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add GET fine_tuning/jobs

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This commit is contained in:
Ishaan Jaff 2024-07-31 15:58:35 -07:00
parent 287b09cff6
commit 9b6231810b
3 changed files with 141 additions and 10 deletions
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103
litellm/proxy/fine_tuning_endpoints/endpoints.py
View file

@ -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),
)

2
litellm/proxy/proxy_config.yaml
View file

@ -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

46
tests/test_openai_fine_tuning.py
View file

@ -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"
_current_dir = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(_current_dir, file_name)
# file_name = "openai_batch_completions.jsonl"
# _current_dir = os.path.dirname(os.path.abspath(__file__))
# file_path = os.path.join(_current_dir, file_name)
response = await client.files.create(
extra_body={"custom_llm_provider": "azure"},
file=open(file_path, "rb"),
purpose="fine-tune",
# response = await client.files.create(
# extra_body={"custom_llm_provider": "azure"},
# file=open(file_path, "rb"),
# 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"},
)