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name: training description: "Routes PyTorch Metric Learning questions about trainers, hooks, logging, checkpointing, and optional distributed wrappers." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training

Use this sub-skill when the user wants to wire PyTorch Metric Learning into an actual training loop, validation hook, or checkpoint/logging workflow.

Typical triggers

• "How do I use MetricLossOnly / TrainWithClassifier / CascadedEmbeddings / DeepAdversarialMetricLearning / TwoStreamMetricLoss?"
• "How do I configure the trainer dictionary keys?"
• "How do I add logging, early stopping, or checkpoint saving?"
• "How do I freeze part of the model or use MPerClassSampler during training?"
• "How do I wrap a loss or miner for distributed training?"

In scope

• Trainers: BaseTrainer, MetricLossOnly, TrainWithClassifier, CascadedEmbeddings, DeepAdversarialMetricLearning, TwoStreamMetricLoss.
• Hooking and logging: logging_presets.HookContainer, record-keeper integration, model saving/loading, early stopping, validation hooks, and metric history access.
• Trainer configuration: models, optimizers, loss_funcs, mining_funcs, sampler, batch_size, iterations_per_epoch, freeze_these, freeze_trunk_batchnorm, label_hierarchy_level, data_and_label_getter, dataset_labels, set_min_label_to_zero, lr_schedulers, and gradient_clippers.
• Optional distributed wrappers: DistributedLossWrapper and DistributedMinerWrapper.

Out of scope

• Choosing the loss/miner/distance/reducer/regularizer stack belongs in components.
• Accuracy calculators, testers, and nearest-neighbor search belong in evaluation.
• Downloading datasets or building samplers belongs in data unless the question is purely about their role inside a trainer.

How to use this sub-skill

1. Read references/training-workflows.md for the trainer map, hook sequence, and config expectations.
2. Run scripts/smoke_training.py when you want to confirm the trainer wiring on toy data without downloads.
3. Read references/troubleshooting.md when the failure mentions missing dictionary keys, frozen modules, checkpointing, or logging dependencies.
4. If the request mentions distributed loss/miner wrappers, read the optional distributed note in the workflow reference and treat GPU execution as optional unless the user specifically asks for it.

Common routing decisions

• If the user only needs the right loss or miner, route to components first.
• If the user wants retrieval metrics, validation scores, or nearest-neighbor search after training, route to evaluation after the trainer is set up.
• If the user wants dataset download or batch construction help, route to data before wiring the trainer.

Useful public facts

• All trainer subclasses share the BaseTrainer argument pattern.
• MetricLossOnly needs a trunk model and a metric_loss entry.
• TrainWithClassifier expects a trunk/classifier architecture and both metric and classification losses.
• CascadedEmbeddings splits the embedding dimension into multiple stages.
• DeepAdversarialMetricLearning adds generator-specific loss keys and epoch scheduling.
• TwoStreamMetricLoss expects a dataset that yields (anchor, positive, label).
• HookContainer is the easiest path for record-keeper, tensorboard, early stopping, and model checkpointing.

Read next

• references/training-workflows.md for trainer and hook details.
• references/troubleshooting.md for trainer-key, checkpoint, logging, and distributed-wrapper failures.
• scripts/smoke_training.py for a tiny end-to-end trainer smoke check.