ring.
- channel: HybridChannel::push and Sender::send now return () — overflow always
absorbs, never rejects — dropping the Result<(), T> surface and its callers.
- stats: tick_timings moves from Mutex<VecDeque> to a lock-free crossbeam
ArrayQueue (drop-oldest-on-full), removing the per-tick lock.
- ripple the signature change through worker/runtime/config; drop worker_benchmarks.
- expand runtime_api tests around the new channel/stats shapes.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Add a data-parallel MNIST training example where swactor actors coordinate gradient averaging across workers.
- crates/swactor-dp-mnist/worker.py: add `MnistNet` MLP and `MnistWorker` actor handling `train_batch`/`update`/`evaluate`/`save_model` over a sharded MNIST split with SGD
- crates/swactor-dp-mnist/aggregator.py: add `Aggregator` actor that buffers per-worker gradients, averages them, fans out updates, then logs/evaluates on completion
- crates/swactor-dp-mnist/run_training.py: spawn the Aggregator plus two MnistWorkers (identical initial weights, disjoint shards), run 750 rounds, and poll the inbox for `log`/`done`
- crates/swactor-dp-mnist/pyproject.toml: declare torch/torchvision/numpy deps, an editable local `swactor` source, and the PyTorch CPU index
- Cargo.toml: add a `[profile.bench]` retaining debug symbols (`debug = true`, `strip = false`) for profiling
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Add Criterion multi-threaded runtime benchmarks and a live dashboard load example.
- benches/mt_benchmarks.rs: add a Criterion suite with `mt/single_actor`, `mt/multi_actor`, `mt/ring`, and `mt/spawn` benchmarks over 2-4 threads, using `iter_custom` and a `wait_for_n_done` helper for deterministic completion
- crates/runtime-dashboard/examples/bench_dashboard.rs: add an example driving sink/ring/spawner load scenarios through `start_dashboard`/`DashboardConfig` for sustained cross-worker visualization
- Cargo.toml: register the `mt_benchmarks` Criterion bench target with `harness = false`
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Trim runtime/worker/channel per coverage-fuzz findings; expand runtime_api tests; drop worker_benchmarks.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Split the monolithic crate into a Cargo workspace with the Python and Wasm bindings as separate member crates.
- Cargo.toml: declare a `[workspace]` with members `.`/`crates/swactor-python`/`crates/swactor-wasm`, remove the `python` feature and pyo3 dependency, and change root crate-type from `["cdylib","rlib"]` to `["rlib"]`
- crates/swactor-python: new cdylib crate re-exporting the PyO3 bindings (Runtime/RuntimeConfig/RuntimeHandle/Inbox/Ctx/ActorAddress/RuntimeStats), depending on `swactor` + pyo3; pyproject.toml and uv.lock relocated here from the root
- crates/swactor-wasm: new cdylib crate moved from top-level `wasm/`, depending on `swactor` with `no_random` features
- src/actor.rs: widen `Actor::new`, `AnyActor`, `ContextInner`, and `Ctx::raw_inner` to `pub` so the separate binding crates can drive the runtime
- src/lib.rs: delete the in-tree `python` module and `#[pymodule]`, and gate the `no_random` RNG behind `all(feature = "no_random", not(feature = "getrandom"))`
- tools/: relocate package.json/package-lock.json; drop the now-duplicate `wasm/Cargo.lock`
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
