# NVIDIA-Merlin/NVTabular

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

Repository: https://github.com/NVIDIA-Merlin/NVTabular
Canonical: https://ross.abutalabs.com/products/nvtabular
Language: Python
License: Apache-2.0
License Family: permissive
Topics: deep-learning, feature-engineering, feature-selection, gpu, machine-learning, nvidia, preprocessing, recommendation-system, recommender-system
Last push: 2026-05-22T14:11:39+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 83, release rhythm 8, longevity 100
- inputs: {"age_days": 2343, "days_push": 103, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1150, forks 149 (observed 2026-08-28T04:03:46.528733+00:00)

## What it is
NVTabular is a GPU-accelerated feature engineering and preprocessing library for tabular data, built to handle terabyte-scale datasets for training deep learning recommender systems. It is part of NVIDIA Merlin and uses RAPIDS Dask-cuDF to process datasets that exceed GPU and CPU memory.

## Use cases
- preprocess terabyte-scale tabular datasets for deep learning
- feature engineering for recommender systems on GPU
- accelerate data loading for GPU model training
- build end-to-end recommender system pipelines with NVIDIA Merlin
- apply training-time preprocessing to inference with Triton

## When to choose
- you train deep learning recommenders on very large tabular datasets
- you need GPU-accelerated ETL that out-of-memory scales via Dask-cuDF
- you want preprocessing steps reusable at inference time within the Merlin ecosystem

## When to avoid
- you work with small datasets where pandas or scikit-learn suffice
- you have no NVIDIA GPU available
- you need general-purpose CPU-only feature engineering

## Facets
- artifact type: library
- maturity: maintenance
- function: etl, machine-learning, data-science
- domain: machine-learning, deep-learning, gpu-computing
- platform: python
- tags: recommender-systems, feature-engineering, tabular-data, rapids, nvidia-merlin, cudf, preprocessing, data-engineering, gpu, linux, docker

## Member repositories
- NVIDIA-Merlin/NVTabular (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:46.528733+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:33:39.182422+00:00, confidence not recorded.
  - readme: https://github.com/NVIDIA-Merlin/NVTabular (fetched 2026-08-28T04:03:46.528733+00:00, sha 626668c17523)
  - registry_pypi: https://pypi.org/pypi/nvtabular/json (fetched 2026-08-29T12:38:28.207754+00:00, sha 7767de24a50c)
- Data as of 2026-08-30T08:39:29.467469+00:00.