TDPredictor.from_predictor Method |teradataml Extension | API Integration - TDPredictor.from_predictor Method - Teradata Vantage

Use the TDPredictor.from_predictor method to create TDPredictor from the SageMaker predictor object to allow for prediction using teradataml DataFrame and SageMaker endpoint represented by this predictor object.

Example

from tdapiclient import TDPredictor, create_tdapi_context
import sagemaker
from sagemaker.xgboost.estimator import XGBoost
from sagemaker.session import s3_input, Session

# Initialize hyperparameters
hyperparameters = {
        "max_depth":"5",
        "eta":"0.2",
        "gamma":"4",
        "min_child_weight":"6",
        "subsample":"0.7",
        "verbosity":"1",
        "objective":"reg:linear",
        "num_round":"50"}
 
    

# Set an output path where the trained model will be saved
bucket = sagemaker.Session().default_bucket()
prefix = 'DEMO-xgboost-as-a-framework'
output_path = 's3://{}/{}/{}/output'.format(bucket, prefix, 'abalone-xgb-framework')

# Construct a SageMaker XGBoost estimator
# Specify the entry_point to your xgboost training script
estimator = XGBoost(entry_point = "your_xgboost_abalone_script.py",
                    framework_version='1.0-1',
                    hyperparameters=hyperparameters,
                    role=sagemaker.get_execution_role(),
                    train_instance_count=1,
                    train_instance_type='ml.m5.2xlarge',
                    output_path=output_path)

# Define the data type and paths to the training and validation datasets
content_type = "csv"
train_input = s3_input("s3://{}/{}/{}/".format(bucket, prefix, 'train'), content_type=content_type)
validation_input = s3_input("s3://{}/{}/{}/".format(bucket, prefix, 'validation'), content_type=content_type)

# Execute the XGBoost training job
estimator.fit({'train': train_input, 'validation': validation_input})
sagemaker_predictor = estimator.deploy()
context = create_tdapi_context("aws", "s3_bucket")
tdsg_predictor = TDSagemakerPredictor.from_predictor(sagemaker_predictor, context)