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name: training-and-evaluation description: "Operate FastReID training and evaluation launch commands, DefaultTrainer APIs, distributed options, checkpoints, solver schedules, metrics, logs, and custom training loops." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

FastReID training and evaluation

Use this sub-skill when the task is about running or adapting FastReID training, evaluation-only runs, distributed launch flags, resume/checkpoint behavior, solver/scheduler settings, trainer hooks, evaluation metrics, logs, or custom training-loop extensions.

Route elsewhere first

• Dataset download, directory layout, FASTREID_DATASETS, custom dataset registration, dataloaders, transforms, and samplers: use ../data-and-datasets/.
• Environment setup, source-only import, config merge, _BASE_ inheritance, recipe selection, and general opts syntax: use ../setup-and-configuration/.
• Model registry, feature tensor contracts, DefaultPredictor, and no-download model construction: use ../modeling-and-inference/.
• ONNX, Caffe, TensorRT, deployment runtimes, and project-extension export workflows: use ../deployment-and-projects/.

Bundled references

• references/training-cli.md — use for safe train/eval command templates, parser flags, opts examples, device overrides, 1-GPU to multi-GPU conversion, multi-machine launch, resume, and output conventions.
• references/trainer-api.md — use for DefaultTrainer, launch, solver/scheduler builders, hooks, writers, checkpoint APIs, and custom trainer/loop extension points.
• references/evaluation.md — use for DefaultTrainer.test, ReidEvaluator, inference_on_dataset, rank/mAP/mINP metrics, AQE/rerank/ROC options, and metric-output interpretation.
• references/troubleshooting.md — use for missing datasets, CUDA OOM, batch/world-size divisibility, resume/checkpoint confusion, config.yaml output, Python rank fallback, distributed launch failures, and stale tests or legacy imports.

Bundled scripts

• scripts/train_command_builder.py — build and print a FastReID train or eval-only shell command without executing it; validates eval-only checkpoint requirements and warns about distributed batch divisibility.
• scripts/run_training_entrypoint.py — bundled replacement for the source-tree training launcher; safe by default because it requires --dry-run or --confirm-run.
• scripts/training_cli_help_check.py — import FastReID's standard parser from an explicit repo root and print the available CLI flags without launching training.

Public facts to remember

• FastReID version 1.3 exposes the standard CLI parser through fastreid.engine.default_argument_parser().
• Parser flags include --config-file, --resume, --eval-only, --num-gpus, --num-machines, --machine-rank, --dist-url, and trailing opts (KEY VALUE pairs).
• The standard launcher is launch(main_func, num_gpus_per_machine, num_machines=1, machine_rank=0, dist_url=None, args=()).
• DefaultTrainer(cfg) builds the standard train stack, and DefaultTrainer.test(cfg, model) runs evaluation over cfg.DATASETS.TESTS.
• build_optimizer(cfg, model, contiguous=True) returns optimizer state plus a parameter wrapper, and build_lr_scheduler(cfg, optimizer, iters_per_epoch) returns scheduler objects keyed by scheduler role.
• Full training/evaluation can require datasets, local checkpoints, CUDA, and long runtime; this skill's bundled scripts do not train, evaluate, download, or write destructive outputs.