AINode Updates & Release Notes

[0.5.3] — 2026-07-07

Fixed

Truthful instances everywhere (#59) — every instance card shows its node NAME (not a hex id); the Server view lists stacked instances with node + port and its count matches reality (Eject only where it can actually target); per-instance status probes both directions (starting → serving, and back to failed when an engine dies — no more stale STARTING bars or false READY); the top-bar update banner now updates the whole fleet (/api/cluster/update-all) with an honest confirm, not just the head node.

[0.5.2] — 2026-07-06

The two majors from the 0.5.1 live lifecycle verification. Image published to GHCR;
deploy via ainode update at the operator's discretion.

Fixed

Download Cancel is real (#56) — cancel was a backend no-op (the flag was never wired into snapshot_download; a cancelled 15 GB download ran to completion). Downloads are now per-file, cancel-checked between files, commit-pinned (single sha for the whole snapshot — no mixed-commit directories if the repo is pushed mid-download) and parallel (AINODE_DOWNLOAD_MAX_WORKERS, cancellable futures).

Launch form respects user node picks (#56) — auto-recommend fired on model select and silently reset the node dots to the head, landing node-targeted loads on the wrong machine. Manual node toggles now set an intent flag auto-recommend honors.

[0.5.1] — 2026-07-06

Same-day follow-up to 0.5.0: every defect found while live-proving the 0.5.0 fleet
(GUI sweep, browser-driven training, vision serving), fixed and review-gated.

Fixed

• Per-load overrides survive restarts (#51) — solo loads persisted kv_cache_dtype
  / max_model_len / aliases only to the stacked-instance manifest; the primary model
  boots from config.json and silently lost them (a VLM loaded with kv_cache_dtype=auto
  came back on fp8 → corrupted output). All override fields now persist and reset
  correctly, with no cross-model inheritance on the launch config.

• fp8 KV cache off by default for multimodal models (#51) — fp8 KV corrupts VLM
  generation on GB10 (text models unaffected); vision models now default to auto,
  explicit operator fp8 still honored via a provenance flag.

• Stacked-load admission guard (#53) — stacked loads require explicit
  gpu_memory_utilization (400) and reject when the node's projected total exceeds
  0.9 (409) — the missing check let a default-sized second model hard-crash a node
  (unified memory). Gate runs before any existing instance is touched.

• Training accepts on-disk models (#53) — wizard cards submitted disk slugs
  (org--name) that HF rejects; wizard now sends canonical repo ids and the container
  builder maps any on-disk reference to its mount across all four disk layouts.

• Training/merge no longer require live PyPI (#53) — peft wheel vendored per node
  with a tolerant, import-guarded install chain (a review-caught event-loop freeze in
  the wheel fetch was fixed with executor offloading).

• Node-targeted launches (#53) — the launch form loaded on the head regardless of
  node selection; now routes through /api/cluster/load with the chosen node_id,
  plus a GMU input (validated on distributed launches too).

• Stacked instances visible (#53) — /api/nodes surfaces per-node instances;
  dashboard renders STACKED sub-cards (ports 8001+) with their own unload controls.

• GUI lifecycle polish (#54) — Server-view LLM "Load" button actually loads
  (was a "coming soon" toast); cancel-download failures surface a toast and recover
  (true event delegation on the queue); training wizard shows an honest empty state
  instead of hardcoded not-on-disk models; dead renderModels() view purged.

Added

• AutoData v2.2 live results (#52) — first live val-set run on the fleet:
  weak-solver lift +0.458 (0.500 → 0.958 on 24 held-out GT probes),
  exact-McNemar p=0.0005, 44% yield, early stop at target in round 1.

[0.5.0] — 2026-07-06

Consolidates the fleet-internal 0.4.45 / 0.4.46 builds (never published to GHCR)
plus everything since. First release cut by the tag-triggered CI pipeline.

Added

AutoData (training utility) — agentic Δ-filtered synthetic-data generation
(Meta Autodata). ainode/training/autodata/: a Challenger generates tasks, weak +
strong solvers attempt each, a Judge grades both, and only Δ = I_strong - I_weak == 1
examples (strong solves, weak fails — the "zone of proximal development") are kept and
emitted as ShareGPT JSONL with a yield report. Pure HTTP over AInode-served
OpenAI-compatible endpoints (no torch — runs in the slim orchestrator). CLI:
python -m ainode.training.autodata.run --config cfg.json. Includes the v2.1
history-aware meta-optimizer (run --meta), a dashboard panel, and a verify-mode
judge (#40, #44).

Training in a spawned GPU container (#46) — LoRA/QLoRA/full jobs and adapter
merge now run in a GPU container (quant-image based) when the orchestrator is
the slim shipped image: models store mounted RW, datasets RO, runner + container
config staged into the job dir, peft shimmed at launch. Job cancel tears down
the actual container. Distributed (DDP) training remains host-mode.

