Load data

Amazon SageMaker AI trains machine learning models using data stored in an Amazon S3 bucket. Follow these steps to load training data from Teradata into an Amazon S3 bucket:

1. In the Amazon SageMaker AI console, select Applications and IDEs -> Notebooks -> Notebook instances, and then select Create notebook instance. See the Amazon SageMaker AI Developer Guide for instructions on how to create a notebook instance:

1. Open your notebook instance:

2. Start a new file by clicking on New -> conda_python3:

3. Install Teradata Python library:

   !pip install teradataml
   

4. In a new cell and import additional libraries:

   import teradataml as tdml
   from teradataml import create_context, get_context, remove_context
   from teradataml.dataframe.dataframe import DataFrame
   import pandas as pd
   import boto3, os
   

5. In a new cell, connect to Teradata. Replace <hostname>, <database user name>, <database password> to match your Teradata environment:

   create_context(host = '<hostname>', username = '<database user name>', password = '<database password>')
   

6. Retrieve data from the table where the training dataset resides using TeradataML DataFrame API:

   train_data = tdml.DataFrame('table_with_training_data')
   trainDF = train_data.to_pandas()
   

   Note

   For the XGBoost training job used in this how-to, place the target column first and encode categorical values as numeric values before exporting the data. The CSV file must not contain a header row.

7. Write data to a local file:

   trainFileName = 'train.csv'
   trainDF.to_csv(trainFileName, header=None, index=False)
   

8. Upload the file to Amazon S3. Replace <s3_bucket_name> with the name of your Amazon S3 bucket:

   bucket = '<s3_bucket_name>'
   prefix = 'sagemaker/train'
   
   with open(trainFileName, 'rb') as trainFile:
       boto3.Session().resource('s3') \
           .Bucket(bucket) \
           .Object(os.path.join(prefix, trainFileName)) \
           .upload_fileobj(trainFile)