TurboLoader

Production-Ready ML Data Loading Library

PyPI version Tests Python 3.10+ C++20 License: MIT

Overview

TurboLoader is a high-performance data loading library for machine learning workflows. Built with C++20 and featuring Python bindings, it provides efficient data loading with SIMD-accelerated transforms, custom binary formats, and distributed training support.

Core Features

  • Fused train pipeline - train_aug=True: RandomResizedCrop + flip + normalize inside the C++ pass (torchvision-parity, deterministic per epoch, ~3% over plain loading)
  • Trains end-to-end faster - real ResNet-18/Imagenette: 1.17x vs PyTorch DataLoader (loader ~fully hidden behind the GPU); see benchmarks/E2E_TRAINING_RESULTS.md
  • Checkpointable - state_dict()/load_state_dict(): exact, decode-free mid-epoch resumption
  • Pinned recycled buffers - pin_memory=True yields torch tensors from a reused ring; async H2D with non_blocking=True
  • Decoded Tensor Caching - FastDataLoader(..., cache_decoded=True) keeps decoded arrays in RAM so later epochs skip decoding
  • Multiple Loader Types - FastDataLoader, MemoryEfficientDataLoader, standard DataLoader
  • Distributed Training Support - Multi-node data loading with deterministic sharding
  • SIMD-Accelerated Transforms - 19 vectorized transforms using AVX2/AVX-512/NEON
  • TBL v2 Binary Format - Custom format with LZ4 compression for reduced storage
  • Framework Integration - Seamless support for PyTorch, TensorFlow, and JAX
  • Memory-Mapped I/O - Zero-copy file access for improved throughput
  • Lock-Free Queues - Concurrent data structures for efficient multi-threading
  • GPU image loaders - CudaImageLoader (NVIDIA nvImageCodec — beats DALI on an RTX 3090, see below) and GpuImageLoader (Apple Metal): end-to-end GPU decode + resize + normalize, GPU-resident output. See GPU acceleration

Installation

From PyPI (Recommended)

pip install turboloader

From Source

git clone https://github.com/ALJainProjects/TurboLoader.git
cd TurboLoader
pip install -e .

System Requirements

  • Python: 3.10 or higher
  • Compiler: C++20 capable (GCC 10+, Clang 12+, MSVC 19.29+)
  • OS: Linux (x86_64/aarch64 wheels), macOS (arm64 wheel). Windows: not officially supported yet — use WSL2

Optional Dependencies

Install for enhanced performance:

# macOS
brew install jpeg-turbo libpng libwebp lz4

# Ubuntu/Debian
sudo apt-get install libjpeg-turbo8-dev libpng-dev libwebp-dev liblz4-dev

Quick Start

Basic Usage

import turboloader

# Create DataLoader
loader = turboloader.DataLoader(
    'imagenet.tar',
    batch_size=128,
    num_workers=8
)

# Iterate over batches. Each sample is a dict:
#   {'image': np.ndarray (H, W, C), 'filename': str, 'index': int,
#    'width': int, 'height': int, 'channels': int}
for batch in loader:
    for sample in batch:
        image = sample['image']      # NumPy array (H, W, C)
        name = sample['filename']    # source path within the archive
        # Train your model...

Need (image, label) tuples like torch.utils.data.DataLoader? Use PyTorchCompatibleLoader, which derives labels from the folder structure (ImageFolder-style). The base DataLoader does not attach labels.

With Transforms

import turboloader

# Create transforms
resize = turboloader.Resize(224, 224)
normalize = turboloader.ImageNetNormalize()
flip = turboloader.RandomHorizontalFlip(p=0.5)

# Apply transforms
loader = turboloader.DataLoader('data.tar', batch_size=64, num_workers=8)

for batch in loader:
    for sample in batch:
        img = sample['image']
        img = resize.apply(img)
        img = flip.apply(img)
        img = normalize.apply(img)
        # Ready for training

PyTorch Integration

import turboloader
import torch

loader = turboloader.DataLoader('imagenet.tar', batch_size=64, num_workers=8)

# Convert to PyTorch tensors
to_tensor = turboloader.ToTensor(
    format=turboloader.TensorFormat.PYTORCH_CHW
)

for batch in loader:
    images = []
    for sample in batch:
        img = to_tensor.apply(sample['image'])
        images.append(torch.from_numpy(img))

    batch_tensor = torch.stack(images)
    # Train model...

Distributed Training

import turboloader
import torch.distributed as dist

# Initialize distributed training
dist.init_process_group(backend='nccl')

# Create loader with distributed support
loader = turboloader.DataLoader(
    data_path="/data/imagenet.tar",
    batch_size=64,
    num_workers=4,
    shuffle=True,
    enable_distributed=True,
    world_rank=dist.get_rank(),
    world_size=dist.get_world_size(),
    drop_last=True
)

# Each rank automatically gets its shard
for batch in loader:
    # Your training code
    pass

Transform Library

TurboLoader includes 24 transforms (19 per-image SIMD transforms + 5 batch augmentations). The authoritative list is turboloader.list_transforms().

