# How to use SageMaker AI TabTransformer
<a name="tabtransformer-modes"></a>

You can use TabTransformer as an Amazon SageMaker AI 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 pretrained models](studio-jumpstart.md).
+ **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 AI `image_uris.retrieve` API.

  After specifying the TabTransformer image URI, you can use the TabTransformer container to construct a ModelTrainer using the SageMaker AI ModelTrainer 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.core import image_uris
  from sagemaker.core import 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.train import ModelTrainer
  from sagemaker.train.configs import InputData
  from sagemaker.train.configs import SourceCode, Compute, StoppingCondition, OutputDataConfig
  from sagemaker.utils import name_from_base
  
  training_job_name = name_from_base(f"built-in-algo-{train_model_id}-training")
  
  # Create SageMaker ModelTrainer instance
  tabular_model_trainer = ModelTrainer(
      role=aws_role,
      training_image=train_image_uri,
      source_code=SourceCode(source_dir=train_source_uri, entry_script="transfer_learning.py"),
      # In V3, pre-trained model artifacts are passed via input_data_config
      compute=Compute(instance_type=training_instance_type, instance_count=1),
      stopping_condition=StoppingCondition(max_runtime_in_seconds=360000),
      hyperparameters=hyperparameters,
      output_data_config=OutputDataConfig(s3_output_path=s3_output_location)
  )
  
  # Launch a SageMaker Training job by passing the S3 path of the training data
  tabular_model_trainer.train(
      input_data_config=[
          InputData(channel_name="training", data_source=training_dataset_s3_path),
          InputData(channel_name="validation", data_source=validation_dataset_s3_path),
          InputData(channel_name="model", data_source=train_model_uri),
      ]
  )
  ```

  For more information about how to set up the TabTransformer as a built-in algorithm, see the following notebook examples.
  + [Tabular classification with Amazon SageMaker AI TabTransformer algorithm](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/tabtransformer_tabular/Amazon_Tabular_Classification_TabTransformer.ipynb)
  + [Tabular regression with Amazon SageMaker AI TabTransformer algorithm](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/tabtransformer_tabular/Amazon_Tabular_Regression_TabTransformer.ipynb)

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for Amazon SageMaker. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query sagemaker` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).