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name: training-and-configs description: "Plan detrex LazyConfig training, evaluation, dataset, launcher, and runtime override workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

detrex training and configs

Use this sub-skill when the task involves launching, adapting, or debugging detrex training/evaluation commands or Python LazyConfig files.

Route here when

• The task mentions tools.train_net, hydra_train_net, LazyConfig, get_config, train.max_iter, train.init_checkpoint, command overrides, or DETECTRON2_DATASETS.
• The user needs a train, eval-only, resume, fast debug, multi-GPU, multi-machine, Hydra, or submitit/Slurm command plan.
• The user is changing datasets, dataloaders, optimizers, schedulers, DDP fields, AMP, EMA, WandB, checkpoints, output directories, or custom backbones.
• The user is deciding whether a project-specific trainer such as DINO or CO-MOT is required instead of the generic detrex trainer.

Fast route

1. Read references/configuration.md when the task is about config namespaces, common fragments, dataset names, optimizers/schedules, AMP, EMA, WandB, DDP, or custom backbone wiring.
2. Read references/training-workflows.md when the task is about train/eval commands, resume, fast debug, distributed launch, Hydra/submitit, project trainers, datasets, or output directories.
3. Run scripts/build_train_command.py with --help before constructing commands. The helper only prints commands; it never launches training or downloads assets.
4. Read references/troubleshooting.md when imports, datasets, CUDA, ports, checkpoints, config paths, logging, Hydra, or trainer selection fails.

Operating rules

• Prefer command construction and config inspection before expensive jobs. Do not start long training/evaluation without explicit user approval, available data, intended checkpoint paths, and suitable backend resources.
• Use DETECTRON2_DATASETS or a user-defined dataset registration before claiming a COCO-style command is runnable.
• Use train.fast_dev_run.enabled=True for a short smoke/debug path; it changes max_iter, eval_period, and log_period inside the trainer.
• Treat DINO, CO-MOT, and similar project-specific trainer files as separate workflow contracts when they change optimizer grouping, data movement, or model-specific assumptions.
• Do not download datasets, checkpoints, or pretrained backbones by default. Use user-provided local paths unless the user explicitly authorizes downloads.
• Keep full native training/evaluation tests out of routine guidance; use dry-run command building, config loading, and explicit preflight checks first.

Quick command builder examples

python scripts/build_train_command.py \
  --config-file user_configs/dab_detr_r50_50ep.py \
  --num-gpus 8 --fast-dev-run

python scripts/build_train_command.py \
  --eval-only --config-file user_configs/dino_r50_4scale_12ep.py \
  --checkpoint weights/dino_r50_4scale_12ep.pth \
  --override train.device=cuda

python scripts/build_train_command.py \
  --launcher hydra --config-file user_configs/detr_r50_300ep.py \
  --num-gpus 8 --auto-output-dir --override model.num_queries=50

The helper prints the command and non-fatal warnings; it does not execute the training loop.