TurboLoader

High-performance ML data loading — a C++20 core with SIMD transforms, GPU kernels, and one pip install.

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

TurboLoader demo: pip install, then ~60k img/s on real ImageNet JPEGs

Real recording (tape, script): 9,469 real ImageNet JPEGs decoded → RandomResizedCrop+flip → resized → normalized, ~60k img/s per epoch on an M4 Max laptop.

How it works

The fast path does everything in one fused, GIL-released C++ pass — no worker processes, no per-sample Python, no offline format conversion:

flowchart LR
    A["TAR of JPEGs<br/>(local / http / s3 / gs)"] --> B["persistent C++<br/>thread pool"]
    B --> C["decode → augment → resize → normalize<br/>SIMD (NEON / AVX2 / AVX-512), fused"]
    C --> D[("contiguous batch<br/>N×3×H×W float32")]
    D --> E["your training step<br/>(zero-copy to torch)"]
  • Fast on CPU: ~55k img/s on-the-fly (2.0× tf.data, 2.7× PyTorch DataLoader); trains a real ResNet-18 1.05–1.17× faster end-to-end (run-dependent), ~9% above the pure-GPU floor
  • Fast on GPU: beats NVIDIA DALI on-the-fly (+12%, RTX 3090) and FFCV on pre-processed data (1.6–3.5×); ~757k img/s resident on Apple unified memory
  • Pre-processed pipeline (TBL-RAW): decode once, mmap-serve every epoch — 531k img/s raw serve on CPU, beats the float32 RAM cache at every consumption level on ~1/9th the peak RSS, bit-identical batches, any hardware; fastest e2e input pipeline we've measured (3.64s epochs vs 3.76 TAR / 3.92 PyTorch, floor 3.39)
  • Video: hardware decode to training batches — 3.9× the best industry standard on Apple Silicon; CUDA VideoDatasetLoader trains a real video classifier 1.16× faster than the PyTorch+PyAV recipe (first e2e video benchmark)
  • Train-ready: fused train_aug (torchvision-parity RandomResizedCrop+flip), state_dict() mid-epoch resume, pinned-memory rings, DDP sharding
  • Also tokens & arrays: memory-mapped TokenDataLoader (1.9× nanoGPT get_batch to-device, zero-alloc pinned ring, device='cuda' overlapped H2D), ArrayDataLoader, and MapDataLoader for any __getitem__ dataset
  • Every number is honest: interleaved medians, real consumption, corrections published — full methodology

Which loader do I use?

flowchart TD
    S{{"What are you loading?"}}
    S --> IMG["🖼 Images<br/>(TAR of JPEGs)"]
    S --> VID["🎬 Video files"]
    S --> TOK["🔤 LLM tokens"]
    S --> ARR["📊 Arrays / tabular"]
    S --> ANY["🐍 Anything with<br/>__getitem__"]

    IMG --> Q0{"Many epochs,<br/>resize+flip recipe OK?"}
    Q0 -- "no (full random aug)" --> Q2{"Where to decode?"}
    Q0 -- yes --> Q1{"Fits in GPU /<br/>unified memory?"}
    Q1 -- yes --> RES["CudaResidentLoader · NVIDIA<br/>MetalResidentLoader · Apple<br/>(both ingest .tbl)"]
    Q1 -- no --> TBL["preprocess_to_tbl once →<br/>DataLoader('data.tbl') · mmap"]
    Q2 -- "CPU fast path (default)" --> DL["DataLoader(output_format='pytorch',<br/>image_size=N)"]
    Q2 -- "NVIDIA GPU" --> CIL["CudaImageLoader(decode='nvimgcodec',<br/>return_indices=True)"]
    VID --> QV{"Training on a labeled<br/>video dataset?"}
    QV -- yes --> VDS["VideoDatasetLoader · NVIDIA<br/>(dir of class folders → clips)"]
    QV -- "stream one file" --> MV["MetalVideoLoader · Apple<br/>CudaVideoLoader · NVIDIA"]
    TOK --> TDL["TokenDataLoader<br/>(device='cuda' for GPU batches)"]
    ARR --> ADL["ArrayDataLoader<br/>MetalResidentArrays (GPU gathers)"]
    ANY --> MAP["MapDataLoader"]

