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Tutorial 1B: Batch scoring with the Python client

This tutorial showcases the use of H2O eScorer Python client to run the Batch Scorer. Batch scoring is built to read, score and write large amounts of datasets from storage. An example of a storage is AWS S3. In this tutorial, we will use a properties file with the eScorer Python client to score a .csv dataset from an AWS S3 bucket, and write results back to the same bucket.

Installing the Python client​

You can download the H2O eScorer Python client wheel from the Python client tab in the H2O eScorer downloads page.

In your Python environment, run the following command to install the package and its dependencies:

pip install <python-client-wheel-name>

Authentication​

H2O eScorer environment variables for authentication are set just once, and can be automatically used by the client for as many runs as you want.

Note

For more information about authenticating the Python client, see Python client overview: Authentication

In your Python environment, run the following to set the environment variables for the BE service and Keycloak service account:

export HAIC_ESCORER_URL = 'https://rest...'
export HAIC_AUTH_URL = 'https://auth...'
export HAIC_ESCORER_URL = '...'
export HAIC_ESCORER_URL = '...'
export HAIC_ESCORER_URL = '...'

Score​

In H2O eScorer, batch scoring can be performed easily with the properties file.

Note

For information on how to autogenerate and populate a properties file to configure batch scoring, see Batch scoring configuration and usage. 

import h2o_escorer 

client = h2o_escorer.Client()

await client.authenticate()

response = await client.batch_scorer(
    model_name='riskmodel.mojo',
    properties_filepath='s3_scorer.properties',
)

Now you have made predictions using the S3 dataset which is specified in the properties file using the model pipeline191.mojo. Results can be accessed from the response and describe the completion of scoring, number of threads, and the number of errors.

response['result']

ModelStats​

H2O eScorer Wave app provides a live dashboard with real-time updates to view ModelStats as models are scored in batch.

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