alimentar

Data Loading, Distribution and Tooling in Pure Rust

A sovereignty-first data loading library for the paiml AI stack. Provides HuggingFace-compatible functionality with local-first design.

Table of Contents

• Features
• Installation
• Quick Start
• Examples
  • Apply Transforms
  • Import from HuggingFace
  • Use S3 Backend
  • Streaming Large Datasets
• Feature Flags
• CLI Usage
• Architecture
• Performance
• Quality Standards
• Citation
• License
• Related Projects

Features

• Zero-copy Arrow: All data flows through Arrow RecordBatches for maximum performance
• Multiple backends: Local filesystem, S3-compatible storage, HTTP/HTTPS sources
• HuggingFace Hub: Import datasets directly from HuggingFace
• Streaming: Memory-efficient lazy loading for large datasets
• Transforms: Filter, sort, sample, shuffle, normalize, and more
• Registry: Local dataset registry with versioning and metadata
• Data Quality: Null detection, duplicate checking, outlier analysis
• Drift Detection: KS test, Chi-square, PSI for distribution monitoring
• Federated Learning: Local, proportional, and stratified dataset splits
• Built-in Datasets: MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, Iris
• WASM support: Works in browsers via WebAssembly

Installation

Add to your Cargo.toml:

[dependencies]
alimentar = "0.1"

With specific features:

[dependencies]
alimentar = { version = "0.1", features = ["s3", "hf-hub"] }

Quick Start

use alimentar::{ArrowDataset, Dataset, DataLoader};

// Load a Parquet file
let dataset = ArrowDataset::from_parquet("data/train.parquet")?;
println!("Loaded {} rows", dataset.len());

// Create a DataLoader for batched iteration
let loader = DataLoader::new(dataset)
    .batch_size(32)
    .shuffle(true);

for batch in loader {
    // Process Arrow RecordBatch
    println!("Batch has {} rows", batch.num_rows());
}

Examples

Apply Transforms

use alimentar::{ArrowDataset, Dataset, Select, Filter, Shuffle};
use alimentar::Transform;

let dataset = ArrowDataset::from_parquet("data.parquet")?;

// Select specific columns
let select = Select::new(vec!["id", "text", "label"]);
let dataset = dataset.with_transform(Box::new(select));

// Shuffle with a seed for reproducibility
let shuffle = Shuffle::with_seed(42);

Import from HuggingFace

use alimentar::hf_hub::HfDataset;

let dataset = HfDataset::builder("squad")
    .split("train")
    .build()?
    .load()?;

Use S3 Backend

use alimentar::backend::{S3Backend, StorageBackend};

let backend = S3Backend::new(
    "my-bucket",
    "us-east-1",
    None, // Use default endpoint
)?;

let data = backend.get("datasets/train.parquet")?;

Streaming Large Datasets

use alimentar::streaming::StreamingDataset;

// Load in chunks of 1024 rows
let dataset = StreamingDataset::from_parquet("large_data.parquet", 1024)?;

for batch in dataset {
    // Process without loading entire file into memory
}

Feature Flags

Feature	Description	Default
local	Local filesystem backend	Yes
tokio-runtime	Async runtime support	Yes
cli	Command-line interface	Yes
mmap	Memory-mapped file support	Yes
s3	S3-compatible storage backend	No
http	HTTP/HTTPS sources	No
hf-hub	HuggingFace Hub integration	No
wasm	WebAssembly support	No

CLI Usage

# Convert between formats
alimentar convert data.csv data.parquet

# View dataset info
alimentar info data.parquet

# Preview first N rows
alimentar head data.parquet --rows 10

# Import from HuggingFace
alimentar import hf squad --output ./data/squad

Architecture

alimentar
├── ArrowDataset      # In-memory dataset backed by Arrow
├── StreamingDataset  # Lazy loading for large datasets
├── DataLoader        # Batched iteration with shuffle
├── Transforms        # Data transformations (Select, Filter, Sort, etc.)
├── Backends          # Storage (Local, S3, HTTP, Memory)
├── Registry          # Dataset versioning and metadata
└── HF Hub            # HuggingFace integration

Performance

• Zero-copy data access via Arrow
• Memory-mapped file support for large datasets
• Parallel data loading (when not in WASM)
• Efficient columnar operations

Quality Standards

This project follows extreme TDD practices:

• 85%+ test coverage
• 85%+ mutation score target
• Zero unwrap()/expect() in library code
• Comprehensive clippy lints

Citation

If you use alimentar in your research, please cite:

@software{alimentar2025,
  author       = {paiml},
  title        = {alimentar: Data Loading, Distribution and Tooling in Pure Rust},
  year         = {2025},
  publisher    = {GitHub},
  url          = {https://github.com/paiml/alimentar},
  version      = {0.1.0}
}

License

MIT License - see LICENSE for details.

Related Projects

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• aprender - ML/DL framework in Rust