Dali Dynamic Mode

NVIDIAOfficialCreated by DALI TeamDistributed by NVIDIA

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From the author

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

— the description by DALI Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides comprehensive guidance on utilizing the NVIDIA DALI imperative dynamic-mode API (ndd). Unlike traditional pipeline-based DALI, dynamic mode allows for direct, imperative Python calls to DALI operators, enabling more flexible control flow. The documentation covers core data types (Tensor, Batch), stateful reader management, device handling, checkpointing, and migration strategies from pipeline-based DALI to dynamic mode.

What it does

Explains the imperative API structure for direct DALI operator calls.
Details the management of stateful readers across training epochs.
Provides conventions for batch and tensor manipulation, including slicing and framework conversion.
Outlines debugging techniques using synchronous evaluation modes.
Describes the checkpointing mechanism for stateful objects like readers and RNGs.
Offers a mapping table for migrating existing pipeline-mode code to dynamic mode.

When to use it

When you need to integrate DALI operators into standard Python control flow.
When migrating existing DALI pipeline code to a more flexible imperative style.
When you require fine-grained control over data loading and processing state.
When debugging complex DALI operations that require immediate error feedback.

Advantages

Provides greater flexibility compared to static pipeline graphs.
Allows for easier integration with standard Python debugging tools.
Simplifies state management for data readers and random number generation.
Enables direct, imperative manipulation of data batches and tensors.

Usage examples

“How do I convert a DALI dynamic mode batch to a PyTorch tensor?”: Use the .torch() method on the batch object. For ragged batches, use batch.torch(pad=True) to ensure uniform shapes.
“How can I debug an error in my dynamic mode pipeline?”: Wrap your code block in 'with ndd.EvalMode.sync_full:' to force synchronous execution, which surfaces errors at the exact call site.
“How do I handle checkpointing for a DALI reader in dynamic mode?”: Construct the reader with enable_checkpointing=True, then use an ndd.checkpoint.Checkpoint object to register the reader and save/load its state.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/dali-dynamic-mode/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip dali-dynamic-mode.zip -d .agents/skills

Documentation by DALI Team

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.

Batch -- collection of samples (variable shapes OK)

b = ndd.batch([arr1, arr2])    # copy
b = ndd.as_batch(data)         # wrap, no copy if possible

Batch has no __getitem__ -- batch[i] raises TypeError because indexing is ambiguous (sample selection vs. per-sample slicing). Use the explicit APIs instead:

Intent	Method	Returns
Get sample i	`batch.tensors[i]`	`Tensor`
Get subset of samples	`batch.tensors[slice_or_list]`	`Batch`
Slice within each sample	`batch.slice[...]`	`Batch` (same batch_size)
Sample-wise slicing	`batch.slice[batch_of_indices]`	`Batch` (same batch_size)

.tensors[] picks which samples. .slice indexes inside each sample.

xy = ndd.random.uniform(batch_size=16, range=[0, 1], shape=2)
crop_x = xy.slice[0]       # Batch of 16 scalars, first element from each sample
crop_y = xy.slice[1]       # Batch of 16 scalars, second element from each sample
sample_0 = xy.tensors[0]   # Tensor, the entire first sample [x, y]

Advanced slicing

The .slice[] API accepts batches of indices, allowing the user to mix and match batches and scalar values, e.g.:

imgs = ndd.imread(filenames)  # a batch of images, if `filenames` is a list
sliced = imgs.slice[
    42 :  # the range start is broadcast to all samples
    ndd.batch(imgs.shape).slice[0] // 2  # per-sample range stop (half of each image)
]

PyTorch conversion:

batch.torch() -- works for uniform shapes; raises for ragged batches
batch.torch(pad=True) -- zero-pads ragged batches to max shape (use for variable-length audio, detection boxes, etc.)
batch.torch(copy=None) is the default (avoids copy if possible)
Batch has no __dlpack__ -- use ndd.as_tensor(batch) first for DLPack consumers. ndd.as_tensor supports pad as well.
Tensor.torch(copy=False) is default (no copy)

Iteration: for sample in batch: yields Tensors.

Readers

Readers are stateful objects -- create once, reuse across epochs. This matters because readers track internal state like shuffle order and shard position.

reader = ndd.readers.File(file_root=image_dir, random_shuffle=True)

for epoch in range(num_epochs):
    for jpegs, labels in reader.next_epoch(batch_size=64):
        # jpegs, labels are Batch objects
        ...

Key points:

Reader outputs (jpegs, labels, etc.) are CPU tensors/batches. Labels typically stay on CPU until you convert them for your framework (e.g. labels.torch().to(device)).
Reader classes are PascalCase: ndd.readers.File(...), ndd.readers.COCO(...), ndd.readers.TFRecord(...)
batch_size goes to next_epoch(), not to the reader constructor
next_epoch(batch_size=N) yields tuples of Batch; next_epoch() without batch_size yields tuples of Tensor
The iterator from next_epoch() must be fully consumed before calling next_epoch() again
Once a reader is used with a given batch_size, it cannot be changed. Similarly, a reader used in batch mode cannot switch to sample mode or vice versa.

