datapruning 1.0.1

Intelligent data pruning for ML datasets

DataPruning Lite

Local-first dataset pruning. Free for datasets up to 100K rows.

Overview

DataPruning reduces dataset size before training by selecting a smaller, more informative subset of data. Runs entirely on your machine through a compiled SDK — no data upload, no external processing.

Installation

pip install datapruning

Note: PyTorch (~2 GB) is a required dependency and will be installed automatically. See pytorch.org for GPU/CPU options.

Requirements

• Python 3.9–3.13
• PyTorch 2.0+
• Pandas 2.0+
• NumPy 1.24+

Usage

import pandas as pd
from datapruning import Pruner

df = pd.read_csv("dataset.csv")
p = Pruner(df)
filtered = p.prune(target_col="label", keep_ratio=0.5)

print(f"Kept {len(filtered)} / {len(df)} rows")

Features

• Local execution, no data upload
• Pre-training dataset optimization
• Pandas DataFrame input
• Compiled processing module
• Free tier: up to 100K rows per dataset

Limits

Datasets exceeding 100,000 rows require an enterprise license. Visit https://www.datapruning.com for details.

License

Proprietary software. See LICENSE file.