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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_train.py
DESCRIPTION:
This sample demonstrates how to start a training job for a Conversation Authoring project
and print the final training result.
USAGE:
python sample_train.py
REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
AZURE_CONVERSATIONS_AUTHORING_ENDPOINT
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
NOTE:
If you want to use AzureKeyCredential instead, set:
- AZURE_CONVERSATIONS_AUTHORING_ENDPOINT
- AZURE_CONVERSATIONS_AUTHORING_KEY
OPTIONAL ENV VARS:
PROJECT_NAME # defaults to "<project-name>"
"""
# [START conversation_authoring_train]
import os
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations.authoring import ConversationAuthoringClient
from azure.ai.language.conversations.authoring.models import (
TrainingJobDetails,
TrainingMode,
EvaluationDetails,
EvaluationKind,
)
def sample_train():
# settings
endpoint = os.environ["AZURE_CONVERSATIONS_AUTHORING_ENDPOINT"]
project_name = os.environ.get("PROJECT_NAME", "<project-name>")
# create a client with AAD
credential = DefaultAzureCredential()
client = ConversationAuthoringClient(endpoint, credential=credential)
project_client = client.get_project_client(project_name)
# build training request
training_job_details = TrainingJobDetails(
model_label="<model-label>",
training_mode=TrainingMode.STANDARD,
training_config_version="<config-version>",
evaluation_options=EvaluationDetails(
kind=EvaluationKind.PERCENTAGE,
testing_split_percentage=20,
training_split_percentage=80,
),
)
# start training job (long-running operation)
poller = project_client.project.begin_train(body=training_job_details)
# wait for job completion and get the result (no explicit type variables)
result = poller.result()
# print result details
print("=== Training Result ===")
print(f"Model Label: {result.model_label}")
print(f"Training Config Version: {result.training_config_version}")
print(f"Training Mode: {result.training_mode}")
print(f"Training Status: {result.training_status}")
print(f"Data Generation Status: {result.data_generation_status}")
print(f"Evaluation Status: {result.evaluation_status}")
print(f"Estimated End: {result.estimated_end_on}")
# [END conversation_authoring_train]
def main():
sample_train()
if __name__ == "__main__":
main()
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