structure saas with tools
This commit is contained in:
Davidson Gomes
Binary file not shown.
757
.venv/lib/python3.10/site-packages/litellm/fine_tuning/main.py
Normal file
757
.venv/lib/python3.10/site-packages/litellm/fine_tuning/main.py
Normal file
@@ -0,0 +1,757 @@
|
||||
"""
|
||||
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
|
||||
Reference in New Issue
Block a user