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5 commits

Author SHA1 Message Date
c6af8e0a5d feat: successful 8 stage pipeline parallel run, more metrics
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>
2026-07-25 20:04:57 +04:00
f54f62b491 refactor ssh bootstrap logic
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>
2026-07-08 18:06:00 +04:00
6f1c048669 refactor: retire old pipeline-parallel app, restructure mvp-system
- Drop the standalone apps/old-pipeline-parallel-inference app and its
  fleet/orchestrator/tests (~24k lines).
- Add datastream::hardware (cpu/gpu/net) modules; add mvp-system config, arena_manager,
  and vastai_offer_preview.
- Rename mvp_orch_one_node->orchestrator and mvp_node->worker_node; expand VastAI
  provisioning; rework xtask runner.


Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
2026-07-07 14:40:02 +04:00
a97864f7ec feat(mvp-system): expand one-node mvp binaries and provisioning
Flesh out mvp_node, mvp_one_node_chat, and mvp_orch_one_node binaries. Add node_image
and relay_provisioning; grow provisioning and vastai. Tune datastream mux/timing/emit.


Signed-off-by: Zachery Aaron Shores-Chmielewski <zacheryasc@gmail.com>
2026-07-05 13:59:51 +04:00
6608824cb0 feat(mvp-chat): local e2e chat on cuda gpu
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>
2026-07-01 12:44:25 +04:00