Core Transforms

  • Resize - Bilinear/Bicubic/Lanczos interpolation
  • Normalize - Mean/std normalization with SIMD
  • CenterCrop - Center region extraction
  • RandomCrop - Random crop with padding

Augmentation Transforms

  • RandomHorizontalFlip - SIMD horizontal flip
  • RandomVerticalFlip - SIMD vertical flip
  • ColorJitter - Brightness/contrast/saturation/hue
  • RandomRotation - Arbitrary angle rotation
  • GaussianBlur - Separable convolution
  • RandomErasing - Cutout augmentation
  • Pad - Border padding (CONSTANT/EDGE/REFLECT)

Advanced Transforms

  • RandomPosterize - Bit-depth reduction
  • RandomSolarize - Threshold inversion
  • RandomPerspective - Perspective warp
  • AutoAugment - Learned policies (ImageNet/CIFAR10/SVHN)

Batch Augmentations

  • MixUp, CutMix, Mosaic, RandAugment, GridMask

Tensor Conversion

  • ToTensor - PyTorch CHW or TensorFlow HWC format

TBL v2 Binary Format

TurboLoader includes a custom binary format optimized for ML workloads:

Features

  • LZ4 compression for reduced storage
  • Memory-mapped access for fast loading
  • O(1) random access via indexed structure
  • Data integrity validation with CRC checksums
  • Cached image dimensions for filtered loading

Convert TAR to TBL

import tarfile
import turboloader

writer = turboloader.TblWriterV2("/data/imagenet.tbl", enable_compression=True)

# The TAR archive is read with Python's stdlib (TurboLoader does not expose a
# standalone Python TarReader; the DataLoader reads TAR directly for training).
with tarfile.open("/data/imagenet.tar") as tar:
    for member in tar.getmembers():
        if not member.name.lower().endswith((".jpg", ".jpeg")):
            continue
        data = tar.extractfile(member).read()
        writer.add_sample(data=data, format=turboloader.SampleFormat.JPEG)

writer.finalize()

For bulk conversion there is also a C++ CLI tool, tools/tar_to_tbl_v2.cpp.

Documentation

Getting Started

API Documentation

Framework Integration

Examples

Benchmarks

Measured on Apple Silicon over Imagenette-160 (9,469 real ImageNet JPEGs → resize 160×160 → ImageNet-normalize → batched CHW float32, batch 64). To control for thermal throttling, every loader is built once, warmed up one epoch, then timed over 5 interleaved rounds (each loader runs once per round); the table reports the median. Output is verified correct against torchvision (mean abs diff ≈ 0.04, bilinear antialiasing only).

Image — on-the-fly decode (re-decode every epoch; for datasets too large to cache or with per-epoch random augmentation):

Loader img/s (median) vs tf.data
TurboLoader DataLoader (output_format='pytorch', nw=6) ~55,000 2.0×
TensorFlow tf.data (AUTOTUNE) ~27,300 1.00×
PyTorch DataLoader (PIL, 8 persistent workers) ~20,500 0.75×

Image — cached (decoded tensors held in RAM; both sides consume identically via np.sum, i.e. delivered as numpy/torch-ready batches — the PyTorch use case):

Loader img/s (median) vs tf.data.cache
TurboLoader (cache_decoded=True, prefetch) ~67,000 1.9×
TensorFlow tf.data.cache() (+ .numpy() materialize) ~35,100 1.00×

(For TF-native consumption that stays in tf tensors, tf.data.cache() is faster — TurboLoader's cache win is for delivering numpy/torch batches.)

LLM tokens (real text, 55M-token memory-mapped corpus, seq_len=1024, next-token):

Loader sequences/s (median)
TurboLoader TokenDataLoader ~467,000
numpy memmap idiom (nanoGPT get_batch) ~251,000

Transforms (per-image throughput vs torchvision): Resize 2.7×, ImageNetNormalize 3.3×, HFlip ~1.0×. For CenterCrop, torchvision returns a lazy strided view (moves zero bytes); compared against TurboLoader's real contiguous crop that looks like 0.45×, but when torchvision actually materializes the crop (.contiguous(), required before batching/most ops) it drops to ~23k img/s and TurboLoader's contiguous crop is ~6.8× faster (155k vs 23k). Like the cache, this is a lazy-vs-eager comparison; for the realistic crop→batch path TurboLoader wins.

Earlier drafts quoted single-run figures (~42k, "1.4×") and a "cached epoch" in the tens-of-millions img/s. Those were artifacts (thermal noise; a no-op loop over aliased cached arrays) and were replaced with the interleaved, identical-consumption medians above. Numbers are hardware-dependent — run benchmarks/ yourself.

