Add a remote-node file picker that uploads one Python file through the data namespace, materializes it over Iroh, and launches it with contextual-process bootstrap while streaming lifecycle and output events. Clean up execution artifacts, install Python and the swactor wheel in the production node image, and build the worker inside Docker to prevent stale host binaries.
Replace the eventfd/ring job bootstrap with one inherited arena descriptor, actor-owned sessions, sealed blob leases, awaitable inbox wakeups, and zero-copy Python mappings. Route VastAI mock provisioning through image-backed local Docker workers and preserve pinned child and controller routes across directory updates.
Add TOML job-file submission to the Fleet UI with generic started, running, and completed feedback, reusable remote job controller routing, cancellation and kill invariants, Tinygrad fixture and image support, and comprehensive Rust, Python, CUDA, and lifecycle-ordering coverage.
Replace the single-purpose chat runtime with a persistent fleet daemon that provisions, adopts, and controls nodes through the dashboard.
Add distributed job-runner actors and provider-backed deployment so jobs can materialize workspaces, execute remotely, and return outputs over iroh.
Promote the `mvp-system` workspace library crate to a standalone application at `apps/myelin`, rebranding the MVP system along with its binaries, node image, and spec.
- workspace `Cargo.toml`: swap member `crates/mvp-system` -> `apps/myelin` and drop `apps` from `exclude` so the app joins the workspace
- `apps/myelin/Cargo.toml`: declare package `myelin` with `autobins = false` and explicit `[[bin]]` targets `myelin-worker`, `myelin-orchestrator`, `myelin-chat`
- `apps/myelin/src`: move the whole `mvp-system` source tree and rebrand module surfaces (`chat/mod.rs`, `prompt/mod.rs`); add `bin/chat.rs` (`myelin::run_chat_from_args`) and delete the old `mvp_chat.rs`
- `apps/myelin/node-image`: relocate the worker image assets from `apps/mvp-node/` (Dockerfile, Dockerfile.base, tinygrad_worker.py, entrypoint, e2e script) and rename `MVP_SYSTEM_SPEC.md` -> `MYELIN_SPEC.md`
- `xtask`: rewrite build/reference paths for the rename (~1000-line churn); add `crates/dashboard/ACTOR_PANEL_SPEC.md`
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Stand up an interactive end-to-end chat over a CUDA GPU, provisioning a Dockerized node that loads a GGUF model and serves prompts over TCP.
- prompt_rpc: add the newline-JSON prompt protocol (`SubmitPrompt` + `PromptEvent::{TextDelta,Done,Fault}`) carried over TCP
- mvp_chat: add an interactive REPL client connecting to the prompt RPC port (default 127.0.0.1:19777)
- mvp_orch_one_node / mvp_one_node_chat: add the single-node orchestrator that provisions a `LocalDockerPlugin` node, loads `bartowski/Llama-3.2-1B-Instruct-GGUF` (Q4_K_M), and exposes the prompt RPC listener with boot/route/weight timeouts
- mvp_node: add the GPU worker binary that spawns `tinygrad_worker.py` (default device CUDA) and ships runtime telemetry via a `ClusterFrameSink`
- vastai_provisioning / bootstrap_datastream: add the vast.ai provider adapter (`VastAiProvisioningConfig`, `VastAiLeaseClient`) wrapping `swactor_vastai`, plus a bridge that folds provision stdout onto a per-node datastream
- apps/mvp-node: add CUDA base/runtime Dockerfiles (nvidia/cuda 12.6.3, tinygrad 0.12.0, sshd), `mvp_entrypoint.sh` (sshd + mvp-node, held for postmortem), `local_docker_e2e.sh`, the GGUF tinygrad worker, and one-node-chat/bootstrap/vastai guarantee tests
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Move iroh_driver and the relay binary out of distribution into a dedicated
crates/iroh-driver (lib re-exports IrohDriver; relay bin renamed). Remove the node crate
and the single-gpu-inference example; drop the docker/datastream demo. Slim
pipeline-parallel vastai.
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>
Structured observability for the iroh/SWIM layer: Aggregator, typed Event/Snapshot
types, Sink (NoopSink default), ProbeScheduler, process stats, and host/iroh/swim
introspection, plus the swactor-diag-collector, -postproc, and -iroh-relay binaries
that assemble and render per-run bundles. Generalizes the pipeline-parallel-inference
example to N stages and adds the topology-planner spec.
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>
Make distribution and deployment more stable. Consolidate the logic for a generic swactor node.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
