Training Trackers

FastVideo can send training metrics and validation media to Weights & Biases or SwanLab. Tracking runs only on global rank 0, and local tracker files are stored under <output_dir>/tracker.

Supported Trackers

You can enable more than one backend, for example trackers: [wandb, swanlab]. Metrics and validation media are converted to the artifact type required by each backend.

Install SwanLab

For a published FastVideo installation, install the SwanLab extra:

uv pip install "fastvideo[swanlab]"

For an editable source checkout, include the same extra during installation:

uv pip install -e ".[swanlab]"

If FastVideo is already installed, you can install the compatible SDK directly:

uv pip install "swanlab>=0.6.7"

Authenticate once before starting a training run:

swanlab login

See the SwanLab login documentation for non-interactive and self-hosted setups.

Configure Tracking

Select SwanLab in the YAML config used by the modular training framework:

training:
  checkpoint:
    output_dir: outputs/my_run
  tracker:
    trackers: [swanlab]
    project_name: my_project
    run_name: my_run

To log to both supported services:

training:
  tracker:
    trackers: [wandb, swanlab]
    project_name: my_project
    run_name: my_run

An empty or omitted trackers list selects W&B when project_name is set. Use an explicit none entry to disable external tracking:

training:
  tracker:
    trackers: [none]

Validation Videos

SwanLab currently accepts GIF video artifacts. FastVideo converts validation MP4 files and in-memory video arrays to GIF automatically before logging them. For video files, FastVideo uses the sampling frame rate supplied by the caller, or the source file’s frame rate when no value is supplied. In-memory arrays use the frame rate supplied by the caller. Both forms fall back to 16 FPS when no frame rate is available.

For details about configuring validation callbacks, see Training Infrastructure.