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dali-dynamic-mode

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DALI imperative dynamic mode (`nvidia.dali.experimental.dynamic`, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.

skills/dali-dynamic-mode/SKILL.md

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# DALI Dynamic Mode

## Purpose

Guide AI agents in writing, reviewing, and migrating code that uses DALI's imperative dynamic-mode API, `nvidia.dali.experimental.dynamic` (`ndd`).

## Instructions

- Import dynamic mode as `nvidia.dali.experimental.dynamic as ndd` and write code as direct `ndd` calls in ordinary Python; do not use pipeline-mode APIs such as `Pipeline`, `@pipeline_def`, `pipe.build()`, or `pipe.run()`.
- Treat readers as stateful: create them once, reuse them across epochs, and pass `batch_size` to `next_epoch(...)`.
- Pass explicit `batch_size` to random ops; there is no pipeline-level batch size to inherit.
- Use dynamic-mode API conventions: `device="gpu"` instead of pipeline-mode `"mixed"`, `Batch.tensors[...]` for sample selection, and `Batch.slice[...]` for per-sample slicing.
- Use `.torch()` to convert a tensor or batch to a PyTorch tensor. Use `pad=True` for batches with variable shapes.