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name: training-evaluation description: "Build and troubleshoot UniAD distributed and SLURM train/eval runs for BEVFormer, stage1, and stage2 workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Evaluation

Use this sub-skill when the task is about launching, resuming, or troubleshooting UniAD training or evaluation runs for BEVFormer, stage1 track/map, or stage2 end-to-end workflows.

Route here for

• tools/train.py and tools/test.py command questions.
• tools/uniad_dist_train.sh, tools/uniad_dist_eval.sh, tools/uniad_slurm_train.sh, and tools/uniad_slurm_eval.sh launcher behavior.
• Checkpoint placement, work_dirs/ layout, logs, and stage smoke checks.
• --seed, --deterministic, --cfg-options, --resume-from, or load_from usage.
• GPU-count, memory, or distributed-launch issues during training or evaluation.

Route elsewhere for

• Dataset download, info-PKL generation, CAN bus/map layout, or motion-anchor preparation -> data-preparation.
• Config semantics, model-head ownership, or architecture changes -> config-and-model-architecture.
• Result PKLs, visualization, or log plots -> visualization-and-results.

Operating rules

• Prefer command construction, path validation, and dry-run diagnosis before recommending an expensive run.
• Treat ckpts/ at the repo root as the default checkpoint landing zone; the stage configs use relative load_from paths that expect that layout.
• tools/test.py is distributed-only in practice: the non-distributed branch asserts false, so evaluation must go through torchrun, srun, or the bundled command builder.
• The bundled launchers cap one node at 8 GPUs per node. Fewer GPUs are allowed, but runtime increases and exact metrics can drift.
• Quote list or tuple --cfg-options values so the shell preserves brackets and commas.
• Use --resume-from only for resuming optimizer or scheduler state; use load_from or --cfg-options load_from=... for initialization checkpoints.
• When the user asks for an exact metric match, remind them that the published stage1 eval numbers are reference targets and can vary slightly with GPU count or launch topology.

Read/run next

• Read references/train-eval-cli.md for CLI surfaces, launcher flags, launcher defaults, and work-dir/log behavior.
• Read references/runtime-and-gpu.md for the public v2.0 stack, memory guidance, and inspection caveats.
• Read references/checkpoints.md for checkpoint names, placement, and expected stage signals.
• Read references/troubleshooting.md for missing-data, missing-checkpoint, launcher, GPU-count, and version-mismatch failures.
• Run scripts/build_uniad_command.py with --help to render a dry-run command template without starting training or evaluation.
• If you need dataset preparation or visualization instead, switch sub-skills rather than extending this one.