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

```python
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)

```python
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**.

```python
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.:
```python
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.

```python
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:
```python
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 executio