    style DL stroke-width:3px
Full decision table + lifetime rules
You have Use Notes
A TAR of JPEGs, training on any hardware DataLoader(..., output_format='pytorch', image_size=N) The default fast path — auto-fused C++ decode+resize+normalize. Start here.
The same, need per-sample dicts (inspection, irregular data) DataLoader(...) (default output_format='dict') Several times slower; not for training loops.
Labels derive from meta['indices'] / sample['filename'] Samples carry no label key; align an external label array by index.
A dataset that fits in GPU/unified memory, many epochs CudaResidentLoader (NVIDIA) / MetalResidentLoader (Apple) Decode once, ~280k / 433–757k img/s per epoch. return_indices=True for labels. Both ingest .tbl.
Many epochs, fixed resize(+hflip) recipe, any hardware preprocess_to_tbl once → DataLoader('data.tbl') mmap serve, zero decode, ~zero owned RAM; bit-identical to the TAR pipeline. No random crop — bake it or use the TAR path.
A pre-processed dataset larger than VRAM (NVIDIA) CudaStreamLoader Fully-C++ streaming, ~140k img/s.
On-the-fly GPU decode (NVIDIA) CudaImageLoader(decode='nvimgcodec', return_indices=True) Beats DALI; batches complete OUT of order — align labels via the returned indices.
On-the-fly GPU transforms (Apple) MetalImageLoader (alias of GpuImageLoader) Metal decode+transforms.
Video files (stream one) MetalVideoLoader (Apple) / CudaVideoLoader (NVIDIA) Hardware decode → training batches; iter_clips() for augmented clips.
Labeled video dataset, training (NVIDIA) VideoDatasetLoader(root_dir) root/class_x/*.mp4 → (clips, labels, meta) CUDA batches; threaded decode + ONE fused kernel per clip.
LLM token streams (memmap) TokenDataLoader CPU memmap is already optimal (measured); device='cuda' yields ready GPU batches (pinned ring, overlapped H2D).
Arrays / embeddings / tabular ArrayDataLoader; MetalResidentArrays for GPU row gathers
WebDataset-style TARs WebDatasetLoader

Two lifetime rules: (1) loaders yielding zero-copy views (pin_memory=True ring, Metal/CUDA resident + video loaders) reuse their buffers — consume or copy a batch before advancing past the documented window; (2) GPU loaders yield __cuda_array_interface__ objects — adopt with torch.as_tensor(x, device='cuda').

Installation

pip install turboloader            # Linux x86_64/aarch64 + macOS arm64 wheels (CPU + Apple Metal)

CUDA loaders: prebuilt cu13 wheel on the latest release, or build from source — see GPU acceleration. Details: installation guide.

Quick Start

import turboloader

loader = turboloader.DataLoader(
    'imagenet.tar',                 # TAR archive of JPEGs
    batch_size=128,
    image_size=224,                 # fixed size => one contiguous tensor per batch
    output_format='pytorch',        # (N, 3, H, W) float32, normalized
    transform=turboloader.ImageNetNormalize(),
    shuffle=True,
    train_aug=True,                 # fused RandomResizedCrop + flip in C++
)
for images, meta in loader:
    # images: numpy (N,3,224,224); torch.from_numpy(images) is zero-copy.
    # meta['indices'] aligns external labels to this batch.
    ...

Labels: samples carry no label key (a TAR is flat). Use PyTorchCompatibleLoader for ImageFolder-style (image, label) tuples, or align a label array via meta['indices'].

More: quickstart · per-sample dict API & transforms · tokens, arrays & any Python dataset · interactive notebook

Benchmarks (headlines)

Real data, real consumption, interleaved medians, warmup excluded. Run them yourself — scripts in benchmarks/, full methodology + honest caveats (and the corrections we published) in docs/benchmarks.

