mirror of
https://github.com/BerriAI/litellm.git
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407 lines
13 KiB
Python
407 lines
13 KiB
Python
#########################################################################
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# /v1/fine_tuning Endpoints
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# Equivalent of https://platform.openai.com/docs/api-reference/fine-tuning
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##########################################################################
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import asyncio
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import traceback
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from datetime import datetime, timedelta, timezone
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from typing import List, Optional
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import fastapi
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import httpx
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from fastapi import (
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APIRouter,
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Depends,
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File,
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Form,
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Header,
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HTTPException,
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Request,
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Response,
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UploadFile,
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status,
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)
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import litellm
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from litellm._logging import verbose_proxy_logger
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from litellm.batches.main import FileObject
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from litellm.proxy._types import *
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from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
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router = APIRouter()
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from litellm.types.llms.openai import LiteLLMFineTuningJobCreate
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fine_tuning_config = None
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def set_fine_tuning_config(config):
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global fine_tuning_config
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if not isinstance(config, list):
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raise ValueError("invalid fine_tuning config, expected a list is not a list")
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for element in config:
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if isinstance(element, dict):
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for key, value in element.items():
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if isinstance(value, str) and value.startswith("os.environ/"):
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element[key] = litellm.get_secret(value)
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fine_tuning_config = config
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# Function to search for specific custom_llm_provider and return its configuration
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def get_fine_tuning_provider_config(
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custom_llm_provider: str,
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):
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global fine_tuning_config
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if fine_tuning_config is None:
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raise ValueError(
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"fine_tuning_config is not set, set it on your config.yaml file."
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)
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for setting in fine_tuning_config:
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if setting.get("custom_llm_provider") == custom_llm_provider:
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return setting
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return None
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@router.post(
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"/v1/fine_tuning/jobs",
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dependencies=[Depends(user_api_key_auth)],
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tags=["fine-tuning"],
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)
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@router.post(
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"/fine_tuning/jobs",
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dependencies=[Depends(user_api_key_auth)],
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tags=["fine-tuning"],
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)
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async def create_fine_tuning_job(
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request: Request,
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fastapi_response: Response,
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fine_tuning_request: LiteLLMFineTuningJobCreate,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Creates a fine-tuning job which begins the process of creating a new model from a given dataset.
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This is the equivalent of POST https://api.openai.com/v1/fine_tuning/jobs
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Supports Identical Params as: https://platform.openai.com/docs/api-reference/fine-tuning/create
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Example Curl:
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```
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curl http://localhost:4000/v1/fine_tuning/jobs \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer sk-1234" \
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-d '{
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"model": "gpt-3.5-turbo",
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"training_file": "file-abc123",
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"hyperparameters": {
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"n_epochs": 4
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}
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}'
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```
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"""
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from litellm.proxy.proxy_server import (
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add_litellm_data_to_request,
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general_settings,
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get_custom_headers,
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proxy_config,
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proxy_logging_obj,
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version,
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)
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try:
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# Convert Pydantic model to dict
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data = fine_tuning_request.model_dump(exclude_none=True)
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verbose_proxy_logger.debug(
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"Request received by LiteLLM:\n{}".format(json.dumps(data, indent=4)),
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)
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# Include original request and headers in the data
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data = await add_litellm_data_to_request(
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data=data,
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request=request,
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general_settings=general_settings,
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user_api_key_dict=user_api_key_dict,
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version=version,
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proxy_config=proxy_config,
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)
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# get configs for custom_llm_provider
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llm_provider_config = get_fine_tuning_provider_config(
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custom_llm_provider=fine_tuning_request.custom_llm_provider,
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)
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# add llm_provider_config to data
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data.update(llm_provider_config)
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response = await litellm.acreate_fine_tuning_job(**data)
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### ALERTING ###
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asyncio.create_task(
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proxy_logging_obj.update_request_status(
