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# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
FILE: sample_train_async.py
DESCRIPTION:
This sample demonstrates how to start a training job for a Conversation Authoring project (async)
and print the final training result.
USAGE:
python sample_train_async.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_async]
import os
import asyncio
from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations.authoring.aio import ConversationAuthoringClient
from azure.ai.language.conversations.authoring.models import (
TrainingJobDetails,
TrainingMode,
EvaluationDetails,
EvaluationKind,
)
async def sample_train_async():
# settings
endpoint = os.environ["AZURE_CONVERSATIONS_AUTHORING_ENDPOINT"]
project_name = os.environ.get("PROJECT_NAME", "<project-name>")
# async client with AAD
credential = DefaultAzureCredential()
async with ConversationAuthoringClient(endpoint, credential=credential) as client:
project_client = client.get_project_client(project_name)
# build training request
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 (async long-running operation)
poller = await project_client.project.begin_train(body=details)
# wait for job completion and get the result (no explicit type variables)
result = await poller.result()
# print result details (direct attribute access; no getattr)
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_async]
async def main():
await sample_train_async()
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
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