add fine tuning for vertex

litellm/llms/fine_tuning_apis/vertex_ai.py

@@ -0,0 +1,113 @@
+import traceback
+from typing import Any, Coroutine, Optional, Union
+
+import httpx
+
+from litellm._logging import verbose_logger
+from litellm.llms.base import BaseLLM
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
+from litellm.llms.vertex_httpx import VertexLLM
+from litellm.types.llms.openai import FineTuningJobCreate
+from litellm.types.llms.vertex_ai import FineTuneJobCreate, FineTunesupervisedTuningSpec
+
+
+class VertexFineTuningAPI(VertexLLM):
+    """
+    Vertex methods to support for batches
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.async_handler = AsyncHTTPHandler(
+            timeout=httpx.Timeout(timeout=600.0, connect=5.0)
+        )
+
+    async def acreate_fine_tuning_job(
+        self,
+        fine_tuning_url: str,
+        headers: dict,
+        request_data: dict,
+    ):
+        try:
+            verbose_logger.debug(
+                "about to create fine tuning job: %s, request_data: %s",
+                fine_tuning_url,
+                request_data,
+            )
+            response = await self.async_handler.post(
+                headers=headers,
+                url=fine_tuning_url,
+                json=request_data,
+            )
+
+            if response.status_code != 200:
+                raise Exception(
+                    f"Error creating fine tuning job. Status code: {response.status_code}. Response: {response.text}"
+                )
+
+            verbose_logger.debug(
+                "got response from creating fine tuning job: %s", response.json()
+            )
+
+            return response
+
+        except Exception as e:
+            verbose_logger.error("asyncerror creating fine tuning job %s", e)
+            trace_back_str = traceback.format_exc()
+            verbose_logger.error(trace_back_str)
+            raise e
+
+    def create_fine_tuning_job(
+        self,
+        _is_async: bool,
+        create_fine_tuning_job_data: FineTuningJobCreate,
+        vertex_project: Optional[str],
+        vertex_location: Optional[str],
+        vertex_credentials: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+    ):
+
+        verbose_logger.debug(
+            "creating fine tuning job, args= %s", create_fine_tuning_job_data
+        )
+
+        auth_header, _ = self._get_token_and_url(
+            model=None,
+            gemini_api_key=None,
+            vertex_credentials=vertex_credentials,
+            vertex_project=vertex_project,
+            vertex_location=vertex_location,
+            stream=False,
+            custom_llm_provider="vertex_ai_beta",
+            api_base=api_base,
+        )
+
+        headers = {
+            "Authorization": f"Bearer {auth_header}",
+            "Content-Type": "application/json",
+        }
+
+        supervised_tuning_spec = FineTunesupervisedTuningSpec(
+            training_dataset_uri=create_fine_tuning_job_data.training_file
+        )
+        fine_tune_job = FineTuneJobCreate(
+            baseModel=create_fine_tuning_job_data.model,
+            supervisedTuningSpec=supervised_tuning_spec,
+            tunedModelDisplayName="ishaan-test",
+        )
+
+        fine_tuning_url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs"
+        if _is_async is True:
+            return self.acreate_fine_tuning_job(  # type: ignore
+                fine_tuning_url=fine_tuning_url,
+                headers=headers,
+                request_data=fine_tune_job,
+            )
+        # response = self.async_handler.post(
+        #     url=fine_tuning_url,
+        #     headers=headers,
+        #     json=fine_tune_job,
+        # )
+
+        # response = openai_client.fine_tuning.jobs.create(**create_fine_tuning_job_data)  # type: ignore