TabTransformer

TabTransformer is a novel deep tabular data modeling architecture for supervised learning. The TabTransformer architecture is built on self-attention-based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. Furthermore, the contextual embeddings learned from TabTransformer are highly robust against both missing and noisy data features, and provide better interpretability. This page includes information about Amazon EC2 instance recommendations and sample notebooks for TabTransformer.

Amazon EC2 instance recommendation for the TabTransformer algorithm

SageMaker TabTransformer supports single-instance CPU and single-instance GPU training. Despite higher per-instance costs, GPUs train more quickly, making them more cost effective. To take advantage of GPU training, specify the instance type as one of the GPU instances (for example, P3). SageMaker TabTransformer currently does not support multi-GPU training.

TabTransformer sample notebooks

The following table outlines a variety of sample notebooks that address different use cases of Amazon SageMaker TabTransformer algorithm.

Notebook Title	Description
Tabular classification with Amazon SageMaker TabTransformer algorithm	This notebook demonstrates the use of the Amazon SageMaker TabTransformer algorithm to train and host a tabular classification model.
Tabular regression with Amazon SageMaker TabTransformer algorithm	This notebook demonstrates the use of the Amazon SageMaker TabTransformer algorithm to train and host a tabular regression model.

For instructions on how to create and access Jupyter notebook instances that you can use to run the example in SageMaker, see Amazon SageMaker Notebook Instances. After you have created a notebook instance and opened it, choose the SageMaker Examples tab to see a list of all of the SageMaker samples. To open a notebook, choose its Use tab and choose Create copy.