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litellm_call_id=data.get("litellm_call_id", ""), status="success"
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)
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)
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### RESPONSE HEADERS ###
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hidden_params = getattr(response, "_hidden_params", {}) or {}
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model_id = hidden_params.get("model_id", None) or ""
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cache_key = hidden_params.get("cache_key", None) or ""
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api_base = hidden_params.get("api_base", None) or ""
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fastapi_response.headers.update(
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get_custom_headers(
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user_api_key_dict=user_api_key_dict,
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model_id=model_id,
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cache_key=cache_key,
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api_base=api_base,
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version=version,
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model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
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)
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)
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return response
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except Exception as e:
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await proxy_logging_obj.post_call_failure_hook(
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user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
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)
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verbose_proxy_logger.error(
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"litellm.proxy.proxy_server.create_fine_tuning_job(): Exception occurred - {}".format(
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str(e)
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)
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)
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verbose_proxy_logger.debug(traceback.format_exc())
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if isinstance(e, HTTPException):
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raise ProxyException(
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message=getattr(e, "message", str(e.detail)),
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type=getattr(e, "type", "None"),
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param=getattr(e, "param", "None"),
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code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
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)
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else:
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error_msg = f"{str(e)}"
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raise ProxyException(
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message=getattr(e, "message", error_msg),
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type=getattr(e, "type", "None"),
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param=getattr(e, "param", "None"),
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code=getattr(e, "status_code", 500),
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)
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@router.get(
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"/v1/fine_tuning/jobs",
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dependencies=[Depends(user_api_key_auth)],
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tags=["fine-tuning"],
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)
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@router.get(
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"/fine_tuning/jobs",
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dependencies=[Depends(user_api_key_auth)],
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tags=["fine-tuning"],
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)
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async def list_fine_tuning_jobs(
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request: Request,
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fastapi_response: Response,
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custom_llm_provider: Literal["openai", "azure"],
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after: Optional[str] = None,
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limit: Optional[int] = None,
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user_api_key_dict: dict = Depends(user_api_key_auth),
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):
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"""
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Lists fine-tuning jobs for the organization.
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This is the equivalent of GET https://api.openai.com/v1/fine_tuning/jobs
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Supported Query Params:
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- `custom_llm_provider`: Name of the LiteLLM provider
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- `after`: Identifier for the last job from the previous pagination request.
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- `limit`: Number of fine-tuning jobs to retrieve (default is 20).
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"""
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from litellm.proxy.proxy_server import (
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add_litellm_data_to_request,
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general_settings,
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get_custom_headers,
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proxy_config,
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proxy_logging_obj,
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version,
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)
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data: dict = {}
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try:
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# Include original request and headers in the data
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data = await add_litellm_data_to_request(
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data=data,
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request=request,
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general_settings=general_settings,
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user_api_key_dict=user_api_key_dict,
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version=version,
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proxy_config=proxy_config,
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)
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# get configs for custom_llm_provider
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llm_provider_config = get_fine_tuning_provider_config(
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custom_llm_provider=custom_llm_provider
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)
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data.update(llm_provider_config)
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response = await litellm.alist_fine_tuning_jobs(
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**data,
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after=after,
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limit=limit,
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)
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### RESPONSE HEADERS ###
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hidden_params = getattr(response, "_hidden_params", {}) or {}
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model_id = hidden_params.get("model_id", None) or ""
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cache_key = hidden_params.get("cache_key", None) or ""
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api_base = hidden_params.get("api_base", None) or ""
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fastapi_response.headers.update(
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get_custom_headers(
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user_api_key_dict=user_api_key_dict,
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model_id=model_id,
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cache_key=cache_key,
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api_base=api_base,
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version=version,
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model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
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)
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)
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return response
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except Exception as e:
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await proxy_logging_obj.post_call_failure_hook(
