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-ready outputs - output_format='pytorch'/'numpy'/'tensorflow' batch layouts, zero-copy torch adoption for the GPU loaders, and a shipped WebDatasetLoader
  • 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
  • Resident (pre-processed) epochs - CudaResidentLoader (~280k img/s, beats FFCV 3.5×) and MetalResidentLoader (433–757k img/s on unified memory) + MetalResidentArrays for any-dtype rows. Decode once, serve every epoch with one fused gather+shuffle+normalize kernel launch per batch
  • Video loaders - MetalVideoLoader (VideoToolbox hardware decode, 3.9× the best industry standard on an M4 Max) and CudaVideoLoader (GPU-resident batches, dual CPU/NVDEC decode backends, novel fused clip-assembly kernel via iter_clips). See video results

Which loader do I use?

One decision table for every entry point — pick by data type and hardware, without reading the internals:

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.
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 MetalVideoLoader (Apple) / CudaVideoLoader (NVIDIA) Hardware decode → training batches; iter_clips() for augmented clips.
LLM token streams (memmap) TokenDataLoader CPU memmap is already optimal (measured).
Arrays / embeddings / tabular ArrayDataLoader; MetalResidentArrays for GPU row gathers
WebDataset-style TARs WebDatasetLoader

Two lifetime rules to know: (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

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

Training input (the fast path — start here)

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.
    ...

This is the path all the benchmark numbers refer to. The dict API below is the flexible per-sample path — several times slower; use it for inspection, not epochs.

Basic Usage (per-sample dicts)

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 MetalResidentLoader (Apple M4 Max, unified memory: no H2D exists) ~757,000 produced / ~433,000 consumed ships in the pip wheel
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

On Apple Silicon the resident trick is even better than on NVIDIA: memory is unified, so "upload" is one memcpy and every GPU-written batch is a zero-copy numpy view. MetalResidentLoader serves each epoch as one fused gather+shuffle+normalize kernel launch per batch; MetalResidentArrays does the same for any-dtype rows (embedding tables: ~5× numpy fancy-indexing). Honest null result included: MetalTokenGather ties the CPU memmap path (0.87–1.08×) — keep using TokenDataLoader for tokens.

Video: MetalVideoLoader (macOS arm64, in the pip wheel — no FFmpeg needed) drives VideoToolbox hardware H.264/HEVC decode into a fused NV12→RGB+resize+normalize Metal kernel: real 1080p → 224px training batches at ~2,550 frames/s on an M4 Max — 3.9× the best industry standard (OpenCV 657, PyAV 535, torchcodec 173) and 97–99% of the media engine's hardware decode ceiling. On NVIDIA, CudaVideoLoader (CUDA build) lands GPU-resident batches via a dual decode backend (threaded CPU decode by default; NVDEC opt-in — measured virtualization-throttled under WSL2) plus a novel fused clip-assembly kernel (iter_clips: consistent RandomResizedCrop+flip across a whole clip + YUV→RGB + resize + normalize in ONE launch). Honest scorecard incl. where decord still wins on weak-CPU hosts: benchmarks/VIDEO_RESULTS.md.

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 12, 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 Jul 12, 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-2.35.0.tar.gz (678.2 kB view details)

Uploaded Source

Built Distributions

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

turboloader-2.35.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (10.0 MB view details)

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

turboloader-2.35.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.7 MB view details)

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

turboloader-2.35.0-cp314-cp314-macosx_11_0_arm64.whl (852.3 kB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

turboloader-2.35.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (10.0 MB view details)

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

turboloader-2.35.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.7 MB view details)

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

turboloader-2.35.0-cp313-cp313-macosx_11_0_arm64.whl (851.5 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

turboloader-2.35.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (10.0 MB view details)

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

turboloader-2.35.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.7 MB view details)

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

turboloader-2.35.0-cp312-cp312-macosx_11_0_arm64.whl (851.3 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

turboloader-2.35.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.9 MB view details)

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

turboloader-2.35.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.6 MB view details)

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

turboloader-2.35.0-cp311-cp311-macosx_11_0_arm64.whl (845.7 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

turboloader-2.35.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.9 MB view details)

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

turboloader-2.35.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl (9.6 MB view details)

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

turboloader-2.35.0-cp310-cp310-macosx_11_0_arm64.whl (844.0 kB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

File details

Details for the file turboloader-2.35.0.tar.gz.