Training view completed (#45) — job detail gains state-gated Merge Adapter →
Full Model and Resume from Checkpoint actions plus an Artifacts panel with
download links; dead legacy form + placeholder Benchmarks tab removed.

Deploy pipeline (#47) — git tag vX.Y.Z → CI (self-hosted Spark runner) builds
and pushes ghcr.io/getainode/ainode:X.Y.Z; ainode update [version], POST /api/engine/update, and POST /api/cluster/update-all now genuinely swap the
running image via an image.env EnvironmentFile read by the systemd unit
(Restart=always, self-stop relaunch), gated so un-migrated units pin but never
self-kill. install.sh resolves and pins the latest numeric tag instead of
trusting :latest.

PR CI (#45) — tests.yml runs ruff + pytest on every PR and main push.

Launch path: served-model-name aliases + per-load overrides (#41) —
served_model_name: [aliases], max_model_len, kv_cache_dtype, quantization,
trust_remote_code accepted by POST /api/models/load, emitted on both local-mount
and remote paths, persisted in the replay manifest.

Fixed

Merge endpoint rejected its own sentinel config (method="merge" failed
validation) — merges silently 500'd (#45).

version is now single-sourced from package metadata (pyproject) — the banner
can no longer drift from the built image (#47).

Models page "Installed" list shows disk-only models, not just catalog entries (#42).

Dashboard no longer re-renders the active view on the 5s poll while the user is
interacting (#43).

Repo-wide lint debt cleared (64 ruff errors → 0) and kept at zero by CI (#45).

[0.4.11] — 2026-06-17

Fixed (dashboard now reports the truth — Phase 1+2)

• Phantom READY eliminated — /api/status engine_ready is now a live /v1/models probe each call (latched engine.ready no longer trusted); the instances panel + master node card derive status from it (READY vs STARTING) instead of a hardcoded READY.
• Per-node VRAM no longer stuck at 0% — metrics/collector.py falls back to psutil for GB10 unified memory (nvidia-smi reports N/A), and the UI merges live GPU metrics into the (local) node so the memory ring shows real %. (Per-peer VRAM still pending a metrics fan-out — Phase 3.)
• Cluster graphic shows distributed membership — participating nodes are stamped with the model + TP=N from the authoritative /api/cluster/resources distributed_instance (the old path keyed off active_sharding, which is null while serving, so workers showed nothing).
• Ray status no longer falsely "not installed" — /api/sharding/status derives Ray health from the head engine when a distributed instance is serving (the orchestrator container has no ray binary to probe).
• DELETE works on a dead/phantom instance — unload force-clears (stopped: true) when the engine is unreachable instead of blocking on a SIGTERM that can't confirm.

Changed

• AUTO sharding now defaults to Tensor parallel (was Pipeline) — matches the distributed launch path and this hardware. Launch UI defaults the Sharding pill to Tensor and relabels the node selector "Tensor Parallel Size (nodes)".
• Launch dropdown marks model state — loaded (●) / on-disk (○) so you can tell what's downloaded.

v0.4.7

Five UI fixes and infrastructure documentation for shared model storage.

Fixed

Image drop overlay stuck

Dragging a file across the chat window showed "DROP IMAGES TO ATTACH" that couldn't be dismissed. Now clears on Escape, click-outside, or dragging away.

Master stuck on "starting..." forever

When the master had no model configured (fresh install or idle cluster), the topology showed a permanent loading overlay. Now correctly shows "online" — the server IS ready, there's just no model to wait for.

Minimum Nodes capped at 3

Was hardcoded. Now populated dynamically from the discovered cluster size — shows 1-2-3-4 with four nodes.

Launch model dropdown incomplete

Only showed models loaded in vLLM. Now merges catalog + disk-scanned models so everything you've downloaded appears.

Download button on already-downloaded models

Live catalog tabs (Trending, Most Used, Latest, Search HF) now check disk presence. Shows "Downloaded" badge and blocks re-download with a toast. Fixes #35.

Shared model storage

AINode supports NFS-shared model storage across a cluster. Download a model once to a central NVMe-over-TCP volume (NAS, MikroTik ROSA, TrueNAS, etc.), export via NFS, and mount on every node. Set models_dir in each node's config to the shared path — all nodes serve the same weights, zero duplicate downloads.

// ~/.ainode/config.json on each node
{ "models_dir": "/mnt/shared-models" }

Upgrade

ainode update

Full changelog · Docs

v0.4.5

Fixed

Master node shows its identity while loading

The master node circle now always shows the node name, GPU type, and crown as soon as it's discovered — even while vLLM is still warming up. A subtle spinning arc + dim veil + "starting..." overlay communicates the loading state without hiding the node's identity. Fades out cleanly when the engine is ready.

"Update all" button is now context-aware

Hidden by default. Only appears in the CLUSTER pill when a newer version is available on GHCR (/api/version/check). Shows the target version number in the button label. Hides again after a successful update.

