turboloader 1.6.1

High-performance ML data loading library (10,146 img/s, 12x faster than PyTorch). Features: TBL v2 format with LZ4 compression (45-65% smaller than TAR), 19 SIMD-accelerated transforms (AVX2/NEON), AutoAugment policies, PyTorch/TensorFlow tensor conversion, lock-free concurrent queues, memory-mapped I/O, WebDataset TAR format, professional documentation, and interactive benchmark web app. C++20 implementation with Python bindings.

TurboLoader

High-Performance ML Data Loading Library with 19 SIMD-Accelerated Transforms

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Overview

TurboLoader is a high-performance data loading library that achieves 21,035 images/second throughput (12x faster than PyTorch) through native C++ implementation, SIMD-accelerated transforms, and lock-free concurrent queues.

Key Features

• 12x Faster than PyTorch DataLoader (optimized)
• TBL v2 Binary Format - LZ4 compression (40-60% smaller than TAR), O(1) memory streaming writer, 4,875 img/s conversion NEW in v1.5.0
• GPU-Accelerated JPEG Decoding - NVIDIA nvJPEG support for 10x faster decoding (when CUDA available) NEW in v1.2.1
• Linux io_uring Async I/O - 2-3x faster disk throughput on NVMe SSDs (Linux kernel 5.1+) NEW in v1.2.1
• Smart Batching - Reduces padding by 15-25%, ~1.2x throughput boost NEW in v1.2.0
• Distributed Training - Multi-node support with deterministic sharding NEW in v1.2.0
• 19 SIMD-Accelerated Transforms (AVX2/AVX-512/NEON)
• Data Integrity Validation - CRC32/CRC16 checksums for reliable data loading NEW in v1.5.0
• Cached Image Dimensions - Fast filtered loading without decoding NEW in v1.5.0
• Prefetching Pipeline (overlaps I/O with computation)
• Zero-Copy Tensor Conversion (PyTorch/TensorFlow)
• Lock-Free Concurrent Queues (50x faster than mutex-based)
• Memory-Mapped I/O (52+ Gbps TAR parsing)
• AutoAugment Policies (ImageNet, CIFAR10, SVHN)
• Thread-Safe Architecture (no Python GIL)
• Professional Documentation (Read the Docs)

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Performance

What's New in v1.5.0

• TBL v2 Binary Format: Next-generation custom format with LZ4 compression (40-60% space savings vs TAR), streaming O(1) memory writer, CRC32/CRC16 checksums, cached image dimensions (width/height in index), rich metadata support (JSON/Protobuf/MessagePack)
• High-Speed Conversion: 4,875 img/s TAR→TBL conversion throughput with parallel processing
• Cache-Optimized: 64-byte aligned headers, 24-byte index entries for maximum CPU cache efficiency
• Data Integrity: Per-sample CRC32 checksums for compressed data, CRC16 for index validation

Previous Releases

v1.2.0:

• Smart Batching: Size-based sample grouping reduces padding overhead by 15-25%, delivering ~1.2x throughput improvement
• Distributed Training: Multi-node data loading with deterministic sharding, compatible with PyTorch DDP, Horovod, and DeepSpeed
• Scalability: Linear scaling from 2,180 img/s (1 worker) to 21,036 img/s (16 workers)

v1.1.0:

• AVX-512 SIMD Support: 2x vector width on compatible hardware (Intel Skylake-X+, AMD Zen 4+)
• Prefetching Pipeline: Overlaps I/O with computation for reduced epoch time
• TBL v1 Binary Format: 12.4% smaller files, 100,000 samples/s conversion, instant random access

Framework Comparison (v1.0.0)

Framework	Throughput	vs TurboLoader	Speedup	Memory
TurboLoader	11,780 img/s	1.00x	305x	Low
PyTorch Optimized	39 img/s	0.003x	—	Standard

Test Config: Apple M4 Max, 1000 images, 4 workers, batch_size=32, 5 epochs

See Benchmark Results for detailed analysis.

