Offline Bundle Install

This is the recommended way to install on a customer edge site with no internet, no registry access, and no image building on the box. You install everything from a single self-contained release bundle that ships on USB media.

When to use this page

Use the offline bundle for production edge boxes at a plant or OT site. For a quick laptop evaluation with internet access, the Quickstart is faster.

What you receive

A release bundle is one directory, named for its hardware profile and version (for example amd64-cpu-v1.5.0). Choose the bundle that matches your hardware:

Bundle profile	Use it when the box has…
amd64-cpu	x86_64 CPU, no GPU — CPU-only inference
amd64-gpu	x86_64 CPU + NVIDIA GPU — TensorRT acceleration
jetson	NVIDIA Jetson (arm64 / L4T) edge module

Inside the bundle:

• Directoryamd64-cpu-v1.5.0/

  • Directoryimages/

    • amd64-cpu-v1.5.0.images.tar.zst the three service images, compressed
    • manifest.txt image tags + checksums
  • Directorycompose/

    • docker-compose.release.yml image-based stack (no build step)
    • .env.template port + secret placeholders
  • Directorymodels/

    • predictive_maintenance.onnx demo seed model (random weights)
  • install.sh
  • update.sh
  • uninstall.sh
  • README.txt operator one-pager

The seed model is a demo

The bundled model has random weights — its predictions are meaningless until you replace it with your trained model. See step 5 of the install runbook.

Before you start

The target box must already have these installed (they ship on the golden OS image — the installer checks for them and stops if anything is missing; it does not install them for you):

• Docker Engine 20.10 or newer, with the docker compose v2 plugin
• zstd and openssl
• NVIDIA Container Toolkit — GPU and Jetson bundles only

Caution

Install on a clean target. The release stack reuses the same container names, volumes, and host ports as a development stack — running it on a box that already has Xisom running will collide.

Install runbook

1. Copy the bundle to the box

   Copy the whole bundle directory from your USB media to the target box, then open a terminal in it:

   cd amd64-cpu-v1.5.0/

2. Run the installer

   The installer auto-detects your hardware profile, loads the container images, generates secrets, brings the stack up, and waits for health.

   sudo ./install.sh

   To force a specific profile, or to also install a boot-start service:

   sudo ./install.sh --profile amd64-cpu   # force the profile
   sudo ./install.sh --with-systemd        # also start on boot

   Under the hood install.sh runs these steps in order:

   1. Preflight — checks Docker, the GPU toolkit (GPU/Jetson), and disk.
   2. Verify the image archive checksum (detects corrupt or tampered media).
   3. Load images — docker load from the bundled archive (no pull, no build).
   4. Generate secrets — creates the JWT signing secret and admin password on the box and writes them to a protected .env (mode 600). No secret ever ships inside the bundle.
   5. Start the stack — docker compose up -d.
   6. Health gate — waits for all three services to report healthy.
   7. Seed the admin and print the dashboard URL + admin password once.

3. Save the admin password

   Printed only once

   The installer prints the dashboard URL and the generated admin password a single time at the end of the run. Copy it into your password manager immediately — it is not stored anywhere you can read it back.

4. Confirm the secret was written

   The installer generates the JWT signing secret automatically. You normally never touch it, but you can confirm the protected .env exists:

   ls -l .env        # expect mode -rw------- (600)

   If you ever need to provide your own value (e.g. a corporate secret-management policy), edit .env before the first up and set a strong random value:

   # .env  (placeholder — generate a strong random value, do not reuse)
   AIBOARD_JWT_SECRET=your-strong-random-jwt-secret

5. Replace the demo model

   The bundle ships a demo model with random weights. Replace it with your trained predictive_maintenance.onnx so predictions are meaningful:

   • Before first install: overwrite models/predictive_maintenance.onnx in the bundle, then run install.sh.
   • After install: drop your model into the model-data volume at /data/models and restart the inference service.

   You can also upload models later from the dashboard — see Deploying Models.

6. Verify health

   Confirm all three services are healthy:

   docker compose -f compose/docker-compose.release.yml ps

   Expect three containers in a healthy state. Then open the dashboard URL the installer printed and sign in with the admin account.

   GPU first run is slow

   On GPU and Jetson profiles, the inference service may sit in unhealthy for 2–3 minutes on first boot while it compiles the TensorRT engine. This is expected — wait, then re-check ps.

Day-2 operations

Update

Deploy a newer bundle. The update snapshots the database first so you have a rollback path.

sudo ./update.sh

Carry your live .env into the new bundle

update.sh reads the .env from the bundle directory — it holds your per-deployment secret, ports, and image references. A freshly downloaded bundle has none, so copy the existing install’s .env into the new bundle directory before running the update, or it will abort.

The pre-update snapshot covers the database only (not uploaded models or the TensorRT cache). Back those volumes up separately if you need to roll models back.

Uninstall

./uninstall.sh          # stop the stack, KEEP all data
./uninstall.sh --purge  # also delete models + database (confirmation prompt)

If something goes wrong

Symptom	What it means
Bundle manifest missing	You ran the installer from a source folder, not an assembled bundle. Use the bundle directory you copied from USB.
Architecture mismatch	Wrong bundle profile for this box’s CPU. Use the matching profile.
NVIDIA toolkit not wired in	A GPU/Jetson bundle on a box without the container toolkit. Use the amd64-cpu bundle, or fix the GPU stack on the OS image.
Inference stuck unhealthy	First-run TensorRT compile (GPU) takes 2–3 min — wait. Otherwise check the inference logs.
Archive checksum mismatch	Corrupted or tampered USB media. Re-copy the bundle and retry.

See the full Troubleshooting runbook for symptom-keyed fixes.

Next steps

Connect an input datasource Stream sensor data into the runtime.

Deploy a model Upload and activate your trained model.

Your first inference Seed a model and see live predictions.