TabTransformer - Amazon SageMaker
How to use TabTransformerInput and Output interface for the TabTransformer algorithmAmazon EC2 instance recommendation for the TabTransformer algorithmSample Notebooks
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

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.

How to use SageMaker TabTransformer

You can use TabTransformer as an Amazon SageMaker built-in algorithm. The following section describes how to use TabTransformer with the SageMaker Python SDK. For information on how to use TabTransformer from the Amazon SageMaker Studio Classic UI, see SageMaker JumpStart.

  • Use TabTransformer as a built-in algorithm

    Use the TabTransformer built-in algorithm to build a TabTransformer training container as shown in the following code example. You can automatically spot the TabTransformer built-in algorithm image URI using the SageMaker image_uris.retrieve API (or the get_image_uri API if using Amazon SageMaker Python SDK version 2).

    After specifying the TabTransformer image URI, you can use the TabTransformer container to construct an estimator using the SageMaker Estimator API and initiate a training job. The TabTransformer built-in algorithm runs in script mode, but the training script is provided for you and there is no need to replace it. If you have extensive experience using script mode to create a SageMaker training job, then you can incorporate your own TabTransformer training scripts.

    from sagemaker import image_uris, model_uris, script_uris

train_model_id, train_model_version, train_scope = "pytorch-tabtransformerclassification-model", "*", "training"
training_instance_type = "ml.p3.2xlarge"

# Retrieve the docker image
train_image_uri = image_uris.retrieve(
    region=None,
    framework=None,
    model_id=train_model_id,
    model_version=train_model_version,
    image_scope=train_scope,
    instance_type=training_instance_type
)

# Retrieve the training script
train_source_uri = script_uris.retrieve(
    model_id=train_model_id, model_version=train_model_version, script_scope=train_scope
)

train_model_uri = model_uris.retrieve(
    model_id=train_model_id, model_version=train_model_version, model_scope=train_scope
)

# Sample training data is available in this bucket
training_data_bucket = f"jumpstart-cache-prod-{aws_region}"
training_data_prefix = "training-datasets/tabular_binary/"

training_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}/train"
validation_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}/validation"

output_bucket = sess.default_bucket()
output_prefix = "jumpstart-example-tabular-training"

s3_output_location = f"s3://{output_bucket}/{output_prefix}/output"

from sagemaker import hyperparameters

# Retrieve the default hyperparameters for training the model
hyperparameters = hyperparameters.retrieve_default(
    model_id=train_model_id, model_version=train_model_version
)

# [Optional] Override default hyperparameters with custom values
hyperparameters[
    "n_epochs"
] = "50"
print(hyperparameters)

from sagemaker.estimator import Estimator
from sagemaker.utils import name_from_base

training_job_name = name_from_base(f"built-in-algo-{train_model_id}-training")

# Create SageMaker Estimator instance
tabular_estimator = Estimator(
    role=aws_role,
    image_uri=train_image_uri,
    source_dir=train_source_uri,
    model_uri=train_model_uri,
    entry_point="transfer_learning.py",
    instance_count=1,
    instance_type=training_instance_type,
    max_run=360000,
    hyperparameters=hyperparameters,
    output_path=s3_output_location
)

# Launch a SageMaker Training job by passing the S3 path of the training data
tabular_estimator.fit(
    {
        "training": training_dataset_s3_path,
        "validation": validation_dataset_s3_path,
    }, logs=True, job_name=training_job_name
)

    For more information about how to set up the TabTransformer as a built-in algorithm, see the following notebook examples.

Input and Output interface for the TabTransformer algorithm

TabTransformer operates on tabular data, with the rows representing observations, one column representing the target variable or label, and the remaining columns representing features.

The SageMaker implementation of TabTransformer supports CSV for training and inference:

  • For Training ContentType, valid inputs must be text/csv.

  • For Inference ContentType, valid inputs must be text/csv.

Note

For CSV training, the algorithm assumes that the target variable is in the first column and that the CSV does not have a header record.

For CSV inference, the algorithm assumes that CSV input does not have the label column.

Input format for training data, validation data, and categorical features

Be mindful of how to format your training data for input to the TabTransformer model. You must provide the path to an Amazon S3 bucket that contains your training and validation data. You can also include a list of categorical features. Use both the training and validation channels to provide your input data. Alternatively, you can use only the training channel.

Use both the training and validation channels

You can provide your input data by way of two S3 paths, one for the training channel and one for the validation channel. Each S3 path can either be an S3 prefix that points to one or more CSV files or a full S3 path pointing to one specific CSV file. The target variables should be in the first column of your CSV file. The predictor variables (features) should be in the remaining columns. If multiple CSV files are provided for the training or validation channels, the TabTransformer algorithm concatenates the files. The validation data is used to compute a validation score at the end of each boosting iteration. Early stopping is applied when the validation score stops improving.

If your predictors include categorical features, you can provide a JSON file named categorical_index.json in the same location as your training data file or files. If you provide a JSON file for categorical features, your training channel must point to an S3 prefix and not a specific CSV file. This file should contain a Python dictionary where the key is the string "cat_index_list" and the value is a list of unique integers. Each integer in the value list should indicate the column index of the corresponding categorical features in your training data CSV file. Each value should be a positive integer (greater than zero because zero represents the target value), less than the Int32.MaxValue (2147483647), and less than the total number of columns. There should only be one categorical index JSON file.

Use only the training channel:

You can alternatively provide your input data by way of a single S3 path for the training channel. This S3 path should point to a directory with a subdirectory named training/ that contains one or more CSV files. You can optionally include another subdirectory in the same location called validation/ that also has one or more CSV files. If the validation data is not provided, then 20% of your training data is randomly sampled to serve as the validation data. If your predictors include categorical features, you can provide a JSON file named categorical_index.json in the same location as your data subdirectories.

Note

For CSV training input mode, the total memory available to the algorithm (instance count multiplied by the memory available in the InstanceType) must be able to hold the training dataset.

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.