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>
Land end-to-end canonical benchmark observability and a synthetic datastream-connectivity preflight across the orchestrator, chat, worker-node, and Python tinygrad worker, plus an xtask validator, so a 4-stage 7B pipeline run is fully diagnosable.
- benchmark_observability: expand stamp() with canonical producer fields (producer_component/instance_id/process_id/sequence, wall_clock_unix_ms, monotonic_ms, clock_source) and schema_version so every event shares one envelope shape
- orchestrator_app + bin/{mvp_chat,worker_node}: stamp OrchBootstrap/OrchPromptEvent/ChatProgress/NodeEvent/SamplerHealth with the canonical fields plus span_id/parent_span_id, and add a 4-phase synthetic datastream preflight (ProducerConfigured/Connected/SyntheticEventSent/Observed) plus an endpoint_config_snapshot event on each process
- apps/mvp-node/tinygrad_worker: add apply_canonical_envelope()/datastream_endpoint_snapshot() and emit_python_datastream_preflight() mirroring the Rust preflight, and enrich benchmark_stamp() with the same producer fields
- bin/worker_node: pass MVP_DATASTREAM_ENDPOINT_ID/MVP_BENCHMARK_PRODUCER_INSTANCE/MVP_IROH_ENDPOINT_ADDR_MASK/MVP_IROH_RELAY_MODE env to the spawned tinygrad worker so its stamps identify the stage
- bin/mvp_chat: add --pipeline-parallel as an alias for --pipeline-stages (with a duplicate-guard) and bump recursion_limit
- xtask: add a benchmark-observability validator (ValidatorFinding/BenchmarkValidation, validate_benchmark_observability, canonical-stamp and stage/edge checks, evidence + gap-report builders) with tests for missing python datastream connectivity, wrong run_id, and missing span_id
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Complete an 8-stage pipeline-parallel run over VastAI by provisioning stages high-to-low, adding per-stage/per-step metrics, host anti-colocation, and provider state-timeout guardrails.
- orchestrator_app: select the next weight-load stage by max index (provision stages high-to-low for parallel spread), add a throttled "loaded N of M; waiting on stage X" stage_provision_wait headline, and surface min_compute_cap/state_timeout_secs in the config dump.
- orchestrator_app: enrich pipeline_token_in/out and tokenizer_decode events with token_count/token_ids/generated_index.
- worker_node: add timing metrics across the data path (helper_execute_ms, egress_ring_read_ms, send_ms, ingress_ring_write_ms, object_load_ms), refactor take_complete_ingress_record into IngressRecordBytes (object_id/sequence/extent/flags), and emit a new object_loaded event.
- vastai_provisioning: track leased host_ids and blacklist already-leased hosts in later ProvisionRequests so stages don't co-locate, and tag SSH-bootstrap retry logs with the attempt number.
- tools/vastai: add min_compute_cap (PP_MIN_COMPUTE_CAP) filter/search query and a LifecyclePolicy state_timeout (PP_STATE_TIMEOUT_SECS) that fails instances stuck in a non-running status instead of polling forever.
- xtask: raise the check timeout to 1800s/30s grace, drop --skip-rebuild for VastAI, aggregate per-stage StepExecuted metrics, add a vastai summary section, and write failure artifacts on abort.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Turn mvp-chat-check into a benchmarking harness with per-stage latency capture, a run envelope, summary artifacts, and a mvp-chat-compare command for delta analysis.
- xtask: add mvp-chat-compare <baseline> <candidate> that checks comparability (schema/scenario/workload/model/provider/pipeline_stages) and prints deltas for total, prepare, standup-to-RPC, and per-prompt roundtrip/first-token/decode/text-decode ms.
- xtask: build_benchmark_summary now writes stdout/stderr/prompts/redacted-config/summary artifacts with per-artifact byte counts and a vastai summary section; rename the dump log to datastream.ndjson.
- xtask: add write_failure_artifacts so failed checks still emit a failure summary with the prompt-corpus blake3 and artifact sizes.
- mvp_chat: add the mvp.chat.benchmark channel and emit_benchmark_envelope (BenchmarkRunEnvelope with model/runtime/provider/workload detail), and tag prompt events with prompt_index and a blake3 prompt_hash.
- tinygrad_worker: add per-phase latency metrics (encode/decode/text-decode/first-token elapsed_ms; stage_execution_ms/record_write_ms on execute_step; ring_readable/encode_prompt/decode_tokens elapsed_ms plus payload sizes).
- vastai_provisioning: emit VastAiLeaseReady and VastAiSshEndpointReady provider lines (contract/offer/host/gpu/dph, ssh host/port/user) for observability.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Land the first working two-stage pipeline-parallel run over VastAI, wiring a real inter-stage data path with observability, a max-price offer cap, and remote-image reuse.
- orchestrator_app: raise the VastAI pipeline-stage cap from 1 to 2 and let VastAI pipeline planning resolve the HuggingFace GGUF from the default local cached-model metadata path instead of requiring host mounts; add --vastai-max-dph-total (CLI/env/TOML) config.
- vastai_provisioning: make complete_bootstrap a no-op so the SSH bootstrap log tail stays alive past runtime-ready until node stop, preserving post-ready worker logs; add a test asserting the tail is only stopped on NodeStop.
- worker_node: emit data-path NodeEvents across the pipeline (iroh_edge_stream_arrived/bytes_read/bytes_sent, egress_ring_read, ingress_ring_write) with edge/byte metadata.
