Deployment and building¶

Bundled examples¶

Run a bundled example in one command (installs its requirements automatically):

weightslab start example          # classification (default)
weightslab start example --seg    # segmentation
weightslab start example --det    # detection
weightslab start example --3d_det # 3D LiDAR point-cloud detection

In another terminal, start the UI:

weightslab start

See weightslab start example --help for all options.

Cloud deployment¶

Because the UI is a plain Python process, cloud deployment is straightforward:

Install WeightsLab on the server:
```
pip install weightslab
```
Run weightslab se once to generate certificates.
Start the backend in your training process (wl.serve(serving_grpc=True)).

Start the UI process:

WEIGHTSLAB_UI_HOST=0.0.0.0 weightslab start --port 8080 --certs --no-browser

Put a reverse proxy (nginx / ALB / Caddy) in front of port 8080 and expose only 443 publicly.

The UI and backend can run on different machines — set --backend-host and --backend-port accordingly.

Example systemd unit¶

[Unit]
Description=Weights Studio UI
After=network.target

[Service]
EnvironmentFile=/etc/weightslab/env
ExecStart=/usr/local/bin/weightslab start --port 8080 --no-browser
Restart=on-failure
RestartSec=5

[Install]
WantedBy=multi-user.target

Building the frontend from source¶

The pre-built SPA is vendored into weightslab/ui/static/. To rebuild from the weights_studio source repository and update the vendored copy:

  # from the weights_studio repo
  npm ci && npm run build

# from the weightslab repo
rm -rf weightslab/ui/static/*
cp -R ../weights_studio/dist/. weightslab/ui/static/