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

  • 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 JPEG Decoding - Optional NVIDIA nvJPEG support for accelerated decoding

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: macOS, Linux, Windows

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).

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 Jun 30, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2.36.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for Ajain0422 from gravatar.com Ajain0422

Credits

Author/Maintainer: Arnav Jain

GitHub Statistics

Data verified by PyPI on Jun 30, 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

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

turboloader-2.28.0.tar.gz (675.3 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

turboloader-2.28.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (10.2 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

turboloader-2.28.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.8 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.27+ ARM64manylinux: glibc 2.28+ ARM64

turboloader-2.28.0-cp314-cp314-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

turboloader-2.28.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (10.2 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

turboloader-2.28.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.9 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.27+ ARM64manylinux: glibc 2.28+ ARM64

turboloader-2.28.0-cp313-cp313-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

turboloader-2.28.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (10.2 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

turboloader-2.28.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.27+ ARM64manylinux: glibc 2.28+ ARM64

turboloader-2.28.0-cp312-cp312-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

turboloader-2.28.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

turboloader-2.28.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.27+ ARM64manylinux: glibc 2.28+ ARM64

turboloader-2.28.0-cp311-cp311-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

turboloader-2.28.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

turboloader-2.28.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.27+ ARM64manylinux: glibc 2.28+ ARM64

turboloader-2.28.0-cp310-cp310-macosx_11_0_arm64.whl (1.1 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

File details

Details for the file turboloader-2.28.0.tar.gz.

File metadata

  • Download URL: turboloader-2.28.0.tar.gz
  • Upload date:
  • Size: 675.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for turboloader-2.28.0.tar.gz
Algorithm Hash digest
SHA256 f56832ffc46542bcf7809b3eb78909585ad6750b66f8c76da442243de9b0c3e0
MD5 c787502cc3a12767396d8c7ecb68bdd1
BLAKE2b-256 3cd39e1ecee82f5bcde804a5224c4994ce6e00990721c1737734e0d7c9cee14b

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0.tar.gz:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 89f83afed580df0db09ad7b7cbeb74279b285dfa77125e50e0db79282b3327e2
MD5 25662e7d127b0dfd7f4f8a844f8f9d10
BLAKE2b-256 0d783e874e77b671829e55d9ed4312306c442b7ae8250ca6ca00bd4ab29c9cc4

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 06e7489d85987cc739d30104c29d66f443f2c9781f7707f28c192dcb35fa1c2d
MD5 4c2bf448ae2bcd3769d7416e725ee42d
BLAKE2b-256 27def43d628549df466c0a3727730ef78e01a814c70628a8444d08c5bb6be01b

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6f7c6bb094b03a194a8d97fd96aa3f945888f04dfa877ae5df8a3239b1788422
MD5 59f26ffd9a626d02bd7be4e01dd8ac16
BLAKE2b-256 132b809781f78d8056462e91564d9cd640bfa853c4c06f3c72a85f56d7689d18

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp314-cp314-macosx_11_0_arm64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 40dbb25d947d86d0f84fbda66e78753c79b7201762f9154a1f7cf5272bcb461f
MD5 44eed8a481600c0418e64638c9654010
BLAKE2b-256 39d0ca451e36ccee044fcae6bcbb5f5141f32a75f2bfce457bc93ddda625e82b

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 eaebfabbf1708539d35f802e250c15efa70bca4a1fee35e561e8d0d2c71222aa
MD5 2b863549899a211b9045661fe4d10382
BLAKE2b-256 291b12961edb0f16c98eb725bbc146b1f030d1faca5c6b64ce9c89b1ed8aad59

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 29e5c3de24a8b7048167821e68b06c30e6c41d7cfb7a7e1c6ea7bdc0ac8efe85
MD5 360357bb95e976840dcc64a1d284dbcd
BLAKE2b-256 56d725cc976ca29a684979fdbee1ac51d9087bf9de0660f10078fbfd6f92e4a7

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp313-cp313-macosx_11_0_arm64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a8e774c344137b9c8f97cc8b601e91414b3ec547c192aaaed1333891b915af12
MD5 daeb6e5702a49299bfbeb7fad9e22906
BLAKE2b-256 7c494655f9a9deb714cf9b3f058887f7a163ab2b6d37fea57a1f0d693fba70d2

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 200e9b4105e6bf0db4cac64c9678baedd9015bcf93c27f4eec5c391660db6108
MD5 d31ebfccfd69d7ad4795801c02a161c9
BLAKE2b-256 82ff873ed99a84bc751aa1c45145c867944a134568c089bff2cc6abd4611d7ed

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b4c1c0dff6922e88007441a1373f8792542ab5ac76d85245b49f4ee7355362fe
MD5 896f8a8beaa4f807eea2a22f2b92bdb9
BLAKE2b-256 2cea87f880085472bc56392046647a49f9125432c3cfefe44da47c1dbc943a85

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp312-cp312-macosx_11_0_arm64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1cf58dcd9585d570d81cef76f127cc4b5f44d116423d8d78069c2a205e1d910c
MD5 2c29eb1103dcb57c67ca5935d27f68e5
BLAKE2b-256 c86c1b0c2d9be3669973b829296b9817e9c9eef7f6ac2f4c4b7be17aa72acec9

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 dc47bd2991817ac918597a66c82f537c794fee2c423706456b23366653835bbd
MD5 f84fefe95953cab86627ef42711b9404
BLAKE2b-256 decd41cd8602e4cc507a4b119b588918067c307c363d0c37044db4f79a2b145a

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 2ce5ed289cd17b60db36b56804262884ec1e794975469d7b40690d4115b99e73
MD5 ccb6282a024e95190af0dd15cfc01663
BLAKE2b-256 830914897e1ac3313c14563c20c9b801f3f6529a7a84ab5c00f44a3b6c06a59c

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp311-cp311-macosx_11_0_arm64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 790dcf88a7352617e39ce5757e0ef9a2be91f656c84d473b400d25594185e136
MD5 0d94b8c25fddc0bbb8553d803597e821
BLAKE2b-256 12824c5bf5aa2b6d86e3958d530e50481536bdda90eb66b1292bf9423609f114

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 95b9a481d57062031d2695341b389018b7bdde518d258fc63b243c667e5e49e7
MD5 9db84d146f2e573f2644ae2b20b07105
BLAKE2b-256 72acd9cd3e1b9c3ed4256b1ca0ad1da29e11d744372ade2773b2009074899743

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file turboloader-2.28.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.28.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a1ce17c6cd2e9e43ce8d2d589add2602c4303170de547dbcdfbf2fc7b9208ea1
MD5 29b4c1da037fc1cd53c667184c39bd03
BLAKE2b-256 f8d23dee64fdb6bbf737867ddf97c4e35549ba67abf8a4348522689163badffb

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.28.0-cp310-cp310-macosx_11_0_arm64.whl:

Publisher: build-wheels.yml on ALJainProjects/TurboLoader

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

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

This release

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

2.3.19

2.3.18

2.3.17

2.3.16

2.3.15

2.3.14

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

0.8.0

0.7.0

0.6.0

0.5.3

0.5.2

0.5.1

0.5.0

0.4.0

0.3.7 yanked

0.3.3 yanked

0.3.2 yanked

0.2.1 yanked

0.2.0 yanked

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page