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name: data-training description: "Route NeuroMANCER data preparation and CPU-first training workflows for dictionary, static, sequence, and graph data, including normalization, splitting, collation, Trainer, Lightning, callbacks, logging, and checkpoints." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Data and training

Use this sub-skill when a NeuroMANCER task starts with tensors/arrays and must produce named batches, sequence windows, graph batches, or a bounded training run. It is written for neuromancer==1.5.6 and a CPU-first workflow.

Route the request

• Dictionary/static samples: use DictDataset or StaticDataset, then a DataLoader with the dataset's collate_fn. See API reference and data formats.
• Time series or multiple trajectories: use SequenceDataset directly or get_sequence_dataloaders; preserve the *p/*f key contract and validate nsteps before constructing windows.
• Node/edge/graph batches: use GraphDataset with precomputed graphs or its local radius-graph builder. Treat graph batching as a separate contract; do not silently substitute ordinary dictionary collation.
• Optimization: choose the base Trainer when explicit PyTorch loaders, callbacks, and a CPU device are desired. Choose LitTrainer when the data_setup_function/Lightning lifecycle, checkpoint callbacks, or custom Lightning hooks are desired. The two APIs are not interchangeable.

Follow workflows for the smallest safe setup. Keep symbolic objectives/constraints in symbolic-problems, model and dynamics construction in dynamics-modeling, and PSL/control rollouts in control-simulation.

Non-negotiable batch contract

1. Every sample-bearing tensor has the same first/sample axis before a dataset is built.
2. Every loader uses the owning dataset's collate_fn; it adds the name field consumed by NeuroMANCER problem outputs.
3. Split raw data before fitting normalization statistics. Apply train statistics to development and test data; do not use the convenience dataloader normalization path when that would leak held-out values.
4. Start with device="cpu" for Trainer or accelerator="cpu" for LitTrainer; CUDA and multi-device behavior is optional and unverified.
5. Keep development data named dev and training data named train when using LitDataModule.

Validate before training

Run the bundled, side-effect-free smoke check from any working directory:

python scripts/data_smoke.py --help
python scripts/data_smoke.py --run

--run creates only tiny in-memory static, dictionary, and sequence fixtures; it performs no training, network access, checkpointing, or writes. For failure recovery and known source-version edge cases, use troubleshooting.