AINode Release Notes

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.