Sharded reading for distributed training:

reader = ndd.readers.File(
    file_root=image_dir,
    shard_id=rank, num_shards=world_size,
    stick_to_shard=True,
    pad_last_batch=True,
)

Device Handling

Device is inferred from inputs -- GPU if any input is on GPU
For hybrid decode: use device="gpu" (NOT "mixed"). The "mixed" keyword is a pipeline-mode concept for implicit CPU-to-GPU transfer; in dynamic mode, passing device="gpu" triggers the same hardware-accelerated decode path.
Don't call .cpu() before passing to a GPU model -- .torch() gives you a GPU tensor directly. .cpu() is only needed for consumers requiring host memory (numpy, __array__).
CUDA stream sync between DALI and PyTorch is automatic via DLPack -- no manual stream management needed.

Execution Model

Default mode is eager -- async execution in a background thread, returns immediately.

No .evaluate() needed in most cases. Any data consumption (.torch(), __dlpack__, __array__, .shape, property access, iteration) triggers evaluation automatically.

For debugging, switch to synchronous mode so errors surface at the exact call site rather than later in the async queue:

with ndd.EvalMode.sync_cpu:
    images = ndd.decoders.image(jpegs, device="gpu")
    images = ndd.resize(images, size=[224, 224])
    # Any error surfaces here, at the exact op that failed

Modes (increasing synchronicity): deferred < eager < sync_cpu < sync_full

Use EvalMode.sync_full for debugging instead of scattering .evaluate() calls -- it's cleaner and catches all issues at once. sync_cpu is often sufficient and lighter than sync_full.

Thread Configuration

ndd.set_num_threads(4)  # Call once at startup, only if necessary to override the defaults

Controls DALI's internal worker threads for CPU operators. Defaults to CPU affinity count or DALI_NUM_THREADS env var. Unrelated to Python-level threading.

RNG

Two approaches (use one, not both):

# Approach 1: set the thread-local default seed (simple, good enough for most cases)
ndd.random.set_seed(42)
angles = ndd.random.uniform(batch_size=64, range=(-30, 30))

# Approach 2: explicit RNG object (finer control, pass rng= to each op)
rng = ndd.random.RNG(seed=42)
values = ndd.random.uniform(batch_size=64, range=[0, 1], shape=2, rng=rng)

When rng= is passed to a random op, the explicit RNG overrides the default seed. Thread-local: each thread has independent random state.

Random ops need an explicit batch_size when working with batches -- there is no pipeline-level batch size to inherit.

Checkpointing

Dynamic mode has no pipeline-level checkpoint. Checkpoints aggregate the state of individual stateful objects: readers and RNG instances. Stateless ops (decoders, resize, rotate, normalize, ...) are not part of a checkpoint.

ckpt = ndd.checkpoint.Checkpoint()
ckpt.register(reader, "my_reader")
ckpt.register(rng, "rng")

# ... iterate for a while ...

ckpt.collect()                       # snapshot the registered objects
ckpt.save("ckpt_{seq:04d}.json")     # writes ckpt_0000.json, ckpt_0001.json, ...

Restoring is the symmetric operation -- build a fresh reader and RNG, then load + register. The loaded state is applied to each object at register time:

reader = ndd.readers.File(file_root=..., enable_checkpointing=True, name="my_reader")
rng = ndd.random.RNG()

ckpt = ndd.checkpoint.Checkpoint()
ckpt.load("ckpt_{seq:04d}.json")     # picks the highest sequence number
ckpt.register(reader, "my_reader")   # state applied here
ckpt.register(rng, "rng")            # ditto

for batch in reader.next_epoch(batch_size=N):
    ...  # produces the next batch after the checkpointed iteration

Key rules:

Readers must opt in. Construct with enable_checkpointing=True. Registering an already-iterated reader without it raises RuntimeError; if the reader has not been iterated yet, register enables it retroactively.
Reader state must be applied before the first next_epoch call. The prefetch thread starts on first iteration and the snapshot queue is locked after that. set_state (or a register from a loaded checkpoint) on an already-iterated reader raises RuntimeError.
enable_checkpointing=True is incompatible with compile=True. Calling reader.next_epoch(..., compile=True) on a checkpointing-enabled reader raises NotImplementedError.
Named registration is safer. Anonymous register(op) uses sequential keys (__op_0, __op_1, ...) so the registration order must match between save and restore. Type tags catch cross-type swaps but not reorders of compatible types. Prefer register(op, name).
ndd.checkpoint.current() returns the Checkpoint bound to the current thread-local EvalContext. It's shared across calls -- call ckpt.clear() if reusing the default context for unrelated runs.
Filename pattern: save/load take a Python format string with a single {seq} placeholder (e.g. "ckpt_{seq:04d}.json"). save picks the next free sequence; load picks the highest matching one on disk.
Format version is strict. deserialize rejects payloads from a different checkpoint format version -- no automatic upgrade.
Not thread-safe. One Checkpoint per thread.

