"""
Main File for Fine Tuning API implementation
https://platform.openai.com/docs/api-reference/fine-tuning
- fine_tuning.jobs.create()
- fine_tuning.jobs.list()
- client.fine_tuning.jobs.list_events()
"""
import asyncio
import contextvars
import os
from functools import partial
from typing import Any, Coroutine, Dict, Literal, Optional, Union
import httpx
import litellm
from litellm._logging import verbose_logger
from litellm.llms.azure.fine_tuning.handler import AzureOpenAIFineTuningAPI
from litellm.llms.openai.fine_tuning.handler import OpenAIFineTuningAPI
from litellm.llms.vertex_ai.fine_tuning.handler import VertexFineTuningAPI
from litellm.secret_managers.main import get_secret_str
from litellm.types.llms.openai import (
FineTuningJob,
FineTuningJobCreate,
Hyperparameters,
)
from litellm.types.router import *
from litellm.utils import client, supports_httpx_timeout
####### ENVIRONMENT VARIABLES ###################
openai_fine_tuning_apis_instance = OpenAIFineTuningAPI()
azure_fine_tuning_apis_instance = AzureOpenAIFineTuningAPI()
vertex_fine_tuning_apis_instance = VertexFineTuningAPI()
#################################################
@client
async def acreate_fine_tuning_job(
model: str,
training_file: str,
hyperparameters: Optional[dict] = {},
suffix: Optional[str] = None,
validation_file: Optional[str] = None,
integrations: Optional[List[str]] = None,
seed: Optional[int] = None,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
) -> FineTuningJob:
"""
Async: Creates and executes a batch from an uploaded file of request
"""
verbose_logger.debug(
"inside acreate_fine_tuning_job model=%s and kwargs=%s", model, kwargs
)
try:
loop = asyncio.get_event_loop()
kwargs["acreate_fine_tuning_job"] = True
# Use a partial function to pass your keyword arguments
func = partial(
create_fine_tuning_job,
model,
training_file,
hyperparameters,
suffix,
validation_file,
integrations,
seed,
custom_llm_provider,
extra_headers,
extra_body,
**kwargs,
)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
init_response = await loop.run_in_executor(None, func_with_context)
if asyncio.iscoroutine(init_response):
response = await init_response
else:
response = init_response # type: ignore
return response
except Exception as e:
raise e
@client
def create_fine_tuning_job(
model: str,
training_file: str,
hyperparameters: Optional[dict] = {},
suffix: Optional[str] = None,
validation_file: Optional[str] = None,
integrations: Optional[List[str]] = None,
seed: Optional[int] = None,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
) -> Union[FineTuningJob, Coroutine[Any, Any, FineTuningJob]]:
"""
Creates a fine-tuning job which begins the process of creating a new model from a given dataset.
Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete
"""
try:
_is_async = kwargs.pop("acreate_fine_tuning_job", False) is True
optional_params = GenericLiteLLMParams(**kwargs)
# handle hyperparameters
hyperparameters = hyperparameters or {} # original hyperparameters
_oai_hyperparameters: Hyperparameters = Hyperparameters(
**hyperparameters
) # Typed Hyperparameters for OpenAI Spec
### TIMEOUT LOGIC ###
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
# set timeout for 10 minutes by default
if (
timeout is not None
and isinstance(timeout, httpx.Timeout)
and supports_httpx_timeout(custom_llm_provider) is False
):
read_timeout = timeout.read or 600
timeout = read_timeout # default 10 min timeout
elif timeout is not None and not isinstance(timeout, httpx.Timeout):
timeout = float(timeout) # type: ignore
elif timeout is None:
timeout = 600.0
# OpenAI
if custom_llm_provider == "openai":
# for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
api_base = (
optional_params.api_base
or litellm.api_base
or os.getenv("OPENAI_API_BASE")
or "https://api.openai.com/v1"
)
organization = (
optional_params.organization
or litellm.organization
or os.getenv("OPENAI_ORGANIZATION", None)
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
)
# set API KEY
api_key = (
optional_params.api_key
or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or os.getenv("OPENAI_API_KEY")
)
create_fine_tuning_job_data = FineTuningJobCreate(
model=model,
training_file=training_file,
hyperparameters=_oai_hyperparameters,