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user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
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)
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verbose_proxy_logger.error(
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"litellm.proxy.proxy_server.list_fine_tuning_jobs(): Exception occurred - {}".format(
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str(e)
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)
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)
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verbose_proxy_logger.debug(traceback.format_exc())
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if isinstance(e, HTTPException):
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raise ProxyException(
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message=getattr(e, "message", str(e.detail)),
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type=getattr(e, "type", "None"),
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param=getattr(e, "param", "None"),
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code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
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)
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else:
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error_msg = f"{str(e)}"
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raise ProxyException(
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message=getattr(e, "message", error_msg),
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type=getattr(e, "type", "None"),
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param=getattr(e, "param", "None"),
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code=getattr(e, "status_code", 500),
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)
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@router.post(
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"/v1/fine_tuning/jobs/{fine_tuning_job_id:path}/cancel",
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dependencies=[Depends(user_api_key_auth)],
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tags=["fine-tuning"],
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)
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@router.post(
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"/fine_tuning/jobs/{fine_tuning_job_id:path}/cancel",
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dependencies=[Depends(user_api_key_auth)],
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tags=["fine-tuning"],
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)
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async def retrieve_fine_tuning_job(
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request: Request,
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fastapi_response: Response,
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fine_tuning_job_id: str,
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user_api_key_dict: dict = Depends(user_api_key_auth),
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):
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"""
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Cancel a fine-tuning job.
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This is the equivalent of POST https://api.openai.com/v1/fine_tuning/jobs/{fine_tuning_job_id}/cancel
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Supported Query Params:
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- `custom_llm_provider`: Name of the LiteLLM provider
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- `fine_tuning_job_id`: The ID of the fine-tuning job to cancel.
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"""
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from litellm.proxy.proxy_server import (
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add_litellm_data_to_request,
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general_settings,
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get_custom_headers,
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proxy_config,
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proxy_logging_obj,
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version,
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)
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data: dict = {}
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try:
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# Include original request and headers in the data
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data = await add_litellm_data_to_request(
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data=data,
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request=request,
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general_settings=general_settings,
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user_api_key_dict=user_api_key_dict,
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version=version,
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proxy_config=proxy_config,
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)
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request_body = await request.json()
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custom_llm_provider = request_body.get("custom_llm_provider", None)
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# get configs for custom_llm_provider
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llm_provider_config = get_fine_tuning_provider_config(
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custom_llm_provider=custom_llm_provider
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)
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data.update(llm_provider_config)
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response = await litellm.acancel_fine_tuning_job(
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**data,
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fine_tuning_job_id=fine_tuning_job_id,
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)
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### RESPONSE HEADERS ###
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hidden_params = getattr(response, "_hidden_params", {}) or {}
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model_id = hidden_params.get("model_id", None) or ""
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cache_key = hidden_params.get("cache_key", None) or ""
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api_base = hidden_params.get("api_base", None) or ""
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fastapi_response.headers.update(
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get_custom_headers(
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user_api_key_dict=user_api_key_dict,
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model_id=model_id,
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cache_key=cache_key,
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api_base=api_base,
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version=version,
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model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
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)
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)
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return response
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except Exception as e:
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await proxy_logging_obj.post_call_failure_hook(
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user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
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)
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verbose_proxy_logger.error(
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"litellm.proxy.proxy_server.list_fine_tuning_jobs(): Exception occurred - {}".format(
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str(e)
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)
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)
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verbose_proxy_logger.debug(traceback.format_exc())
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if isinstance(e, HTTPException):
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raise ProxyException(
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message=getattr(e, "message", str(e.detail)),
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type=getattr(e, "type", "None"),
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param=getattr(e, "param", "None"),
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code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
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)
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else:
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error_msg = f"{str(e)}"
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raise ProxyException(
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message=getattr(e, "message", error_msg),
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type=getattr(e, "type", "None"),
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param=getattr(e, "param", "None"),
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code=getattr(e, "status_code", 500),
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)
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