Manual get_state / set_state is also available directly on each Reader and RNG -- the Checkpoint aggregator is built on top of it. Use the manual API only when integrating with an external checkpoint system.

Examples

Image Classification Pipeline

import nvidia.dali.experimental.dynamic as ndd

reader = ndd.readers.File(file_root="/data/imagenet/train", random_shuffle=True)

for epoch in range(num_epochs):
    for jpegs, labels in reader.next_epoch(batch_size=64):
        images = ndd.decoders.image(jpegs, device="gpu")
        images = ndd.resize(images, size=[224, 224])
        images = ndd.crop_mirror_normalize(
            images,
            mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
            std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
        )
        train_step(images.torch(), labels.torch())

Common Mistakes

Wrong	Right	Why
`device="mixed"`	`device="gpu"`	`"mixed"` is pipeline mode only
`batch[i]`	`batch.tensors[i]`	`Batch` has no `__getitem__`
`batch.tensors[0]` for per-sample slicing	`batch.slice[0]`	`.tensors` pick samples; `.slice` slices within each sample
`.evaluate()` after every op	Let consumption trigger eval	`.torch()`, `.shape`, etc. trigger it automatically
`.cpu()` before GPU model	`.torch()` directly	Avoids wasteful D2H + H2D round-trip
Recreate reader each epoch	`reader.next_epoch()`	Readers are stateful -- create once, reuse
`ndd.readers.file(...)`	`ndd.readers.File(...)`	Reader classes are PascalCase
`break` from `next_epoch()` loop	Exhaust iterator or create new reader	Iterator must be fully consumed before next `next_epoch()`
No `batch_size` to random ops	`ndd.random.uniform(batch_size=N, ...)`	No pipeline-level batch size to inherit
`register(reader)` after first `next_epoch` to restore	Register the freshly built reader before the first iteration	Reader state can only be applied before the prefetch thread starts
Restoring into a reader built without `enable_checkpointing=True` after iteration	Pass `enable_checkpointing=True` at construction (or register before first iteration)	Backend doesn't keep snapshots otherwise
Spelling out default argument values	Skip default argument values	Very high Python-side overhead, especially when the argument accepts Tensors/Batches. Skipping arguments uses a fast path, actually passing a sentinel value.

Pipeline Mode Migration

Pipeline Mode	Dynamic Mode
`@pipeline_def` / `pipe.build()` / `pipe.run()`	Direct function calls in a loop
`fn.readers.file(...)`	`ndd.readers.File(...)` (PascalCase, stateful)
`fn.decoders.image(jpegs, device="mixed")`	`ndd.decoders.image(jpegs, device="gpu")`
`fn.op_name(...)`	`ndd.op_name(...)`
Pipeline-level `batch_size=64`	`reader.next_epoch(batch_size=64)` + random ops `batch_size=64`
Pipeline-level `seed=42`	`ndd.random.set_seed(42)` or `ndd.random.RNG(seed=42)`
Pipeline-level `num_threads=4`	`ndd.set_num_threads(4)` at startup
`output.at(i)`	`batch.tensors[i]`
`output.as_cpu()`	`batch.cpu()`
`pipe.run()` returns tuple of `TensorList`	`reader.next_epoch(batch_size=N)` yields tuples of `Batch`
`Pipeline(..., enable_checkpointing=True)` + `pipe.checkpoint()` / `pipeline(checkpoint=...)`	`ndd.checkpoint.Checkpoint` + per-object `register` / `collect` / `save` / `load`; readers opt in with `enable_checkpointing=True`

Limitations

Dynamic mode is more flexible than pipeline mode, but can have slightly worse performance. For maximum throughput, prefer pipeline mode.

Troubleshooting

If errors surface later than the failing call, rerun the block under EvalMode.sync_cpu or EvalMode.sync_full.
If a reader behaves unexpectedly across epochs, check that it is created once and each next_epoch() iterator is fully consumed.

Files

ATTRIBUTION.mdAdded by AutoGineer912 B
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SKILL.md14.6 KiB
agents/openai.yaml116 B
evals/evals.json8.1 KiB
evals/files/pipeline_to_convert.pyScript1.4 KiB
scripts/requirements.txt41 B
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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

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Attribution and licence

Created by

DALI Team

Publisher

NVIDIA (Official)

Distributed by

NVIDIA

Source

NVIDIA/skills · skills/dali-dynamic-mode · commit fce3cca

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LICENSE (added by AutoGineer from LICENSE-APACHE)
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2026-11-09

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