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name: pytorch-training-evaluation-inference description: "PyTorch DeepLabCut train, evaluate, image/video inference, labeled-video handoff, and export routing." disable-model-invocation: true metadata: disco-role: operating license: LGPL 3.0

PyTorch Training, Evaluation, Inference

Use this sub-skill when the task is inside the DeepLabCut 3.x PyTorch engine and you need to:

• train or resume a shuffle
• evaluate one or more snapshots
• run image or video inference
• choose device, snapshot, batch-size, and config overrides
• hand off analyzed outputs for labeled-video rendering
• export a relocatable PyTorch model bundle

This sub-skill owns the PyTorch-specific choices for device, snapshot selection, detector handling, batch_size, pytorch_cfg_updates, inference_cfg, and export packaging.

Stay here when

• the project or shuffle is already built and you only need training, evaluation, analysis, or export
• you are choosing between cpu, cuda, mps, or auto
• you need to resume from a pose snapshot or detector snapshot
• you want to inspect or override a PyTorch config without editing the full file by hand

Route away when

• upstream label extraction or training-dataset creation is needed → data-labeling-and-training-datasets
• multi-animal tracklet stitching or identity recovery is needed after analysis → multi-animal-tracking
• SuperAnimal pretrained, adaptation, or model-zoo workflows are needed → model-zoo-superanimal
• filtered predictions, labeled-video rendering details, or 3D export are needed → postprocessing-3d-video-exports
• TensorFlow engine details are needed → route to the root compatibility layer

Bundled guidance

• API reference
• PyTorch configuration guide
• Workflow guide
• Troubleshooting guide
• Config inspector

When you only need to inspect a pytorch_config file, use the bundled script. It summarizes the key sections and never trains.