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#########################################################################
# /v1/fine_tuning Endpoints
# Equivalent of https://platform.openai.com/docs/api-reference/fine-tuning
##########################################################################
import asyncio
import traceback
from typing import Optional
from fastapi import APIRouter, Depends, Request, Response
import litellm
from litellm._logging import verbose_proxy_logger
from litellm.proxy._types import *
from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
from litellm.proxy.utils import handle_exception_on_proxy
router = APIRouter()
from litellm.types.llms.openai import LiteLLMFineTuningJobCreate
fine_tuning_config = None
def set_fine_tuning_config(config):
if config is None:
return
global fine_tuning_config
if not isinstance(config, list):
raise ValueError("invalid fine_tuning config, expected a list is not a list")
for element in config:
if isinstance(element, dict):
for key, value in element.items():
if isinstance(value, str) and value.startswith("os.environ/"):
element[key] = litellm.get_secret(value)
fine_tuning_config = config
# Function to search for specific custom_llm_provider and return its configuration
def get_fine_tuning_provider_config(
custom_llm_provider: str,
):
global fine_tuning_config
if fine_tuning_config is None:
raise ValueError(
"fine_tuning_config is not set, set it on your config.yaml file."
)
for setting in fine_tuning_config:
if setting.get("custom_llm_provider") == custom_llm_provider:
return setting
return None
@router.post(
"/v1/fine_tuning/jobs",
dependencies=[Depends(user_api_key_auth)],
tags=["fine-tuning"],
summary="✨ (Enterprise) Create Fine-Tuning Job",
)
@router.post(
"/fine_tuning/jobs",
dependencies=[Depends(user_api_key_auth)],
tags=["fine-tuning"],
summary="✨ (Enterprise) Create Fine-Tuning Job",
)
async def create_fine_tuning_job(
request: Request,
fastapi_response: Response,
fine_tuning_request: LiteLLMFineTuningJobCreate,
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
"""
Creates a fine-tuning job which begins the process of creating a new model from a given dataset.
This is the equivalent of POST https://api.openai.com/v1/fine_tuning/jobs
Supports Identical Params as: https://platform.openai.com/docs/api-reference/fine-tuning/create
Example Curl:
```
curl http://localhost:4000/v1/fine_tuning/jobs \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-1234" \
-d '{
"model": "gpt-3.5-turbo",
"training_file": "file-abc123",
"hyperparameters": {
"n_epochs": 4
}
}'
```
"""
from litellm.proxy.proxy_server import (
add_litellm_data_to_request,
general_settings,
get_custom_headers,
premium_user,
proxy_config,
proxy_logging_obj,
version,
)
data = fine_tuning_request.model_dump(exclude_none=True)
try:
if premium_user is not True:
raise ValueError(
f"Only premium users can use this endpoint + {CommonProxyErrors.not_premium_user.value}"
)
# Convert Pydantic model to dict
verbose_proxy_logger.debug(
"Request received by LiteLLM:\n{}".format(json.dumps(data, indent=4)),
)
# 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=fine_tuning_request.custom_llm_provider,
)
# add llm_provider_config to data
if llm_provider_config is not None:
data.update(llm_provider_config)
response = await litellm.acreate_fine_tuning_job(**data)
### ALERTING ###
asyncio.create_task(
proxy_logging_obj.update_request_status(
litellm_call_id=data.get("litellm_call_id", ""), status="success"
)
)
### 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.create_fine_tuning_job(): Exception occurred - {}".format(
str(e)
)
)
verbose_proxy_logger.debug(traceback.format_exc())
raise handle_exception_on_proxy(e)
@router.get(
"/v1/fine_tuning/jobs/{fine_tuning_job_id:path}",
dependencies=[Depends(user_api_key_auth)],
tags=["fine-tuning"],
summary="✨ (Enterprise) Retrieve Fine-Tuning Job",
)
@router.get(
"/fine_tuning/jobs/{fine_tuning_job_id:path}",
dependencies=[Depends(user_api_key_auth)],
tags=["fine-tuning"],
summary="✨ (Enterprise) Retrieve Fine-Tuning Job",
)
async def retrieve_fine_tuning_job(
request: Request,
fastapi_response: Response,
fine_tuning_job_id: str,
custom_llm_provider: Literal["openai", "azure"],
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
"""
Retrieves a fine-tuning job.
