Merge pull request #3646 from mrbullwinkle/mrb_03_20_2025_fine_tuning_update

[Azure OpenAI] fine-tuning updates

Author: prmerger-automator[bot]

articles/ai-services/openai/includes/fine-tuning-python.md

@@ -110,8 +110,6 @@ For large data files, we recommend that you import from an Azure Blob  store. La
 
 The following Python example uploads local training and validation files by using the Python SDK, and retrieves the returned file IDs.
 
-# [OpenAI Python 1.x](#tab/python-new)
-
 ```python
 # Upload fine-tuning files
 
@@ -144,42 +142,6 @@ print("Validation file ID:", validation_file_id)
 
 ```
 
-# [OpenAI Python 0.28.1](#tab/python)
-
-[!INCLUDE [Deprecation](../includes/deprecation.md)]
-
-```python
-# Upload fine-tuning files
-
-import openai
-import os
-
-openai.api_key = os.getenv("AZURE_OPENAI_API_KEY") 
-openai.api_base =  os.getenv("AZURE_OPENAI_ENDPOINT")
-openai.api_type = 'azure'
-openai.api_version = '2024-02-01' # This API version or later is required
-
-training_file_name = 'training_set.jsonl'
-validation_file_name = 'validation_set.jsonl'
-
-# Upload the training and validation dataset files to Azure OpenAI with the SDK.
-
-training_response = openai.File.create(
-    file=open(training_file_name, "rb"), purpose="fine-tune", user_provided_filename="training_set.jsonl"
-)
-training_file_id = training_response["id"]
-
-validation_response = openai.File.create(
-    file=open(validation_file_name, "rb"), purpose="fine-tune", user_provided_filename="validation_set.jsonl"
-)
-validation_file_id = validation_response["id"]
-
-print("Training file ID:", training_file_id)
-print("Validation file ID:", validation_file_id)
-```
-
----
-
 ## Create a customized model
 
 After you upload your training and validation files, you're ready to start the fine-tuning job.
@@ -205,7 +167,6 @@ print("Job ID:", response.id)
 print("Status:", response.id)
 print(response.model_dump_json(indent=2))
 ```
----
 
 You can also pass additional optional parameters like hyperparameters to take greater control of the fine-tuning process. For initial training we recommend using the automatic defaults that are present without specifying these parameters. 
 
@@ -226,7 +187,7 @@ from openai import AzureOpenAI
 client = AzureOpenAI(
   azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
   api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-  api_version="2024-02-01"  # This API version or later is required
+  api_version="2024-10-21"  # This API version or later is required
 )
 
 client.fine_tuning.jobs.create(
@@ -265,7 +226,7 @@ When each training epoch completes a checkpoint is generated. A checkpoint is a
 You can run the list checkpoints command to retrieve the list of checkpoints associated with an individual fine-tuning job. You might need to upgrade your OpenAI client library to the latest version with `pip install openai --upgrade` to run this command.
 
 ```python
-response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id, limit=10)
+response = client.fine_tuning.jobs.checkpoints.list(job_id)
 print(response.model_dump_json(indent=2))
 ```
 
@@ -336,7 +297,7 @@ resource_group = "<YOUR_RESOURCE_GROUP_NAME>"
 resource_name = "<YOUR_AZURE_OPENAI_RESOURCE_NAME>"
 model_deployment_name ="gpt-35-turbo-ft" # custom deployment name that you will use to reference the model when making inference calls.
 
-deploy_params = {'api-version': "2024-10-21"} 
+deploy_params = {'api-version': "2024-10-01"} # control plane API version rather than dataplane API for this call 
 deploy_headers = {'Authorization': 'Bearer {}'.format(token), 'Content-Type': 'application/json'}
 
 deploy_data = {
@@ -378,7 +339,7 @@ from openai import AzureOpenAI
 client = AzureOpenAI(
   azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
   api_key=os.getenv("AZURE_OPENAI_API_KEY"),  
-  api_version="2024-02-01"  
+  api_version="2024-10-21"  
 )
 
 response = client.fine_tuning.jobs.create(