DALI Dynamic Mode

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.

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().

Introduction

Dynamic mode is DALI's imperative Python API. It lets code call DALI operators directly from normal Python control flow instead of building and running a pipeline graph.

Core Data Types

Tensor -- single sample

t = ndd.tensor(data)           # copy
t = ndd.as_tensor(data)        # wrap, no copy if possible
t.cpu()                        # move to CPU
t.gpu()                        # move to GPU
t.torch(copy=False)            # conversion to PyTorch tensor with no copy (default)
t[1:3]                         # slicing supported
np.asarray(t)                  # NumPy via __array__ (CPU only)

Supports __dlpack__, __cuda_array_interface__, __array__, arithmetic operators.