v0.1.0 - Initial Release

dataset-core v0.1.0

A generic, thread-safe dataset container with lazy loading and caching for Rust.

Note: This is an initial release. The API is not yet stable and may change in future versions.

Highlights

• Zero-dependency core — Dataset<T> pairs a storage directory with lazily-initialized data of any type. The first call to load() runs your closure and caches the result via OnceLock; every subsequent call returns &T with zero overhead, even across threads.

• Feature-gated modules — opt in to only what you need:

  Feature	What it adds	Extra deps
  (none)	Dataset<T>	none
  utils	download_to, unzip, create_temp_dir, file_sha256_matches, acquire_dataset, and the error module	ureq, zip, tempfile, sha2
  datasets	6 built-in ML dataset loaders (implies utils)	ndarray, csv

Built-in Datasets

Six classic machine learning datasets, ready to use with a consistent API (new → features() / labels() / targets() / data()):

Dataset	Samples	Features	Task
Iris	150	4	Classification
Boston Housing	506	13	Regression
Diabetes (Pima)	768	8	Classification
Titanic	891	11 (mixed)	Classification
Wine Quality (Red)	1,599	11	Regression
Wine Quality (White)	4,898	11	Regression

All datasets are automatically downloaded, cached locally, and validated with SHA-256 checksums.

Utility Functions (utils feature)

• download_to — download a remote file into a directory
• unzip — extract a ZIP archive
• create_temp_dir — create a self-cleaning temporary directory
• file_sha256_matches — verify a file's SHA-256 hash
• acquire_dataset — cache-aware dataset acquisition workflow (temp dir → prepare → optional hash check → move to final location)

Requirements

• Rust edition 2024, MSRV 1.88.0
• License: MIT

Quick Start

use dataset_core::Dataset;

let ds = Dataset::<String>::new("./cache");
let data = ds.load(|dir| Ok(std::fs::read_to_string(format!("{dir}/my_file.txt"))?))?;
println!("{data}");

With built-in datasets:

use dataset_core::datasets::Iris;

let iris = Iris::new("./data");
let (features, labels) = iris.data()?;
println!("shape: {:?}, first label: {}", features.shape(), labels[0]);