add GET fine_tuning/jobs

litellm/proxy/fine_tuning_endpoints/endpoints.py

@@ -138,7 +138,6 @@ async def create_fine_tuning_job(
         # add llm_provider_config to data
         data.update(llm_provider_config)
 
-        # For now, use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for fine-tuning
         response = await litellm.acreate_fine_tuning_job(**data)
 
         ### ALERTING ###
@@ -191,3 +190,105 @@ async def create_fine_tuning_job(
                 param=getattr(e, "param", "None"),
                 code=getattr(e, "status_code", 500),
             )
+
+
+@router.get(
+    "/v1/fine_tuning/jobs",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["fine-tuning"],
+)
+async def list_fine_tuning_jobs(
+    request: Request,
+    fastapi_response: Response,
+    custom_llm_provider: Literal["openai", "azure"],
+    after: Optional[str] = None,
+    limit: Optional[int] = None,
+    user_api_key_dict: dict = Depends(user_api_key_auth),
+):
+    """
+    Lists fine-tuning jobs for the organization.
+    This is the equivalent of GET https://api.openai.com/v1/fine_tuning/jobs
+
+    Supported Query Params:
+    - `custom_llm_provider`: Name of the LiteLLM provider
+    - `after`: Identifier for the last job from the previous pagination request.
+    - `limit`: Number of fine-tuning jobs to retrieve (default is 20).
+    """
+    from litellm.proxy.proxy_server import (
+        add_litellm_data_to_request,
+        general_settings,
+        get_custom_headers,
+        proxy_config,
+        proxy_logging_obj,
+        version,
+    )
+
+    data: dict = {}
+    try:
+        # Include original request and headers in the data
+        data = await add_litellm_data_to_request(
+            data=data,
+            request=request,
+            general_settings=general_settings,
+            user_api_key_dict=user_api_key_dict,
+            version=version,
+            proxy_config=proxy_config,
+        )
+
+        # get configs for custom_llm_provider
+        llm_provider_config = get_fine_tuning_provider_config(
+            custom_llm_provider=custom_llm_provider
+        )
+
+        data.update(llm_provider_config)
+
+        response = await litellm.alist_fine_tuning_jobs(
+            **data,
+            after=after,
+            limit=limit,
+        )
+
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers.update(
+            get_custom_headers(
+                user_api_key_dict=user_api_key_dict,
+                model_id=model_id,
+                cache_key=cache_key,
+                api_base=api_base,
+                version=version,
+                model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+            )
+        )
+
+        return response
+
+    except Exception as e:
+        await proxy_logging_obj.post_call_failure_hook(
+            user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
+        )
+        verbose_proxy_logger.error(
+            "litellm.proxy.proxy_server.list_fine_tuning_jobs(): Exception occurred - {}".format(
+                str(e)
+            )
+        )
+        verbose_proxy_logger.debug(traceback.format_exc())
+        if isinstance(e, HTTPException):
+            raise ProxyException(
+                message=getattr(e, "message", str(e.detail)),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
+            )
+        else:
+            error_msg = f"{str(e)}"
+            raise ProxyException(
+                message=getattr(e, "message", error_msg),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", 500),
+            )

litellm/proxy/proxy_config.yaml

@@ -38,7 +38,7 @@ finetune_settings:
 # for /files endpoints
 files_settings:
   - custom_llm_provider: azure
-    api_base: http://0.0.0.0:8090
+    api_base: https://exampleopenaiendpoint-production.up.railway.app
     api_key: fake-key
     api_version: "2023-03-15-preview"
   - custom_llm_provider: openai

tests/test_openai_fine_tuning.py

@@ -10,14 +10,44 @@ async def test_openai_fine_tuning():
     """
     client = AsyncOpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
 
-    file_name = "openai_batch_completions.jsonl"
+    # file_name = "openai_batch_completions.jsonl"
-    _current_dir = os.path.dirname(os.path.abspath(__file__))
+    # _current_dir = os.path.dirname(os.path.abspath(__file__))
-    file_path = os.path.join(_current_dir, file_name)
+    # file_path = os.path.join(_current_dir, file_name)
 
-    response = await client.files.create(
+    # response = await client.files.create(
-        extra_body={"custom_llm_provider": "azure"},
+    #     extra_body={"custom_llm_provider": "azure"},
-        file=open(file_path, "rb"),
+    #     file=open(file_path, "rb"),
-        purpose="fine-tune",
+    #     purpose="fine-tune",
+    # )
+
+    # print("response from files.create: {}".format(response))
+
+    # # create fine tuning job
+
+    # ft_job = await client.fine_tuning.jobs.create(
+    #     model="gpt-35-turbo-1106",
+    #     training_file=response.id,
+    #     extra_body={"custom_llm_provider": "azure"},
+    # )
+
+    # print("response from ft job={}".format(ft_job))
+
+    # # response from example endpoint
+    # assert ft_job.id == "file-abc123"
+
+    # get fine tuning job
+    # specific_ft_job = await client.fine_tuning.jobs.retrieve(
+    #     fine_tuning_job_id="123",
+    #     extra_body={"custom_llm_provider": "azure"},
+    # )
+
+    # list all fine tuning jobs
+    list_ft_jobs = await client.fine_tuning.jobs.list(
+        extra_query={"custom_llm_provider": "azure"}
     )
 
-    print("response from files.create: {}".format(response))
+    # cancel specific fine tuning job
+    cancel_ft_job = await client.fine_tuning.jobs.cancel(
+        fine_tuning_job_id="123",
+        extra_body={"custom_llm_provider": "azure"},
+    )