Upgrade

ainode update

Full changelog · Docs

What's new in v0.4.4

Fixed

AWQ models on GB10 now work correctly.

vLLM auto-upgrades AWQ → awq_marlin (a fused Marlin kernel), but awq_marlin CUDA kernels aren't compiled for sm_12.1 (GB10/Blackwell) in the base image. AINode now pins --quantization awq automatically when loading an AWQ model, preventing the upgrade.

Fixes #34 — reported by Chennu@riai360.

Added

• ⬆ Update all nodes from the master UI

The CLUSTER pill in the topology view has a new ⬆ Update all button. Click to update every node in the cluster simultaneously — master SSHes into workers in parallel, runs docker pull + restart, then updates itself last. Live per-node progress panel shows pending → updating → done/failed.

• Topology loading animation

Before the engine is ready, the cluster canvas shows a pulsating "Loading..." circle at center (same size as the real master node). When the engine comes online, the loading ghost cross-fades out and the real node fades in. Worker nodes fade in individually as they're discovered.

Install / upgrade

# Fresh install
curl -fsSL https://ainode.dev/install | bash
# Upgrade existing install
ainode update
# Update entire cluster from master UI
# → open http://<master>:3000 → click ⬆ Update all in the cluster pill

Images

docker pull ghcr.io/getainode/ainode:0.4.4
docker pull argentaios/ainode:0.4.4

Full changelog · Docs

What's new in v0.4.3

Full training pipeline — from raw dataset to deployable adapter, entirely in the browser.

Training: Artifact retrieval

Download any training output file (adapter weights, tokenizer, checkpoints) directly from the UI or API

GET /api/training/jobs/{id}/output — list artifacts

GET /api/training/jobs/{id}/output/{filename} — stream download

Training: LoRA merge

Merge a LoRA/QLoRA adapter into the base model with one click

POST /api/training/jobs/{id}/merge — async merge via PEFT.merge_and_unload()

Merged model ready for vLLM inference

Training: Checkpoint resume

Resume interrupted or failed jobs from the latest checkpoint

POST /api/training/jobs/{id}/resume

Training: Evaluation loop

Configurable train/eval split (default 10%)

eval_loss + eval_samples_per_second reported in real-time progress

Best checkpoint saved automatically

Training: W&B integration

Set wandb_project to stream loss curves to Weights & Biases

Training: Custom templates

Save your own training templates from the wizard

POST /api/training/templates — persisted to disk

Other fixes (v0.4.2 features also in this image)

Cancel in-progress downloads (✕ button)

Downloaded models show correctly in catalog + "Launch Model" button

Version update badge in top bar — click to update from the browser

pynvml FutureWarning suppressed from logs

Install / upgrade

Fresh install

curl -fsSL https://ainode.dev/install | bash

Upgrade existing install

ainode update

Container images

docker pull ghcr.io/getainode/ainode:0.4.3 # GHCR (canonical)
docker pull argentaios/ainode:0.4.3 # Docker Hub mirror

Full changelog · Docs

Highlights

AINode is now a single-container install: docker pull ghcr.io/getainode/ainode:0.4.0.

No host Python venv, no source-built vLLM. Web UI, OpenAI-compatible API, and GB10-patched vLLM all version-locked in one image.

What's in this release

Unified image — one docker run per node, systemd unit on the host.

Three node modes: solo, head, member. The head orchestrates cross-node tensor-parallel via a patched NCCL (dgxspark-3node-ring); members broadcast their presence on UDP 5679 and reserve GPUs for Ray workers placed by the head.

UI auto-wires distributed launches — pick Minimum Nodes ≥ 2 + Tensor in Launch Instance, the UI writes config and hot-swaps the engine.

Real multi-node cluster topology — aggregated VRAM across members, peer IPs captured via UDP recvfrom, "DISTRIBUTED · TP=N" badges.

NFS-shared model storage pattern + docs.

Verified TP=2 cross-node inference on NVIDIA GB10: 61 GB of model weights on each GPU, NCCL over RoCE @ 200 Gb/s, ~35 tok/s for warm 1.5B model.

Honest State-of-Distributed-Inference section in the README.

What works, what doesn't, lessons learned, and our "why 3 nodes is harder than 2, 4 is probably easier" hypothesis.

Install

curl -fsSL https://ainode.dev/install | bash

or directly:

docker pull ghcr.io/getainode/ainode:0.4.0

docker pull argentos/ainode:0.4.0 # Docker Hub mirror

Screenshots

See README.md on main for the full product tour.

Known limitations

3-node TP requires a proper mesh or dedicated switch subnet — see Networking requirements in the README.

Browser-based fine-tuning UI is scaffolded but not yet validated end-to-end on real GPUs. Tracked as roadmap item.

Ray over VPN (Tailscale) doesn't work for NCCL — use physical cables or a dedicated switch.

Artifacts

ghcr.io/getainode/ainode:0.4.0 (primary)

argentos/ainode:0.4.0 (Docker Hub mirror)

Contributors

This release was driven by Jason Brashear with AI-pair-programming via Claude Opus 4.6 (1M context). All code and docs in this repo are Apache-2.0.