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# 如何使用 SageMaker AI TabTransformer
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您可以將 TabTransformer 用作 Amazon SageMaker AI 內建演算法。下一節描述了如何將 TabTransformer 與 SageMaker Python SDK 搭配使用。如需如何從 Amazon SageMaker Studio Classic 使用者介面使用 TabTransformer 的資訊，請參閱[SageMaker JumpStart 預先訓練模型](studio-jumpstart.md)。
+ **將 TabTransformer 用作內建演算法**

  使用 TabTransformer 內建演算法來建置 TabTransformer 訓練容器，如下面的程式碼範例所示。您可以使用 SageMaker AI `image_uris.retrieve` API (或 `get_image_uri` API，如果使用 [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) 第 2 版) 自動發現 TabTransformer 內建演算法映像 URI。

  指定 TabTransformer 映像 URI 後，您可以使用 TabTransformer 容器，以使用 SageMaker AI 估算器 API 建置估算器，並啟動訓練任務。TabTransformer 內建演算法會以指令碼模式執行，但是訓練指令碼是為您提供的，不需要取代它。如果您有使用指令碼模式建立 SageMaker 訓練任務的豐富經驗，則可以合併自己的 TabTransformer 訓練指令碼。

  ```
  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
  )
  ```

  如需如何將 TabTransformer 設定為內建演算法的更多相關資訊，請參閱下列筆記本範例。
  + [使用 Amazon SageMaker AI TabTransformer 演算法進行表格式分類](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/tabtransformer_tabular/Amazon_Tabular_Classification_TabTransformer.ipynb)
  + [使用 Amazon SageMaker AI TabTransformer 演算法進行表格式迴歸](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/tabtransformer_tabular/Amazon_Tabular_Regression_TabTransformer.ipynb)