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.

Prerequisites

• To run or validate code, NVIDIA DALI must be installed with dynamic mode importable as nvidia.dali.experimental.dynamic.
• GPU decode or GPU operators require a CUDA-capable DALI build and an available NVIDIA GPU/driver.
• Framework conversion examples require the target framework installed, such as PyTorch for .torch().