update

Author: mrbullwinkle

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(
@@ -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(