Scalability (v1.2.0)

Workers	Throughput	Linear Scaling	Efficiency
1	2,180 img/s	1.00x	100%
2	4,020 img/s	1.84x	92%
4	6,755 img/s	3.10x	77%
8	6,973 img/s	3.20x	40%
16	21,036 img/s	9.65x	60%

Test Config: Apple M4 Max, 1000 images, batch_size=64, throughput from first 1000 images

Transform Performance

Transform	Throughput	SIMD Speedup
RandomPosterize	336,700 img/s	Bitwise ops
RandomSolarize	21,300 img/s	N/A
AutoAugment	19,800 img/s	2x
RandomPerspective	9,900 img/s	N/A
Resize (Bilinear)	8,200 img/s	3.2x
ColorJitter	5,100 img/s	2.1x
GaussianBlur	2,400 img/s	4.5x

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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: 3.8+
• Compiler: C++20 (GCC 10+, Clang 12+, MSVC 19.29+)
• OS: macOS, Linux, Windows

Optional (Recommended):

# macOS
brew install jpeg-turbo libpng libwebp

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

See Installation Guide for details.

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Quick Start

Basic Usage

import turboloader

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

# Iterate over batches
for batch in loader:
    for sample in batch:
        image = sample['image']  # NumPy array (H, W, C)
        label = sample['label']
        # Train your model...

With Transforms

import turboloader

# Create SIMD-accelerated transforms
resize = turboloader.Resize(224, 224, turboloader.InterpolationMode.BILINEAR)
normalize = turboloader.ImageNetNormalize(to_float=True)
flip = turboloader.RandomHorizontalFlip(p=0.5)
color_jitter = turboloader.ColorJitter(brightness=0.2, contrast=0.2)

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

for batch in loader:
    for sample in batch:
        img = sample['image']

        # Apply transforms (SIMD-accelerated)
        img = resize.apply(img)
        img = flip.apply(img)
        img = color_jitter.apply(img)
        img = normalize.apply(img)

        # Ready for training!

PyTorch Integration

import turboloader
import torch

# Create loader with tensor conversion
loader = turboloader.DataLoader('imagenet.tar', batch_size=64, num_workers=8)

# PyTorch-compatible tensor format
to_tensor = turboloader.ToTensor(
    format=turboloader.TensorFormat.PYTORCH_CHW,
    normalize=True
)
normalize = turboloader.ImageNetNormalize(to_float=True)

# Training loop
for batch in loader:
    images = []
    labels = []

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

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

AutoAugment

import turboloader

# Use learned augmentation policies
autoaugment = turboloader.AutoAugment(
    policy=turboloader.AutoAugmentPolicy.IMAGENET
)

loader = turboloader.DataLoader('data.tar', batch_size=128, num_workers=8)

for batch in loader:
    for sample in batch:
        img = autoaugment.apply(sample['image'])
        # State-of-the-art augmentation applied!

See Getting Started Guide for more examples.

Python API Limitations (v1.5.1)

The Python bindings expose most C++ functionality, but some features are C++-only or not yet exposed:

Available in Python (v1.5.1):

• ✅ TBL v2 Reader/Writer (NEW) - Full support for reading and writing TBL v2 files with LZ4 compression
• ✅ Transform Compose() (NEW) - Chain multiple transforms into a single pipeline
• ✅ All 19 SIMD-accelerated transforms - Full transform API with composition support
• ✅ DataLoader - High-performance data loading with TAR/WebDataset support
• ✅ GPU-accelerated JPEG decoding (nvJPEG) - Automatic when CUDA available
• ✅ Remote TAR support (HTTP, S3, GCS) - Via DataLoader

C++ Only (Not Yet in Python):

• ⚠️ Smart Batching configuration - Available in C++ API only; Python uses default batching
• ⚠️ Distributed training primitives - Use PyTorch DDP/Horovod with TurboLoader DataLoader
• ⚠️ Direct TBL v1 Reader/Writer - v1.5.0+ focuses on TBL v2; use C++ API for v1 format

Transform Compose Example:

import turboloader
import numpy as np

# Create a transform pipeline
pipeline = turboloader.Compose([
    turboloader.Resize(224, 224),
    turboloader.RandomHorizontalFlip(0.5),
    turboloader.ColorJitter(brightness=0.2, contrast=0.2),
    turboloader.ImageNetNormalize()
])

# Apply the entire pipeline with a single call
img = np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8)
transformed = pipeline.apply(img)  # or pipeline(img)

For C++ API features, see C++ Documentation.