- tools/vastai: add max_dph_total (PP_MAX_DPH_TOTAL) to SelectionPolicy, the reachable-offer filter, and the search query, and improve the empty-pool error message.
- xtask: pass --skip-rebuild for the VastAI scenario and gate it on a new require_vastai_data_path_facts plus GPU facts (ring install, activation object load/step, interstage handoff, iroh edge read/sent).
- mvp_chat: add ChatModelConfig (model id/gguf/tokenizer/max-context) forwarded to the orchestrator; for VastAI + skip-rebuild, emit skip events and reuse the remote node image without a local build.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Get the multinode pipeline-parallel prompt loop actually running locally by supporting both tinygrad LLM backends and switching the Docker scenario to direct addressing.
- tinygrad_worker.py (load_pipeline_stage_model): try the modern tinygrad.llm gguf/model modules first and fall back to the legacy tinygrad.apps.llm TransformerBlock on ModuleNotFoundError, with PipelineStageTinygradModel constructing blocks positionally when no TransformerConfig exists
- tinygrad_worker.py (load_weights): drop the Transformer.from_gguf whole-model branch and its TinygradAppsLlmPartialStageUnsupported fatal, so partial pipeline stages build through load_pipeline_stage_model on either backend
- xtask (MultinodeDocker): stop forcing --relay-mode default --endpoint-addr-mask relay-only, so the scenario runs over the Docker network with full/direct addresses
- xtask (dump-log facts): rename relay_masked_* facts to docker_*, assert multiple workers join the coordinator via direct addresses (direct_addr_count > 0), and relax the benchmark report to skip the ensure_worker_binary span for the Docker scenario
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
- Pull ~7.4k lines out of the orchestrator bin into a new orchestrator_app library
module.
- Wire a local single-node GPU prompt loop into the mvp_chat bin; touch
gpu_worker_ingress_parser.
- Grow xtask and the mvp-node tinygrad worker.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Add end-to-end timing instrumentation and an xtask benchmark report for mvp-chat runs.
- benchmark_observability: add a shared stamping module — stamp(component) emitting schema/pid/monotonic+wall ms from a process-global start and sequence counter, plus unix_ms_now() — stamped onto every mvp-chat/orchestrator/worker-node event and frame-archive record
- mvp-chat: thread a run_id (new --run-id, defaults to 1) through config and the orchestrator CLI, add per-phase started/ready/failed emits for ensure_orch_binary/ensure_worker_binary/prepare_node_image, and a prompt_complete record carrying tokens_generated/elapsed_ms/final_text bytes
- orchestrator/worker-node: stamp bootstrap and prompt events, add arrival_unix_ms to archived frames, propagate MVP_RUN_ID/MVP_LOGICAL_NODE_ID/MVP_STAGE_INDEX into the tinygrad worker, default the device to CPU for the process provider, emit a prompt_rpc started span, and drop the MVP_TINYGRAD_TEST_MODE passthrough
- tinygrad_worker.py: stamp every control() event and tag it with run/node/stage env, add a CPU:X86 fallback when clang is absent, and remove the test_mode() short-circuits
- xtask: replace the flat dump-log fact assertions with a benchmark report builder (build_benchmark_report) that requires named spans (prepare_runtime, ensure_*_binary, weights_loaded, prompt_rpc) and emits per-prompt first-token/decode/tokens-per-second latency; wrap the cargo run in XtaskBenchmark synthetic frames and pass a unix-ms --run-id
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
Replace stdout-parsed runtime-ready detection with an explicit, plugin-driven bootstrap-completion step and actorize SSH bootstrap teardown.
- provisioning: drop the `PluginObservation::RuntimeReady` variant and add `ProvisionPlugin::complete_bootstrap`, an explicit per-node completion hook (no-op for `LocalDockerPlugin`)
- bootstrap_datastream: remove `parse_runtime_ready`/`RuntimeReadyLine` so bootstrap no longer infers readiness from a parsed stdout JSON line
- vastai_provisioning: drop the `ReadyTrackingSink` ready-flag wrapper; the SSH retry loop now runs purely `while !stopping`, and `complete_bootstrap` stops the node's bootstrap with `BootstrapStopReason::RuntimeReady`
- vastai_provisioning: actorize teardown as `SshBootstrapActor` on the swactor `Runtime` (handle holds an `ActorAddress`), with `stop_bootstrap(handle, reason)` delivering a `Stop` message; add `BootstrapStopReason::{RuntimeReady,NodeStop}`
- actors/provisioner: replace the `RuntimeReady` observation arm with a `ProvisionerMsg::RuntimeReady` handler that calls `complete_bootstrap` then `mark_live`/emits NodeLive (or NodeFailed on error)
- callers/tests: wire the new explicit ready flow through node_agent, the orchestrator/worker_node binaries, and `mvp_one_node_chat`; add the `ssh_bootstrap_actor_stop_kills_child` test
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>
Fold the fragmented distribution swim/routing/gossip tests into swim_core, routing,
and swim_actor. Prune the dashboard tui and command surfaces. Add mvp-system actors
(node_agent, orchestrator, stage_controller), the local_e2e harness, and gpu worker
e2e.
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>
Remove the datastore crate, the top-level design/orchestration/ring specs, the
benches, and the ci config. Add the dashboard host telemetry sampler
(cpu/disk/net/gpu/mem). Localize the pipeline-parallel e2e stub/mock paths.
Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
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>
