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Diffstat (limited to '.venv/lib/python3.12/site-packages/litellm/proxy/fine_tuning_endpoints')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/litellm/proxy/fine_tuning_endpoints/endpoints.py | 473 |
1 files changed, 473 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/litellm/proxy/fine_tuning_endpoints/endpoints.py b/.venv/lib/python3.12/site-packages/litellm/proxy/fine_tuning_endpoints/endpoints.py new file mode 100644 index 00000000..d4c4250b --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/proxy/fine_tuning_endpoints/endpoints.py @@ -0,0 +1,473 @@ +######################################################################### + +# /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.common_request_processing import ProxyBaseLLMRequestProcessing +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, + 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( + ProxyBaseLLMRequestProcessing.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, + 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( + ProxyBaseLLMRequestProcessing.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, + 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( + ProxyBaseLLMRequestProcessing.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, + 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( + ProxyBaseLLMRequestProcessing.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) |