This is the equivalent of GET https://api.openai.com/v1/fine_tuning/jobs/{fine_tuning_job_id}
Supported Query Params:
- `custom_llm_provider`: Name of the LiteLLM provider
- `fine_tuning_job_id`: The ID of the fine-tuning job to retrieve.
"""
from litellm.proxy.proxy_server import (
add_litellm_data_to_request,
general_settings,
get_custom_headers,
premium_user,
proxy_config,
proxy_logging_obj,
version,
)
data: dict = {}
try:
if premium_user is not True:
raise ValueError(
f"Only premium users can use this endpoint + {CommonProxyErrors.not_premium_user.value}"
)
# 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
)
if llm_provider_config is not None:
data.update(llm_provider_config)
response = await litellm.aretrieve_fine_tuning_job(
**data,
fine_tuning_job_id=fine_tuning_job_id,
)
### 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())
raise handle_exception_on_proxy(e)
@router.get(
"/v1/fine_tuning/jobs",
dependencies=[Depends(user_api_key_auth)],
tags=["fine-tuning"],
summary="✨ (Enterprise) List Fine-Tuning Jobs",
)
@router.get(
"/fine_tuning/jobs",
dependencies=[Depends(user_api_key_auth)],
tags=["fine-tuning"],
summary="✨ (Enterprise) List Fine-Tuning Jobs",
)
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: UserAPIKeyAuth = 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,
premium_user,
proxy_config,
proxy_logging_obj,
version,
)
data: dict = {}
try:
if premium_user is not True:
raise ValueError(
f"Only premium users can use this endpoint + {CommonProxyErrors.not_premium_user.value}"
)
# 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
)
if llm_provider_config is not None:
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())
raise handle_exception_on_proxy(e)
@router.post(
"/v1/fine_tuning/jobs/{fine_tuning_job_id:path}/cancel",
dependencies=[Depends(user_api_key_auth)],
tags=["fine-tuning"],
summary="✨ (Enterprise) Cancel Fine-Tuning Jobs",
)
@router.post(
"/fine_tuning/jobs/{fine_tuning_job_id:path}/cancel",
dependencies=[Depends(user_api_key_auth)],
tags=["fine-tuning"],
summary="✨ (Enterprise) Cancel Fine-Tuning Jobs",
)
async def cancel_fine_tuning_job(
request: Request,
fastapi_response: Response,
fine_tuning_job_id: str,
user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
):
"""
Cancel a fine-tuning job.
This is the equivalent of POST https://api.openai.com/v1/fine_tuning/jobs/{fine_tuning_job_id}/cancel
Supported Query Params:
- `custom_llm_provider`: Name of the LiteLLM provider
- `fine_tuning_job_id`: The ID of the fine-tuning job to cancel.
"""
from litellm.proxy.proxy_server import (
add_litellm_data_to_request,
general_settings,
get_custom_headers,
premium_user,
proxy_config,
proxy_logging_obj,
version,
)
data: dict = {}
try:
if premium_user is not True:
raise ValueError(
f"Only premium users can use this endpoint + {CommonProxyErrors.not_premium_user.value}"
)
# 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,
)
request_body = await request.json()
custom_llm_provider = request_body.get("custom_llm_provider", None)
# get configs for custom_llm_provider
llm_provider_config = get_fine_tuning_provider_config(
custom_llm_provider=custom_llm_provider
)
if llm_provider_config is not None:
data.update(llm_provider_config)
response = await litellm.acancel_fine_tuning_job(
**data,
fine_tuning_job_id=fine_tuning_job_id,
)
### 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())
raise handle_exception_on_proxy(e)
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