suffix=suffix,
validation_file=validation_file,
integrations=integrations,
seed=seed,
)
create_fine_tuning_job_data_dict = create_fine_tuning_job_data.model_dump(
exclude_none=True
)
response = openai_fine_tuning_apis_instance.create_fine_tuning_job(
api_base=api_base,
api_key=api_key,
api_version=optional_params.api_version,
organization=organization,
create_fine_tuning_job_data=create_fine_tuning_job_data_dict,
timeout=timeout,
max_retries=optional_params.max_retries,
_is_async=_is_async,
client=kwargs.get(
"client", None
), # note, when we add this to `GenericLiteLLMParams` it impacts a lot of other tests + linting
)
# Azure OpenAI
elif custom_llm_provider == "azure":
api_base = optional_params.api_base or litellm.api_base or get_secret_str("AZURE_API_BASE") # type: ignore
api_version = (
optional_params.api_version
or litellm.api_version
or get_secret_str("AZURE_API_VERSION")
) # type: ignore
api_key = (
optional_params.api_key
or litellm.api_key
or litellm.azure_key
or get_secret_str("AZURE_OPENAI_API_KEY")
or get_secret_str("AZURE_API_KEY")
) # type: ignore
extra_body = optional_params.get("extra_body", {})
if extra_body is not None:
extra_body.pop("azure_ad_token", None)
else:
get_secret_str("AZURE_AD_TOKEN") # type: ignore
create_fine_tuning_job_data = FineTuningJobCreate(
model=model,
training_file=training_file,
hyperparameters=_oai_hyperparameters,
suffix=suffix,
validation_file=validation_file,
integrations=integrations,
seed=seed,
)
create_fine_tuning_job_data_dict = create_fine_tuning_job_data.model_dump(
exclude_none=True
)
response = azure_fine_tuning_apis_instance.create_fine_tuning_job(
api_base=api_base,
api_key=api_key,
api_version=api_version,
create_fine_tuning_job_data=create_fine_tuning_job_data_dict,
timeout=timeout,
max_retries=optional_params.max_retries,
_is_async=_is_async,
organization=optional_params.organization,
)
elif custom_llm_provider == "vertex_ai":
api_base = optional_params.api_base or ""
vertex_ai_project = (
optional_params.vertex_project
or litellm.vertex_project
or get_secret_str("VERTEXAI_PROJECT")
)
vertex_ai_location = (
optional_params.vertex_location
or litellm.vertex_location
or get_secret_str("VERTEXAI_LOCATION")
)
vertex_credentials = optional_params.vertex_credentials or get_secret_str(
"VERTEXAI_CREDENTIALS"
)
create_fine_tuning_job_data = FineTuningJobCreate(
model=model,
training_file=training_file,
hyperparameters=_oai_hyperparameters,
suffix=suffix,
validation_file=validation_file,
integrations=integrations,
seed=seed,
)
response = vertex_fine_tuning_apis_instance.create_fine_tuning_job(
_is_async=_is_async,
create_fine_tuning_job_data=create_fine_tuning_job_data,
vertex_credentials=vertex_credentials,
vertex_project=vertex_ai_project,
vertex_location=vertex_ai_location,
timeout=timeout,
api_base=api_base,
kwargs=kwargs,
original_hyperparameters=hyperparameters,
)
else:
raise litellm.exceptions.BadRequestError(
message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
custom_llm_provider
),
model="n/a",
llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=400,
content="Unsupported provider",
request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
return response
except Exception as e:
verbose_logger.error("got exception in create_fine_tuning_job=%s", str(e))
raise e
async def acancel_fine_tuning_job(
fine_tuning_job_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
) -> FineTuningJob:
"""
Async: Immediately cancel a fine-tune job.
"""
try:
loop = asyncio.get_event_loop()
kwargs["acancel_fine_tuning_job"] = True
# Use a partial function to pass your keyword arguments
func = partial(
cancel_fine_tuning_job,
fine_tuning_job_id,
custom_llm_provider,
extra_headers,
extra_body,
**kwargs,
)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
init_response = await loop.run_in_executor(None, func_with_context)
if asyncio.iscoroutine(init_response):
response = await init_response
else:
response = init_response # type: ignore
return response
except Exception as e:
raise e
def cancel_fine_tuning_job(
fine_tuning_job_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
) -> Union[FineTuningJob, Coroutine[Any, Any, FineTuningJob]]:
"""
Immediately cancel a fine-tune job.
Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete
"""
try:
optional_params = GenericLiteLLMParams(**kwargs)
### TIMEOUT LOGIC ###
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
# set timeout for 10 minutes by default
if (
timeout is not None
and isinstance(timeout, httpx.Timeout)
and supports_httpx_timeout(custom_llm_provider) is False
):
read_timeout = timeout.read or 600
timeout = read_timeout # default 10 min timeout
elif timeout is not None and not isinstance(timeout, httpx.Timeout):
timeout = float(timeout) # type: ignore
elif timeout is None:
timeout = 600.0
_is_async = kwargs.pop("acancel_fine_tuning_job", False) is True
# OpenAI
if custom_llm_provider == "openai":
# for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
api_base = (
optional_params.api_base
or litellm.api_base
or os.getenv("OPENAI_API_BASE")
or "https://api.openai.com/v1"
)
organization = (
optional_params.organization
or litellm.organization
or os.getenv("OPENAI_ORGANIZATION", None)
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
)
# set API KEY
api_key = (
optional_params.api_key
or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or os.getenv("OPENAI_API_KEY")
)
response = openai_fine_tuning_apis_instance.cancel_fine_tuning_job(
api_base=api_base,
api_key=api_key,
api_version=optional_params.api_version,
organization=organization,
fine_tuning_job_id=fine_tuning_job_id,
timeout=timeout,
max_retries=optional_params.max_retries,
_is_async=_is_async,
client=kwargs.get("client", None),
)
# Azure OpenAI
elif custom_llm_provider == "azure":
api_base = optional_params.api_base or litellm.api_base or get_secret("AZURE_API_BASE") # type: ignore
api_version = (
optional_params.api_version
or litellm.api_version
or get_secret_str("AZURE_API_VERSION")
) # type: ignore
api_key = (
optional_params.api_key
or litellm.api_key
or litellm.azure_key
or get_secret_str("AZURE_OPENAI_API_KEY")
or get_secret_str("AZURE_API_KEY")
) # type: ignore
extra_body = optional_params.get("extra_body", {})
if extra_body is not None:
extra_body.pop("azure_ad_token", None)
else:
get_secret_str("AZURE_AD_TOKEN") # type: ignore
response = azure_fine_tuning_apis_instance.cancel_fine_tuning_job(
api_base=api_base,
api_key=api_key,
api_version=api_version,
fine_tuning_job_id=fine_tuning_job_id,
timeout=timeout,
max_retries=optional_params.max_retries,
_is_async=_is_async,
organization=optional_params.organization,
)
else:
raise litellm.exceptions.BadRequestError(
message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
custom_llm_provider
),
model="n/a",
llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=400,
content="Unsupported provider",
request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
return response
except Exception as e:
raise e
async def alist_fine_tuning_jobs(
after: Optional[str] = None,
limit: Optional[int] = None,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
):
"""
Async: List your organization's fine-tuning jobs
"""
try:
loop = asyncio.get_event_loop()
kwargs["alist_fine_tuning_jobs"] = True
# Use a partial function to pass your keyword arguments
func = partial(
list_fine_tuning_jobs,
after,
limit,
custom_llm_provider,
extra_headers,
extra_body,
**kwargs,
)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
init_response = await loop.run_in_executor(None, func_with_context)
if asyncio.iscoroutine(init_response):
response = await init_response
else:
response = init_response # type: ignore
return response
except Exception as e:
raise e
def list_fine_tuning_jobs(
after: Optional[str] = None,
limit: Optional[int] = None,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
):
"""
List your organization's fine-tuning jobs
Params:
- after: Optional[str] = None, Identifier for the last job from the previous pagination request.
- limit: Optional[int] = None, Number of fine-tuning jobs to retrieve. Defaults to 20
"""
try:
optional_params = GenericLiteLLMParams(**kwargs)
### TIMEOUT LOGIC ###
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
# set timeout for 10 minutes by default
if (
timeout is not None
and isinstance(timeout, httpx.Timeout)
and supports_httpx_timeout(custom_llm_provider) is False
):
read_timeout = timeout.read or 600
timeout = read_timeout # default 10 min timeout
elif timeout is not None and not isinstance(timeout, httpx.Timeout):
timeout = float(timeout) # type: ignore
elif timeout is None:
timeout = 600.0
_is_async = kwargs.pop("alist_fine_tuning_jobs", False) is True
# OpenAI
if custom_llm_provider == "openai":
# for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
api_base = (
optional_params.api_base
or litellm.api_base
or os.getenv("OPENAI_API_BASE")
or "https://api.openai.com/v1"
)
organization = (
optional_params.organization
or litellm.organization
or os.getenv("OPENAI_ORGANIZATION", None)
or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
)
# set API KEY
api_key = (
optional_params.api_key
or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
or litellm.openai_key
or os.getenv("OPENAI_API_KEY")
)
response = openai_fine_tuning_apis_instance.list_fine_tuning_jobs(
api_base=api_base,
api_key=api_key,
api_version=optional_params.api_version,
organization=organization,
after=after,
limit=limit,
timeout=timeout,
max_retries=optional_params.max_retries,
_is_async=_is_async,
client=kwargs.get("client", None),
)
# Azure OpenAI
elif custom_llm_provider == "azure":
api_base = optional_params.api_base or litellm.api_base or get_secret_str("AZURE_API_BASE") # type: ignore
api_version = (
optional_params.api_version
or litellm.api_version
or get_secret_str("AZURE_API_VERSION")
) # type: ignore
api_key = (
optional_params.api_key
or litellm.api_key
or litellm.azure_key
or get_secret_str("AZURE_OPENAI_API_KEY")
or get_secret_str("AZURE_API_KEY")
) # type: ignore
extra_body = optional_params.get("extra_body", {})
if extra_body is not None:
extra_body.pop("azure_ad_token", None)
else:
get_secret("AZURE_AD_TOKEN") # type: ignore
response = azure_fine_tuning_apis_instance.list_fine_tuning_jobs(
api_base=api_base,
api_key=api_key,
api_version=api_version,
after=after,
limit=limit,
timeout=timeout,
max_retries=optional_params.max_retries,
_is_async=_is_async,
organization=optional_params.organization,
)
else:
raise litellm.exceptions.BadRequestError(
message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
custom_llm_provider
),
model="n/a",
llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=400,
content="Unsupported provider",
request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
return response
except Exception as e:
raise e
async def aretrieve_fine_tuning_job(
fine_tuning_job_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
) -> FineTuningJob:
"""
Async: Get info about a fine-tuning job.
