SwimTelemetry observer records probe RTTs (p50), recent targets, and membership
transitions for the node's telemetry tick. Expand registry/registry_actor, datastream
catalog/emit/source, and the pipeline-parallel cluster/fleet. Wire the node main loop
to emit host/runtime/transport/membership frames.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Promote pipeline-parallel-inference to a first-class app and consolidate observability on the datastream wire, decoupling the dashboard crate from `distribution`.
- apps/pipeline-parallel-inference: move the example out of `examples/` into `apps/` as its own workspace, rename binaries to `pp-worker`/`pp-orchestrator`, and strip release binaries
- cluster: add `ClusterNode`, a synchronous facade over the actorized distribution protocol (IrohDriver + per-node Runtime hosting Swim/Registry/Metadata/Directory actors with a `MembershipFanout`), replacing ad-hoc `driver.node()`/`tick()` call sites
- fleet: add per-node fleet telemetry that ships identity/resource records as `DatastreamFrame`s over the cluster transport to the orchestrator's `DatastreamSink`, folded into a `FleetView` on a 3s tick
- provision: add best-effort, opt-in SSH boot-phase telemetry (`PP_DEPLOY_KEY`) that streams rented-node boot logs onto the orchestrator's datastream as `proc.boot.<stage>.*`
- dashboard: rewire the crate dependency from `distribution` to `datastream`, drop the standalone `swactor-datastream-dashboard` binary, and rewrite `datastream_source.rs` to demux per-node frames into Overview/Distribution/Fleet views with live-node TTL filtering
- distribution: refresh dist/netmap plugin copy and README from "Kademlia routing" to gossip-directory terminology
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Extend the single-GPU example into a two-node pipeline-parallel run that splits llama3.2:1b across two rented vast.ai GPUs and closes the autoregressive decode loop over iroh.
- topology: add linear-chain helpers where each stage derives its neighbours locally from `STAGE`/`NUM_STAGES`, registering `pp-entry`/`pp-exit`/`pp-stage-{i}` SWIM names
- messages: add `StageActivation` (bf16 hidden-state hand-off carrying position/seq_len/is_prefill) and `NextToken` (sampled-token feedback with a `done` flag) that close the autoregressive loop between stage 0 and stage 1
- stage_actor: add `Stage0Actor` (tokenize -> embed_and_forward -> prefill activation; decode_step on each NextToken) and `Stage1Actor` (forward_and_sample -> NextToken back; emit InferenceResponse on EOS/max_tokens)
- vastai: fork the client and add `create_pipeline_instances` (rents one instance per stage, threading `STAGE`/`NUM_STAGES`, best-effort destroys on partial failure) and `destroy_all_instances`
- pp_tinygrad_worker.py: per-stage worker slicing `model.blk[start:end]` in stub and real (GGUF) modes, plus new `pp_gpu_node`/`pp_smoke_run` binaries and ROADMAP/SPEC/TEST_SPEC docs
- reuse: build on the single-GPU example's iroh transport and process bridge unchanged; add actor/codec/topology/integration test suites
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Add a complete single-GPU distributed-inference example that rents a vast.ai GPU, boots a worker container, and runs a prompt end-to-end over iroh/SWIM.
- examples/single-gpu-inference: add the `single_gpu_inference` orchestrator binary that starts a local iroh node, waits for the remote gpu-node to register the `"inference"` SWIM name, then sends an `InferenceRequest` and prints the response
- examples/single-gpu-inference: add the `gpu_node` binary that joins the cluster via `SEED_ADDR`, spawns an `InferenceActor` over `tinygrad_worker.py`, and registers the `"inference"` bridge
- inference_actor: bridge swactor messaging to a Python child process via stdin/stdout JSON, with `ProcessBridge`/`RequestBridge` adapters that satisfy the single-`Incoming` actor constraint
- iroh_transport: add `IrohActorTransport` that sends `WireEnvelope`s over iroh QUIC uni-streams (connection-cached against early close), plus wire encode/decode and an inbound drain helper
- vastai: add a vast.ai REST client (`find_offer` with reliability/cuda/geo filters excluding CN, `create_instance`, `wait_for_running`, `destroy_instance`) parameterised by a mockable `base_url`
- worker/docs/tests: ship `tinygrad_worker.py`/`echo_worker.py` (newline-JSON, `--stub`/`--model` defaulting to llama3.2:1b), a Dockerfile, Makefile, SPEC, and actor/codec/cluster/integration/vastai test suites
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
