tdapiclient 1.0.0.0

Teradata API Client Python package

tdapiclient - Teradata Third Party Analytics Integration Python Library

tdapiclient Python library allows AWS SageMaker and Teradata users to use AWS SageMaker Python library's interface to train/predict using teradataml DataFrame. tdapiclient will transparantly convert teradataml DataFrame in S3 address to be used for training and it will also allow user to use teradataml DataFrame as input for inference.

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Table of Contents

• Release Notes
• Installation and Requirements
• Using the tdapiclient Python Package
• Documentation
• License

Release Notes

tdapiclient 1.00

• tdapiclient 1.00.00.00 is the first release version. Please refer to the API Integration Guide for Cloud Machine Learning for a list of Limitations and Usage Considerations.

Installation and Requirements

Package Requirements

• Python 3.6 or later

Note: 32-bit Python is not supported.

Minimum System Requirements

• Windows 7 (64Bit) or later
• macOS 10.9 (64Bit) or later
• Red Hat 7 or later versions
• Ubuntu 16.04 or later versions
• CentOS 7 or later versions
• SLES 12 or later versions
• Teradata Vantage Advanced SQL Engine:
  • Advanced SQL Engine 17.05 Feature Update 1 or later

Installation

Use pip to install the tdapiclient - Teradata Sagemaker Python Library

Platform	Command
macOS/Linux	pip install tdapiclient
Windows	py -3 -m pip install tdapiclient

Using the tdapiclient Python Package

Your Python script must import the tdapiclient package in order to use the tdapiclient Python Library

>>> from tdapiclient import create_aws_context,TDApiClient
>>> from teradataml import create_context, DataFrame, copy_to_sql

>>> # Create connection to Teradata Vantage System
>>> host = input("Host: ")
>>> username = input("Username: ")
>>> password = getpass.getpass("Password: ")
>>> td_context = create_context(host=host, username=username, password=password)

# Create AWS Context to be used in TDApiClient
>>> s3_bucket = input("S3 Bucket(Please give just the bucket name) :")
>>> access_id = input("Access ID:")
>>> access_key = getpass.getpass("Acess Key: ")
>>> region = input("AWS Region: ")

>>>   os.environ["AWS_ACCESS_KEY_ID"] = access_id
>>>   os.environ["AWS_SECRET_ACCESS_KEY"] = access_key
>>>   os.environ["AWS_REGION"] = region

>>> aws_context = create_tdapi_context("aws", bucket_name=s3_bucket)
# Create TDApiClient Instance
>>> td_apiclient = TDApiClient(aws_context)

# Load data in teradata tables
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.datasets import fetch_california_housing

>>> data = fetch_california_housing()
>>> X_train, X_test, y_train, y_test = train_test_split(
     data.data, data.target, test_size=0.25, random_state=42)

>>> trainX = pd.DataFrame(X_train, columns=data.feature_names)
>>> trainX["target"] = y_train

>>> testX = pd.DataFrame(X_test, columns=data.feature_names)
>>> testX["target"] = y_test

>>> train_table = "housing_data_train"
>>> test_table = "housing_data_test"

>>> column_types = {"MedInc": FLOAT, "HouseAge": FLOAT,
                "AveRooms": FLOAT, "AveBedrms": FLOAT, "Population": FLOAT,
                "AveOccup": FLOAT, "Latitude": FLOAT, "Longitude": FLOAT,
                "target" : FLOAT}

>>> copy_to_sql(df=trainX, table_name=train_table, if_exists="replace", types=column_types)
>>> copy_to_sql(df=testX, table_name=test_table, if_exists="replace", types=column_types)

# Create teradataml DataFrame for input tables

>>> test_df = DataFrame(table_name=test_table)
>>> train_df = DataFrame(table_name=train_table)

>>> exec_role_arn = "arn:aws:iam::XX:role/service-role/AmazonSageMaker-ExecutionRole-20210112T215668"
>>> FRAMEWORK_VERSION = "0.23-1"
# Create an estimator object based on sklearn sagemaker class
>>> sklearn_estimator = td_apiclient.SKLearn(
    entry_point="sklearn-script.py",
    role=exec_role_arn,
    instance_count=1,
    instance_type="ml.m5.large",
    framework_version=FRAMEWORK_VERSION,
    base_job_name="rf-scikit",
    metric_definitions=[{"Name": "median-AE", "Regex": "AE-at-50th-percentile: ([0-9.]+).*$"}],
    hyperparameters={
        "n-estimators": 100,
        "min-samples-leaf": 3,
        "features": "MedInc HouseAge AveRooms AveBedrms Population AveOccup Latitude Longitude",
        "target": "target",
    },
)
>>> # Start training using DataFrame objects
>>> sklearn_estimator.fit({"train": test_df, "test": train_df}, content_type="csv", wait=True)

>>> from sagemaker.serializers import CSVSerializer
>>> from sagemaker.deserializers import CSVDeserializer
>>> csv_ser = CSVSerializer()
>>> csv_dser = CSVDeserializer()
>>> predictor = sklearn_estimator.deploy(instance_type="ml.m5.large", initial_instance_count=1,serializer=csv_ser,  deserializer=csv_dser)

>>> # Now let's try prediction with UDF and Client options.
>>> input = DataFrame(table_name='housing_data_test')
>>> column_list = ["MedInc","HouseAge","AveRooms","AveBedrms","Population","AveOccup","Latitude","Longitude"]
>>> input = input.sample(n=5).select(column_list)

>>> output = predictor.predict(input, mode="UDF",content_type='csv')

Documentation

General product information, including installation instructions, is available in the Teradata Documentation website

License

Use of the Teradata Python Package is governed by the License Agreement for the Teradata Sagemaker Python Library. After installation, the LICENSE and LICENSE-3RD-PARTY files are located in the tdapiclient directory of the Python installation directory.