File metadata

  • Download URL: turboloader-2.35.0.tar.gz
  • Upload date:
  • Size: 678.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for turboloader-2.35.0.tar.gz
Algorithm Hash digest
SHA256 9d61f08836ba65d66097a2a11dd0a4c2c4f8aa20e416c4041649ae5b33f4ab40
MD5 a480ee6a7a7ed74b81f78ad8169445fd
BLAKE2b-256 d668415c90f10c391af090463b8032879fb76dbdece6bb1e658834c3a1dd663e

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0.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-2.35.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 54dfcaaa52657f004c04be8f6fa0acbc97adfae8f049a5507fe2b34d8f01537c
MD5 e56c07c7a11c53a7abd7838f85ac7b94
BLAKE2b-256 f0462ffe5e7d57e6bd16aad2a1e84dca1e5fe96b9ae93081444373d56b941662

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 6060876efc2ce1bfcddbb5391e725ecc15c29fd752330d2f614c7f6e81b7ed00
MD5 6227abba1416701230e3c9f400547dcf
BLAKE2b-256 9fb7f5debbf836a3d4de32f6b4982ee33cc49a73f8bc26b1732ec1c926a1b84b

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 430b3ed3499a41204eab91818164030bd4d9d7182a29fba207973a05710a007b
MD5 e315eb4dc70ca0aebde47d06e8eeff6e
BLAKE2b-256 88674dc230f0cec5a4a0f89e9e91944f99482deb299f4f5ab56f107f589ebc11

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c425258e989029a7956ae938341a9a4becf2f2e06423efddc39dd310511effd9
MD5 400afa9fe91be533053569d770058e92
BLAKE2b-256 85c7f9a519d5e32096f45b89d41ef8527fb651057f6cdf908514ff47387a07e3

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 a30ccc7e65f468238a85a944cc8a80c59c26be544b579434d40ca6d5585d8fad
MD5 225ed27943a55131508821fb77eac228
BLAKE2b-256 e4f77e86823ddff3bcee3bad66c9e58ab6f10a43994de846ad4fb6b82ae53b96

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 27328bd8212ac68f92a333a42940e7431478f1307cf2aa2bca10b0ce34e3833b
MD5 2ac6621d8f7425aaa300b924f147b0b1
BLAKE2b-256 4d36c7c5f39f2664da7d76c9fc09490bae7525fc62aa638535c04ab540df2553

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 64354c77f58068298729fe37e9bd19eb5a725e0af5ee6a8bc494382905b5d58d
MD5 e73423d6e03cb6180219289f0b0097e7
BLAKE2b-256 7c3ff30a5f8282d83493e2e99c6a009f9e5a6b4e84635701cb6d2c695ad6f8ab

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 e6d979097c44bb79f40fea1d91bee7cf409f18cf644aeb4a93a6236598db622f
MD5 950cc580dbf2a3f65094eda7573ee397
BLAKE2b-256 b88f5d1544d03783eb5ce99b1532e9fd70a5e8a4d76062fba2e76d57eadce2e2

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ead0e28d405001b1f963febce007bda749658f405d6d35a7d90e016fa5fe5bd0
MD5 c0d69b3f2eee2a650941dd6a96ee37cf
BLAKE2b-256 d66332d0a976e631ca973d5112d1ce72ee43b1497ab24d9bcc784be11c5861fa

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 966e700feb7cfee7441181f1670870e8daafadfb1293ca94ff76314bbf54cb1b
MD5 ebafbbe88b7e16f7fa2d6321d0f781c3
BLAKE2b-256 9050b7278814f3d5ddd7b190d9252629c7db2d31df883ae1f37871bca7ffd306

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 78ab6a8c868d5141ee9c0407b680f586cd28024636eb05e7766d70fa69a7f22d
MD5 3c30e6a38775998639f73f0c0a8a3769
BLAKE2b-256 2be03e21e42a116c665f44075fbfef95d261c54eb286aaa589479d4d0ccf6ee5

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c43e1c3b1ae3eb66d5bf9f11ae4cfd63a6ab17a4b9e18ffefe6e50800fe5e10c
MD5 161c410811c8ac6609390bbc6e899e0b
BLAKE2b-256 e5da757ec028eaabde5c65a89175d1d6e2d4dd29629202935342059739aa4fd3

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ef975d7f32ebb21b821d8cf59dd66d75e20f8892df90f5031910476b21544ed5
MD5 4e275084a957dba387b1fc31a2eeca25
BLAKE2b-256 83b481761878d46d944e11d445d5f4543f9bb08469b2d2da39eef3a95af6ae4d

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 94cae0b69a27ac0197d1cac47f6ee9a340f53aba98f90e9c7649c904527141a1
MD5 298d6ef6e6959711c3175ab485aef804
BLAKE2b-256 51ccfe1f4b7f391ed951d544d87c47129347a630ad97c1f2c4524d829aa55389

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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-2.35.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for turboloader-2.35.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 377d5923d083b06c84b333d4094775d72f84867d59676b743090a2d4eed9ee27
MD5 ad8c8f919f8f1426216a6a81b63f8d43
BLAKE2b-256 79a1dfbf0cff5140f8059bab6e57467c039d89862071b52bec4bb1dd3e9e14cd

See more details on using hashes here.

Provenance

The following attestation bundles were made for turboloader-2.35.0-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

This release

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

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