dali-dynamic-mode

NVIDIA DALI dynamic mode replaces static pipeline graphs with an interactive Python API, letting you write and execute GPU data loading and preprocessing operations directly without building a separate pipeline graph.

Migrate existing pipeline-based code to dynamic mode by importing NVIDIA DALI dynamic as ndd, using PascalCase class names like ndd.readers.File, and replacing pipeline-def patterns with direct Python loops.

Yes, DALI dynamic mode supports stateful readers for training, validation, and inference workflows using next_epoch(batch_size=...) to preserve deterministic behavior and stateful reader capabilities.

Yes, you can integrate dynamic mode with common ML frameworks like PyTorch while managing GPU-accelerated image and audio decoding, randomization, batching, and augmentations with explicit device='gpu' handling.

Dynamic mode allows iterative testing and execution of DALI operations directly in Python loops, unlike static pipelines that require defining and running a separate pipeline graph before executing data preprocessing operations.

Switch to dynamic mode when you need to write, test, and migrate data-loading and preprocessing code iteratively, avoiding pipeline-def patterns in favor of imperative operations with explicit device handling.