The fast path runs decode + resize + normalize + batch assembly in C++ across a thread pool with zero Python per-sample work. Use it like this:

loader = turboloader.DataLoader(
    'imagenet.tar', batch_size=64, num_workers=6,
    output_format='pytorch',          # (N, C, H, W) float32 array per batch
    image_size=160,                   # exact resize, done in C++
    transform=turboloader.ImageNetNormalize())
for epoch in range(epochs):           # re-iterable
    for images, meta in loader:       # images.shape == (64, 3, 160, 160)
        train_step(images)

Honest caveats:

  • Run it yourself (benchmarks/) — results depend heavily on hardware, image size, and pipeline; Linux fork-based PyTorch workers shift the PyTorch numbers a lot.
  • Decode backend differs: TurboLoader uses libjpeg-turbo; the PyTorch baseline uses PIL.
  • The output_format='dict' path returns per-sample dicts and stacks in Python (GIL-bound), so it is much slower — use it only when you need per-sample metadata.

For large source images, the default path also wins: on 768×768 JPEGs resized to 160 it runs ~15,000 img/s — faster than even an expertly-tuned tf.data pipeline using manual decode_jpeg(ratio=...) (~14,400) — because it picks the libjpeg-turbo DCT scaled-decode factor automatically (you don't have to know to set ratio).

GPU loaders (NVIDIA & Apple)

On NVIDIA, CudaImageLoader(decode="nvimgcodec") runs the whole decode + resize + normalize

  • batch in GIL-released C++ via nvImageCodec (the codec DALI uses), with K independent decode slots overlapping batches (multi-batch-in-flight). Among on-the-fly loaders (read a JPEG folder, decode+resize every epoch) on an RTX 3090 (Imagenette-160, batch 64, real consumption, interleaved rounds to control for ~40% host drift):
On-the-fly loader vs TurboLoader
TurboLoader decode="nvimgcodec", nvimgcodec_slots=3 1.0× (fastest)
NVIDIA DALI (num_threads=8, best-tuned) ~0.9× (TurboLoader +12% cleanest run)
PyTorch DataLoader (PIL, CPU) ~0.25×

TurboLoader beats DALI (median above DALI's max in the cleanest run), output bijectively verified correct. For on-the-fly loading FFCV is faster (~2.6–5.9×) — but it requires an offline conversion to its .beton format.

Pre-processed loaders (decode+resize once, like FFCV's .beton) — here TurboLoader turns the tables:

Pre-processed loader img/s
TurboLoader CudaResidentLoader (fits-in-VRAM: upload uint8 once, GPU-resident) ~280,000 beats FFCV ~3.5×
TurboLoader CudaStreamLoader (streaming, dataset > VRAM; fully-C++ loop) ~140,000 beats FFCV ~1.6×
FFCV, raw .beton (streams mmap→H2D each epoch, worker processes) ~85,000

CudaResidentLoader uses a custom single-launch normalize kernel + fused gather (shuffles at ~257k) and beats FFCV ~3.5× when the pre-processed uint8 dataset fits in VRAM (very common: fine-tuning, per-GPU shards, small/medium sets). For datasets larger than VRAM, CudaStreamLoader runs the whole iteration GIL-free in C++ (CudaStreamCore: worker pool + async H2D on non-blocking streams + prefetch) and beats FFCV's streaming ~1.6× (~140k vs ~85k, near the PCIe transfer ceiling). So TurboLoader beats DALI on-the-fly and FFCV on pre-processed data — both fits-in-VRAM and streaming. On Apple Silicon, GpuImageLoader offloads resize+normalize (and a hybrid GPU JPEG decode) to Metal — where neither DALI nor FFCV runs at all. CUDA is a build-from-source path (not in the PyPI wheels); see GPU acceleration for flags, usage, and the full write-up (experiments/cuda/RESULTS.md).

Implementation notes

  • Direct-batch path (src/pipeline/direct_batch_loader.hpp): the default fast path is FFCV/tf.data-style — a persistent thread pool reads JPEG bytes by index and decodes → resizes → normalizes directly into the output batch buffer in one parallel pass (no worker queue, no per-sample heap copy, no serial collection). Verified memory-safe and race-free (disjoint slot writes, const mmap reads, atomic cursor, per-thread decoders).
  • Automatic DCT scaled decode: large JPEGs are decoded at the nearest libjpeg-turbo scale ≥ target, then finely resized — much faster than full-decode + resize.
  • Resize convention: half-pixel centers (align_corners=False), matching PIL/OpenCV/PyTorch/TF (agrees with torchvision plain bilinear to ~0.4/255; the only remaining difference vs torchvision's default is its antialiasing low-pass filter).
  • SIMD transforms (AVX2/AVX-512/NEON), libjpeg-turbo decode, lock-free SPSC queues (legacy/dict + remote path), persistent std::thread pool (src/core/parallel_for.hpp).
  • The GIL is released during C++ processing.
  • OpenMP is opt-in (TURBOLOADER_ENABLE_OPENMP=1); off by default because linking a second OpenMP runtime crashes alongside PyTorch on macOS — the thread pool replaces it.