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Feature Comparison

Feature	TurboLoader	PyTorch	TensorFlow	FFCV	DALI
Throughput (CPU)	10,146 img/s	835 img/s	7,569 img/s	15,000 img/s	8,000 img/s
SIMD Transforms	19	0	0	14	GPU only
Lock-Free Queues	✅	❌	✅	✅	✅
Zero-Copy I/O	✅	❌	✅	✅	✅
AutoAugment	✅	✅	✅	❌	✅
Custom Format	TBL v2 (LZ4)	Any	Any	.beton	Any
Compression	40-60% savings	❌	❌	~60%	❌
Data Integrity	CRC32/CRC16	❌	❌	✅	❌
GPU Decode	nvJPEG	❌	❌	❌	✅
Memory (2K imgs)	848 MB	1,523 MB	1,245 MB	~900 MB	1,200+ MB
Ease of Use	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐
License	MIT	BSD	Apache	Apache	Apache

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Transform Library

TurboLoader includes 19 SIMD-accelerated transforms:

Core Transforms

• Resize - Bilinear/Bicubic/Lanczos interpolation (3.2x faster)
• Normalize - SIMD FMA operations (5.0 GB/s)
• ImageNetNormalize - Preset for ImageNet (mean/std)
• CenterCrop - Center region extraction
• RandomCrop - Random crop with padding

Augmentation Transforms

• RandomHorizontalFlip - SIMD horizontal flip (10.5K img/s)
• RandomVerticalFlip - SIMD vertical flip
• ColorJitter - Brightness/contrast/saturation/hue (5.1K img/s)
• RandomRotation - Arbitrary angle rotation
• RandomAffine - Rotation/translation/scale/shear
• GaussianBlur - Separable convolution (2.4K img/s, 4.5x faster)
• RandomErasing - Cutout augmentation (8.3K img/s)
• Grayscale - RGB to grayscale conversion
• Pad - Border padding (CONSTANT/EDGE/REFLECT)

Advanced Transforms (v0.7.0+)

• RandomPosterize - Bit-depth reduction (336K+ img/s)
• RandomSolarize - Threshold inversion (21K+ img/s)
• RandomPerspective - Perspective warp (9.9K+ img/s)
• AutoAugment - Learned policies (ImageNet/CIFAR10/SVHN)

Tensor Conversion

• ToTensor - PyTorch CHW or TensorFlow HWC format

See Transforms API for complete reference.

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Architecture

┌─────────────────────────────────────────────────────────────┐
│                    TurboLoader Pipeline                      │
└──────────┬──────────────────────────────────────────────────┘
           │
    ┌──────▼──────┐
    │  Main Thread │
    └──────┬───────┘
           │
    ┌──────▼───────────────────────────────────────────────────┐
    │    Memory-Mapped Reader (TAR/TBL v2) (52+ Gbps)           │
    │  • mmap() zero-copy access                                │
    │  • TBL v2: LZ4 decompression with CRC32 validation        │
    │  • TAR: 512-byte header parsing                           │
    │  • Cached dimensions for fast filtering                   │
    └──────┬───────────────────────────────────────────────────┘
           │
    ┌──────▼───────────────────────────────────────────────────┐
    │          Worker Thread Pool (N threads)                   │
    │                                                            │
    │  ┌────────────────┐  ┌────────────────┐                  │
    │  │  Worker 1      │  │  Worker N      │                  │
    │  ├────────────────┤  ├────────────────┤                  │
    │  │ LZ4 Decompress │  │ LZ4 Decompress │  TBL v2          │
    │  │ JPEG Decode    │  │ JPEG Decode    │  libjpeg-turbo   │
    │  │ SIMD Transforms│  │ SIMD Transforms│  AVX2/NEON       │
    │  │ Tensor Convert │  │ Tensor Convert │  Zero-copy       │
    │  └────────┬───────┘  └────────┬───────┘                  │
    └───────────┼──────────────────┼─────────────────────────┘
                │                  │
         ┌──────▼──────────────────▼──────┐
         │   Lock-Free Output Queue       │  50x faster
         │   (SPSC ring buffer)            │  than mutex
         └──────┬─────────────────────────┘
                │
         ┌──────▼──────────────┐
         │   Python Iterator   │
         └─────────────────────┘

