AINode Release Notes

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

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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

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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

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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.

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