"""
try:
loop = asyncio.get_event_loop()
kwargs["aretrieve_fine_tuning_job"] = True
# Use a partial function to pass your keyword arguments
func = partial(
retrieve_fine_tuning_job,
fine_tuning_job_id,
custom_llm_provider,
extra_headers,
extra_body,
**kwargs,
)
# Add the context to the function
ctx = contextvars.copy_context()
func_with_context = partial(ctx.run, func)
init_response = await loop.run_in_executor(None, func_with_context)
if asyncio.iscoroutine(init_response):
response = await init_response
else:
response = init_response # type: ignore
return response
except Exception as e:
raise e
def retrieve_fine_tuning_job(
fine_tuning_job_id: str,
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
) -> Union[FineTuningJob, Coroutine[Any, Any, FineTuningJob]]:
"""
Get info about a fine-tuning job.
"""
try:
optional_params = GenericLiteLLMParams(**kwargs)
### TIMEOUT LOGIC ###
timeout = optional_params.timeout or kwargs.get("request_timeout", 600) or 600
# set timeout for 10 minutes by default
if (
timeout is not None
and isinstance(timeout, httpx.Timeout)
and supports_httpx_timeout(custom_llm_provider) is False
):
read_timeout = timeout.read or 600
timeout = read_timeout # default 10 min timeout
elif timeout is not None and not isinstance(timeout, httpx.Timeout):
timeout = float(timeout) # type: ignore
elif timeout is None:
timeout = 600.0
_is_async = kwargs.pop("aretrieve_fine_tuning_job", False) is True
# OpenAI
if custom_llm_provider == "openai":
api_base = (
optional_params.api_base
or litellm.api_base
or os.getenv("OPENAI_API_BASE")
or "https://api.openai.com/v1"
)
organization = (
optional_params.organization
or litellm.organization
or os.getenv("OPENAI_ORGANIZATION", None)
or None
)
api_key = (
optional_params.api_key
or litellm.api_key
or litellm.openai_key
or os.getenv("OPENAI_API_KEY")
)
response = openai_fine_tuning_apis_instance.retrieve_fine_tuning_job(
api_base=api_base,
api_key=api_key,
api_version=optional_params.api_version,
organization=organization,
fine_tuning_job_id=fine_tuning_job_id,
timeout=timeout,
max_retries=optional_params.max_retries,
_is_async=_is_async,
client=kwargs.get("client", None),
)
# Azure OpenAI
elif custom_llm_provider == "azure":
api_base = optional_params.api_base or litellm.api_base or get_secret_str("AZURE_API_BASE") # type: ignore
api_version = (
optional_params.api_version
or litellm.api_version
or get_secret_str("AZURE_API_VERSION")
) # type: ignore
api_key = (
optional_params.api_key
or litellm.api_key
or litellm.azure_key
or get_secret_str("AZURE_OPENAI_API_KEY")
or get_secret_str("AZURE_API_KEY")
) # type: ignore
extra_body = optional_params.get("extra_body", {})
if extra_body is not None:
extra_body.pop("azure_ad_token", None)
else:
get_secret_str("AZURE_AD_TOKEN") # type: ignore
response = azure_fine_tuning_apis_instance.retrieve_fine_tuning_job(
api_base=api_base,
api_key=api_key,
api_version=api_version,
fine_tuning_job_id=fine_tuning_job_id,
timeout=timeout,
max_retries=optional_params.max_retries,
_is_async=_is_async,
organization=optional_params.organization,
)
else:
raise litellm.exceptions.BadRequestError(
message="LiteLLM doesn't support {} for 'retrieve_fine_tuning_job'. Only 'openai' and 'azure' are supported.".format(
custom_llm_provider
),
model="n/a",
llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=400,
content="Unsupported provider",
request=httpx.Request(method="retrieve_fine_tuning_job", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
return response
except Exception as e:
raise e
