BNNR

CLI Reference

What you will find here

Command reference for python3 -m bnnr aligned to src/bnnr/cli.py.

When to use this page

Use this for no-code workflows: training, report reading, dashboard replay/export.

Entry point

python3 -m bnnr --help

train

python3 -m bnnr train --config CONFIG [OPTIONS]

Supported datasets

  • mnist
  • fashion_mnist
  • cifar10
  • imagefolder

Multi-label classification

bnnr train with any of the datasets above always builds single-label pipelines (CrossEntropyLoss, one class index per sample). Setting task: multilabel in your config YAML does not change that behavior. For multi-label, use the Python API (Golden Path) or the scripts under examples/multilabel/ (Examples Guide).

Main options

  • --config, -c (required)
  • --dataset
  • --data-dir
  • --data-path (required for imagefolder)
  • --output, -o
  • --device, -d (cuda, cpu, auto)
  • --epochs, -e
  • --seed, -s
  • --no-xai
  • --augmentation-preset, --preset (auto, light, standard, aggressive, gpu)
  • --with-dashboard / --without-dashboard
  • --dashboard-port
  • --no-auto-open
  • --dashboard-token
  • --batch-size
  • --max-train-samples
  • --max-val-samples
  • --num-classes (for imagefolder)

Behavior notes

  • --with-dashboard (default): starts live dashboard server and keeps process alive.
  • --without-dashboard: no live server; good for one-shot runs.
  • CLI keeps event logging enabled so dashboard export works after training.

Examples

# CIFAR-10 one-shot run
python3 -m bnnr train \
  --config examples/configs/classification/cifar10_example.yaml \
  --dataset cifar10 \
  --preset light \
  --without-dashboard
 
# ImageFolder
python3 -m bnnr train \
  --config examples/configs/classification/imagefolder_example.yaml \
  --dataset imagefolder \
  --data-path /path/to/dataset

report

python3 -m bnnr report path/to/report.json --format summary
python3 -m bnnr report path/to/report.json --format json
python3 -m bnnr report path/to/report.json --format json --output report_payload.json

Notes:

  • --format html is intentionally rejected in current CLI.
  • --output writes rendered report output to file (summary text or JSON payload).
  • Use dashboard export for static HTML output.

Dashboard commands

python3 -m bnnr dashboard serve --run-dir reports --port 8080
python3 -m bnnr dashboard export --run-dir reports/run_YYYYMMDD_HHMMSS --out exported_dashboard

dashboard serve options:

  • --run-dir
  • --port
  • --frontend-dist
  • --token (or env BNNR_DASHBOARD_TOKEN)

dashboard export options:

  • --run-dir (required)
  • --out (required)
  • --frontend-dist

Dashboard usage notes (important)

  • dashboard serve prints both Local URL and Network URL plus terminal QR code.
  • Open Local URL on desktop first, then use QR from phone on the same network.
  • For secured controls, use --token (or BNNR_DASHBOARD_TOKEN).
  • For production workflow details (pause/resume, mobile access, export), see Dashboard Guide.

Utility commands

python3 -m bnnr list-augmentations
python3 -m bnnr list-augmentations --verbose
python3 -m bnnr list-presets
python3 -m bnnr list-datasets
python3 -m bnnr version