update

Author: mrbullwinkle

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

@@ -241,7 +241,7 @@ print(response)
 response = client.fine_tuning.jobs.create(
     training_file=training_file_id,
     validation_file=validation_file_id,
-    model="gpt-35-turbo", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+    model="gpt-35-turbo-0613", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
 )
 
 job_id = response.id

articles/ai-services/openai/tutorials/fine-tune.md

@@ -47,12 +47,22 @@ In this tutorial you learn how to:
 
 ### Python libraries
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 If you haven't already, you need to install the following libraries:
 
 ```cmd
 pip install "openai==0.28.1" json requests os tiktoken time
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```cmd
+pip install openai json requests os tiktoken time
+```
+
+---
+
 [!INCLUDE [get-key-endpoint](../includes/get-key-endpoint.md)]
 
 ### Environment variables
@@ -273,6 +283,8 @@ p5 / p95: 11.6, 20.9
 
 ## Upload fine-tuning files
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```Python
 # Upload fine-tuning files
 import openai
@@ -302,6 +314,41 @@ print("Training file ID:", training_file_id)
 print("Validation file ID:", validation_file_id)
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+# Upload fine-tuning files
+
+import os
+from openai import AzureOpenAI
+
+client = AzureOpenAI(
+  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
+  api_key=os.getenv("AZURE_OPENAI_KEY"),  
+  api_version="2023-10-01-preview"  # This API version or later is required to access fine-tuning for turbo/babbage-002/davinci-002
+)
+
+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 = client.files.create(
+    file=open(training_file_name, "rb"), purpose="fine-tune"
+)
+training_file_id = training_response.id
+
+validation_response = client.files.create(
+    file=open(validation_file_name, "rb"), purpose="fine-tune"
+)
+validation_file_id = validation_response.id
+
+print("Training file ID:", training_file_id)
+print("Validation file ID:", validation_file_id)
+```
+
+---
+
 **Output:**
 
 ```output
@@ -313,6 +360,8 @@ Validation file ID: file-70a3f525ed774e78a77994d7a1698c4b
 
 Now that the fine-tuning files have been successfully uploaded you can submit your fine-tuning training job:
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 response = openai.FineTuningJob.create(
     training_file=training_file_id,
@@ -330,6 +379,27 @@ print("Status:", response["status"])
 print(response)
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+response = client.fine_tuning.jobs.create(
+    training_file=training_file_id,
+    validation_file=validation_file_id,
+    model="gpt-35-turbo-0613", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+)
+
+job_id = response.id
+
+# You can use the job ID to monitor the status of the fine-tuning job.
+# The fine-tuning job will take some time to start and complete.
+
+print("Job ID:", response.id)
+print("Status:", response.id)
+print(response.model_dump_json(indent=2))
+```
+
+---
+
 **Output:**
 
 ```output
@@ -350,26 +420,12 @@ Status: pending
 }
 ```
 
-To retrieve the training job ID, you can run:
-
-```python
-response = openai.FineTuningJob.retrieve(job_id)
-
-print("Job ID:", response["id"])
-print("Status:", response["status"])
-print(response)
-```
-
-**Output:**
-
-```output
-Fine-tuning model with job ID: ftjob-0f4191f0c59a4256b7a797a3d9eed219.
-```
-
 ## Track training job status
 
 If you would like to poll the training job status until it's complete, you can run:
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 # Track training status
 
@@ -402,6 +458,42 @@ response = openai.FineTuningJob.list()
 print(f'Found {len(response["data"])} fine-tune jobs.')
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+# Track training status
+
+from IPython.display import clear_output
+import time
+
+start_time = time.time()
+
+# Get the status of our fine-tuning job.
+response = client.fine_tuning.jobs.retrieve(job_id)
+
+status = response.status
+
+# If the job isn't done yet, poll it every 10 seconds.
+while status not in ["succeeded", "failed"]:
+    time.sleep(10)
+    
+    response = client.fine_tuning.jobs.retrieve(job_id)
+    print(response.model_dump_json(indent=2))
+    print("Elapsed time: {} minutes {} seconds".format(int((time.time() - start_time) // 60), int((time.time() - start_time) % 60)))
+    status = response.status
+    print(f'Status: {status}')
+    clear_output(wait=True)
+
+print(f'Fine-tuning job {job_id} finished with status: {status}')
+
+# List all fine-tuning jobs for this resource.
+print('Checking other fine-tune jobs for this resource.')
+response = client.files.list().data
+print(f'Found {len(response["data"])} fine-tune jobs.')
+```
+
+---
+
 **Output:**
 
 ```ouput
@@ -432,6 +524,8 @@ Found 2 fine-tune jobs.
 
 To get the full results, run the following:
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 #Retrieve fine_tuned_model name
 
@@ -441,6 +535,19 @@ print(response)
 fine_tuned_model = response["fine_tuned_model"]
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+#Retrieve fine_tuned_model name
+
+response = client.fine_tuning.jobs.retrieve(job_id)
+
+print(response.model_dump_json(indent=2))
+fine_tuned_model = response.fine_tuned_model
+```
+
+---
+
 ## Deploy fine-tuned model
 
 Unlike the previous Python SDK commands in this tutorial, since the introduction of the quota feature, model deployment must be done using the [REST API](/rest/api/cognitiveservices/accountmanagement/deployments/create-or-update?tabs=HTTP), which requires separate authorization, a different API path, and a different API version.
@@ -504,6 +611,8 @@ It isn't uncommon for this process to take some time to complete when dealing wi
 
 After your fine-tuned model is deployed, you can use it like any other deployed model in either the [Chat Playground of Azure OpenAI Studio](https://oai.azure.com), or via the chat completion API. For example, you can send a chat completion call to your deployed model, as shown in the following Python example. You can continue to use the same parameters with your customized model, such as temperature and max_tokens, as you can with other deployed models.
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 #Note: The openai-python library support for Azure OpenAI is in preview.
 import os
@@ -527,6 +636,33 @@ print(response)
 print(response['choices'][0]['message']['content'])
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+import os
+from openai import AzureOpenAI
+
+client = AzureOpenAI(
+  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
+  api_key=os.getenv("AZURE_OPENAI_KEY"),  
+  api_version="2023-05-15"
+)
+
+response = client.chat.completions.create(
+    model="gpt-35-turbo-ft", # model = "Custom deployment name you chose for your fine-tuning model"
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
+        {"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."},
+        {"role": "user", "content": "Do other Azure AI services support this too?"}
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+---
+
 ## Delete deployment
 
 Unlike other types of Azure OpenAI models, fine-tuned/customized models have [an hourly hosting cost](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/#pricing) associated with them once they are deployed. It is **strongly recommended** that once you're done with this tutorial and have tested a few chat completion calls against your fine-tuned model, that you **delete the model deployment**.