Regime TurboLoader Best alternative Hardware
On-the-fly CPU (decode every epoch) ~55k img/s tf.data ~27k · PyTorch ~20k M4 Max
On-the-fly GPU 28.5k img/s NVIDIA DALI 25.5k (+12%) RTX 3090
Pre-processed, fits in VRAM ~280k img/s FFCV ~80k (3.5×) RTX 3090
Pre-processed, streaming > VRAM ~140k img/s FFCV ~85k (1.6×) RTX 3090
Pre-processed, unified memory 433–757k img/s numpy resident ~3.7k M4 Max
Pre-processed, CPU mmap (TBL-RAW, any hardware) 531k img/s raw serve (99k np.sum-consumed w/ prefetch) float32 RAM cache 62k (60k) at ~9× the peak RSS M4 Max
Video → training batches 2,556 f/s (3.9×) OpenCV 657 · PyAV 535 · torchcodec 173 M4 Max
End-to-end ResNet-18 training 1.05–1.17× vs PyTorch recipe ~9% above the pure-GPU floor RTX 3090
End-to-end VIDEO training (r3d_18) 1.16× vs PyTorch+PyAV recipe both decode-bound (honest) RTX 3090
LLM tokens → device 168M tok/s (1.9×) nanoGPT get_batch 88M RTX 3090

Honest notes worth knowing before you quote these: FFCV is faster than us on-the-fly is impossible for it (needs .beton conversion); decord beats our CUDA video cpu-backend on weak-CPU hosts; MetalTokenGather ties the CPU path (so we recommend the CPU path); e2e ResNet-18 on Apple MPS is a tie because the GPU is the bottleneck; CudaPrefetcher measured neutral in our e2e runs (decode, not H2D, binds them). All in the full write-ups.

Architecture

flowchart TD
    subgraph PY["Python (thin orchestration)"]
        API["DataLoader · TokenDataLoader · ArrayDataLoader · video/GPU loaders"]
    end
    subgraph CPP["C++20 core — GIL released"]
        MM["memory-mapped TAR / TBL v2 reader"]
        POOL["persistent thread pool<br/>per-thread libjpeg-turbo decoders"]
        SIMD["SIMD transforms<br/>NEON / AVX2 / AVX-512"]
        BUF["fused write into the<br/>output batch buffer"]
        MM --> POOL --> SIMD --> BUF
    end
    subgraph GPU["GPU kernels"]
        METAL["Apple Metal<br/>resident · video · transforms"]
        CUDA["NVIDIA CUDA + nvImageCodec<br/>resident · stream · video · clips"]
    end
    API --> MM
    BUF --> API
    API -.-> METAL
    API -.-> CUDA

Deep dive: architecture · GPU acceleration · transform library (24 transforms) · TBL v2 binary format

Documentation
Getting started installation · quickstart · notebook · troubleshooting
API API reference · transforms
Guides PyTorch · TensorFlow · distributed (DDP)
Examples ResNet-50 training · Lightning · DDP · GPT on tokens
Benchmarks methodology + full results · video · Metal resident · e2e training

License & Citation

MIT. If you use TurboLoader in your research:

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

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Author/Maintainer: Arnav Jain

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This release

2.36.0

2.35.0

2.34.1

2.34.0

2.33.0

2.32.0

2.31.0

2.30.0

2.29.0

2.28.0

2.27.0

2.26.3

2.26.2

2.26.1

2.26.0

2.25.0

2.23.0

2.22.0

2.21.0

2.20.0

2.19.0

2.18.0

2.17.0

2.16.0

2.15.0

2.14.0

2.13.0

2.12.0

2.11.0

2.10.0

2.9.0

2.8.0

2.7.0

2.6.0

2.5.0

2.4.0

2.3.23

2.3.22

2.3.21

2.3.20

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2.3.18

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2.3.16

2.3.15

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2.3.13

2.3.12

2.3.10

2.3.6

2.3.5

2.3.4

2.3.3

2.3.2

2.3.0

2.2.0

2.1.0

2.0.0

1.9.0

1.8.1

1.8.0

1.7.9

1.7.8

1.7.7

1.7.6

1.7.5

1.7.4

1.7.3

1.7.2

1.7.1

1.7.0

1.6.1

1.6.0

1.5.1

1.5.0

1.4.0

1.3.0

1.2.1

1.2.0

1.1.0

0.8.1

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