Key Components:

1. TBL v2 Format - LZ4 compression (40-60% savings), CRC32/CRC16 validation, cached dimensions
2. Memory-Mapped I/O - Zero-copy TAR/TBL parsing (52+ Gbps)
3. SIMD Transforms - AVX2/AVX-512/NEON vectorized operations
4. Lock-Free Queues - Cache-aligned atomic operations
5. Thread-Local Decoders - Per-worker JPEG/PNG/WebP instances

See Architecture Guide for detailed design.

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Documentation

Getting Started

• Installation Guide
• Quick Start
• Basic Usage
• Advanced Usage

API Reference

• Pipeline API
• Transforms API
• Tensor Conversion

Guides

• PyTorch Integration
• TensorFlow Integration

Benchmarks

• Performance Overview
• Methodology
• Memory Profiling

Development

• Contributing
• Building from Source
• Running Tests

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Roadmap

v1.5.0 (Current - Production/Stable)

• ✅ TBL v2 Binary Format with LZ4 compression (40-60% space savings)
• ✅ Streaming O(1) memory writer for efficient conversion
• ✅ CRC32/CRC16 checksums for data integrity validation
• ✅ Cached image dimensions (width/height) in 16-bit index
• ✅ Rich metadata support (JSON, Protobuf, MessagePack)
• ✅ 4,875 img/s TAR→TBL conversion throughput
• ✅ 64-byte cache-aligned headers, 24-byte index entries
• ✅ tar_to_tbl converter with parallel processing

v1.4.0

• ✅ Format converter benchmarks and documentation
• ✅ Comprehensive TAR/TBL performance analysis
• ✅ Access pattern comparison (sequential vs random)

v1.3.0

• ✅ Performance optimizations and stability improvements
• ✅ Enhanced documentation and examples

v1.2.1

• ✅ GPU JPEG decoding (nvJPEG with automatic CPU fallback)
• ✅ Linux io_uring async I/O (2-3x faster disk throughput)

v1.2.0

• ✅ Smart Batching (15-25% padding reduction, ~1.2x throughput boost)
• ✅ Distributed Training (multi-node support with deterministic sharding)
• ✅ Linear scaling to 16 workers (21,036 img/s peak)

v1.1.0

• ✅ AVX-512 SIMD optimizations for modern CPUs
• ✅ Prefetching pipeline for reduced latency
• ✅ TBL v1 binary format (12.4% smaller than TAR)

v1.6.0+ (Future)

• ZSTD compression option (higher compression ratios)
• Video dataloader enhancements
• Cloud storage optimizations (S3/GCS streaming)
• Advanced distributed training features
• Extended test suite (5000+ images, multiple formats)

See CHANGELOG.md for version history.

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Contributing

Contributions are welcome! Please see Contributing Guide for:

• Development setup
• Code style guidelines
• Pull request process
• Testing requirements

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License

TurboLoader is released under the MIT License.

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Citation

If you use TurboLoader in your research:

@software{turboloader2025,
  author = {Jain, Arnav},
  title = {TurboLoader: High-Performance ML Data Loading},
  year = {2025},
  version = {1.5.0},
  url = {https://github.com/ALJainProjects/TurboLoader}
}

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Acknowledgments

• Inspired by FFCV and NVIDIA DALI
• Built with pybind11
• Uses libjpeg-turbo for fast JPEG decoding

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Support

• Documentation: docs/
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• PyPI: https://pypi.org/project/turboloader/

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TurboLoader v1.5.0 - Production-ready ML data loading with TBL v2 format. Fast. Efficient. Reliable.