---

name: training-evaluation description: "Construct, review, and troubleshoot D-FINE train.py commands for training, test-only evaluation, resume, tuning, distributed launch, AMP/EMA, output/summary dirs, and CLI overrides." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Evaluation

Use this sub-skill for command construction and troubleshooting around train.py.

Route here

• Training, --test-only evaluation, --resume, and --tuning
• Single-process or torchrun launch planning
• AMP, EMA, output directories, summary directories, and YAML overrides
• Checkpoint loading behavior and safe preflight checks

Do not use this sub-skill for

• Dataset schema or class-count editing: see ../data-and-configs/SKILL.md
• Model internals or registry changes: see ../architecture-api/SKILL.md
• ONNX / TensorRT / OpenVINO / deployment commands: see ../inference-export/SKILL.md

Bundled helpers

• scripts/dfine_train_command.py builds shell-quoted train.py commands without executing them.
• references/training-and-evaluation.md explains flags, launch topologies, checkpoints, and output files.
• references/troubleshooting.md covers the common failures and fast fixes.

Safe defaults

• Use --mode train|test|resume|tune instead of mixing checkpoints by hand.
• Use --single-process for one-process commands; otherwise let the helper infer torchrun from --devices or --nproc.
• Use --update only for config keys, not for dedicated command flags.