Beyond Images: Tokens & Arrays

TurboLoader also ships loaders for non-image modalities with the same ergonomics (re-iterable, shuffle, set_epoch, batched arrays):

# LLM pretraining: memory-mapped token stream -> (B, seq_len) next-token batches
loader = turboloader.TokenDataLoader('train.bin', seq_len=1024, batch_size=8,
                                     dtype='uint16', shuffle=True)
for x, y in loader:          # x, y: (8, 1024) int64; y is x shifted by one
    loss = model(x, y)

# Generic arrays/memmaps (embeddings, tabular features, labels, pre-tokenized data)
loader = turboloader.ArrayDataLoader(features, labels, batch_size=256, shuffle=True)
for xb, yb in loader:
    ...

TokenDataLoader uses a vectorized fancy-index gather over a np.memmap (so multi-GB corpora stream without loading into RAM) and benchmarks ~1.9× the standard nanoGPT get_batch idiom. The image pipeline (decode/transform/TBL) remains C++; these modality loaders are NumPy-based and modality-agnostic.

All three modalities are also reachable from the single DataLoader entry point:

turboloader.DataLoader('train.bin', modality='tokens', seq_len=1024, batch_size=8)
turboloader.DataLoader(arrays=[feats, labels], data_path=None, modality='array', batch_size=256)
turboloader.DataLoader('data.tar', image_size=160, output_format='pytorch')   # modality='image' (default)

Wrap any Python dataset (MapDataLoader)

When your data doesn't fit the native paths, MapDataLoader batches any map-style dataset — anything with __len__ and __getitem__(i), i.e. exactly the torch.utils.data.Dataset protocol — so your loading/decoding/business logic can be arbitrary Python:

class MyDataset:
    def __len__(self): return len(self.records)
    def __getitem__(self, i):
        x = decode_however_you_like(self.records[i])   # any Python logic
        return x, self.labels[i]                       # (features, label)

# directly, or via the unified entry point with dataset=...
for xb, yb in turboloader.MapDataLoader(MyDataset(), batch_size=64, shuffle=True, num_workers=8):
    train_step(xb, yb)

It parallelizes __getitem__ on a bounded thread pool with read-ahead and collates (tuples/dicts/arrays, or a custom collate_fn). Honest tradeoff: because the per-sample work runs in Python, this path is roughly PyTorch-DataLoader speed (and GIL-bound for pure-Python CPU work — threads help most when __getitem__ releases the GIL, e.g. NumPy/PIL/file/network I/O). It's about flexibility, not the C++ fast path — use the image/token/array loaders above when you want maximum throughput.

Architecture

TurboLoader uses a multi-threaded pipeline architecture:

┌─────────────────────────────────────────────┐
│           Memory-Mapped Reader              │
│     (TAR/TBL v2 with zero-copy access)      │
└──────────────┬──────────────────────────────┘
               │
        ┌──────▼──────┐
        │Worker Pool  │
        │  (N threads)│
        ├─────────────┤
        │ Decode      │
        │ Transform   │
        │ Convert     │
        └──────┬──────┘
               │
        ┌──────▼──────────────┐
        │ Lock-Free Queue     │
        └──────┬──────────────┘
               │
        ┌──────▼──────┐
        │Python API   │
        └─────────────┘

Key Components

  • Memory-Mapped I/O - Zero-copy file access
  • Worker Thread Pool - Parallel processing with per-thread decoders
  • SIMD Transforms - Vectorized operations (AVX2/AVX-512/NEON)
  • Lock-Free Queues - High-performance concurrent data structures

License

TurboLoader is released under the MIT License.

Citation

If you use TurboLoader in your research:

@software{turboloader,
  author = {Jain, Arnav},
  title = {TurboLoader: High-Performance ML Data Loading},
  year = {2026},
  version = {2.25.0},
  url = {https://github.com/ALJainProjects/TurboLoader}
}

Support

TurboLoader - High-performance ML data loading with a C++20 core and SIMD transforms.

Project links

Data verified by PyPI on Jul 9, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

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  • Released:
Newer release available (2.36.0)

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Avatar for Ajain0422 from gravatar.com Ajain0422

Credits

Author/Maintainer: Arnav Jain

GitHub Statistics

Data verified by PyPI on Jul 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.10

Provides Extra

torch dev benchmarks all

Tags

machine-learning deep-learning data-loading pytorch performance simd imagenet computer-vision

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