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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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.
| Date | Stars |
|---|---|
| 2026-07-31 | 1149 |
| 2026-08-05 | 1149 |
| 2026-08-06 | 1149 |
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## [NVTabular](https://github.com/NVIDIA/NVTabular) [](https://pypi.python.org/pypi/NVTabular/) [](https://github.com/NVIDIA-Merlin/NVTabular/blob/stable/LICENSE) [](https://nvidia-merlin.github.io/NVTabular/stable/Introduction.html) NVTabular is a feature engineering and preprocessing library for tabular data that is designed to easily manipulate terabyte scale datasets and train deep learning (DL) based recommender systems. It provides high-level abstraction to simplify code and accelerates computation on the GPU using the [RAPIDS Dask-cuDF](https://github.com/rapidsai/cudf/tree/main/python/dask_cudf) library. NVTabular is a component of [NVIDIA Merlin](https://developer.nvidia.com/nvidia-merlin), an open source framework for building and deploying recommender systems and works with the other Merlin components including [Merlin Models](https://github.com/NVIDIA-Merlin/models), [HugeCTR](https://github.com/NVIDIA/HugeCTR) and [Merlin Systems](https://github.com/NVIDIA-Merlin/systems) to provide end-to-end acceleration of recommender systems on the GPU. Extending beyond model training, with NVIDIA’s [Triton Inference Server](https://github.com/NVIDIA/tensorrt-inference-server), the feature engineering and preprocessing steps performed on the data during training can be automatically applied to incoming data during inference. <!-- <img src='https://developer.nvidia.com/blog/wp-content/uploads/2020/07/recommender-system-training-pipeline-1.png'/> --> ### Benefits When training DL recommender systems, data scientists and machine learning (ML) engineers have been faced with the following challenges: - **Huge Datasets**: Commercial recommenders are trained on huge datasets that may be several terabytes in scale. - **Complex Data Feature Engineering and Preprocessing Pipelines**: Datasets need to be preprocessed and transformed so that they can be used with DL models and frameworks. In addition, feature engineering creates an extensive set of new features from existing ones, requiring multiple iterations to arrive at an optimal solution. - **Input Bottleneck**: Data loading, if not well optimized, can be the slowest part of the training process, leading to under-utilization of high-throughput computing devices such as GPUs. - **Extensive Repeated Experimentation**: The entire data engineering, training, and evaluation process can be repetitious and time consuming, requiring significant computational resources. NVTabular alleviates these challenges and helps data scientists and ML engineers: - process datasets that exceed GPU and CPU memory without having to worry about scale. - focus on what to do with the data and not how to do it by using abstraction at the operation level. - prepare datasets quickly and easily for experimentation so that more models can be trained. - deploy models into production by providing faster dataset transformation Learn more in the NVTabular [core features documentation](https://nvidia-merlin.github.io/NVTabular/stable/core_features.html). ### Performance When running NVTabular on the Criteo 1TB Click Logs Dataset using a single V100 32GB GPU, feature engineering and preprocessing was able to be completed in 13 minutes. Furthermore, when running NVTabular on a DGX-1 cluster with eight V100 GPUs, feature engineering and preprocessing was able to be completed within three minutes. Combined with [HugeCTR](http://www.github.com/NVIDIA/HugeCTR/), the dataset can be processed and a full model can be trained in only six minutes. The performance of the Criteo DRLM workflow also demonstrates the effectiveness of the NVTabular library. The original ETL script provided in Numpy took over five days to complete. Combined with CPU training, the total iteration time is over one week. By optimiz
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Ben Frederickson · Canada
312
Julio Perez · Nvidia
196
121
Karl Higley
102
NVIDIA
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Oliver Holworthy · @NVIDIA · United Kingdom
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Onur Yilmaz · NVIDIA
29
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Mike McKiernan · NVIDIA
25
15
Radek Osmulski · Australia
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Even Oldridge
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Gabriel Moreira · NVIDIA · Brazil
5
3
Alberto Alvarez
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David Xia · @modal-labs
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Jake Awe
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Marc Romeyn · NVIDIA · Netherlands
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:62063fd62f922abe, topic:gpu
matched fp:62063fd62f922abe, topic:deep-learning