update finetuning api with openai format. (#535)

* update finetuning api with openai format.

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* update doc and use 8001 port.

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---------

Co-authored-by: test <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: root <[email protected]>

comps/cores/mega/micro_service.py

@@ -3,7 +3,7 @@
 
 import asyncio
 import multiprocessing
-from typing import Any, Optional, Type
+from typing import Any, List, Optional, Type
 
 from ..proto.docarray import TextDoc
 from .constants import ServiceRoleType, ServiceType
@@ -154,25 +154,27 @@ def register_microservice(
     output_datatype: Type[Any] = TextDoc,
     provider: Optional[str] = None,
     provider_endpoint: Optional[str] = None,
+    methods: List[str] = ["POST"],
 ):
     def decorator(func):
-        micro_service = MicroService(
-            name=name,
-            service_role=service_role,
-            service_type=service_type,
-            protocol=protocol,
-            host=host,
-            port=port,
-            ssl_keyfile=ssl_keyfile,
-            ssl_certfile=ssl_certfile,
-            endpoint=endpoint,
-            input_datatype=input_datatype,
-            output_datatype=output_datatype,
-            provider=provider,
-            provider_endpoint=provider_endpoint,
-        )
-        micro_service.app.router.add_api_route(endpoint, func, methods=["POST"])
-        opea_microservices[name] = micro_service
+        if name not in opea_microservices:
+            micro_service = MicroService(
+                name=name,
+                service_role=service_role,
+                service_type=service_type,
+                protocol=protocol,
+                host=host,
+                port=port,
+                ssl_keyfile=ssl_keyfile,
+                ssl_certfile=ssl_certfile,
+                endpoint=endpoint,
+                input_datatype=input_datatype,
+                output_datatype=output_datatype,
+                provider=provider,
+                provider_endpoint=provider_endpoint,
+            )
+            opea_microservices[name] = micro_service
+        opea_microservices[name].app.router.add_api_route(endpoint, func, methods=methods)
         return func
 
     return decorator

comps/cores/proto/api_protocol.py

@@ -279,3 +279,225 @@ def check_requests(request) -> Optional[JSONResponse]:
         )
 
     return None
+
+
+class Hyperparameters(BaseModel):
+    batch_size: Optional[Union[Literal["auto"], int]] = "auto"
+    """Number of examples in each batch.
+
+    A larger batch size means that model parameters are updated less frequently, but with lower variance.
+    """
+
+    learning_rate_multiplier: Optional[Union[Literal["auto"], float]] = "auto"
+    """Scaling factor for the learning rate.
+
+    A smaller learning rate may be useful to avoid overfitting.
+    """
+
+    n_epochs: Optional[Union[Literal["auto"], int]] = "auto"
+    """The number of epochs to train the model for.
+
+    An epoch refers to one full cycle through the training dataset. "auto" decides
+    the optimal number of epochs based on the size of the dataset. If setting the
+    number manually, we support any number between 1 and 50 epochs.
+    """
+
+
+class FineTuningJobWandbIntegration(BaseModel):
+    project: str
+    """The name of the project that the new run will be created under."""
+
+    entity: Optional[str] = None
+    """The entity to use for the run.
+
+    This allows you to set the team or username of the WandB user that you would
+    like associated with the run. If not set, the default entity for the registered
+    WandB API key is used.
+    """
+
+    name: Optional[str] = None
+    """A display name to set for the run.
+
+    If not set, we will use the Job ID as the name.
+    """
+
+    tags: Optional[List[str]] = None
+    """A list of tags to be attached to the newly created run.
+
+    These tags are passed through directly to WandB. Some default tags are generated
+    by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".
+    """
+
+
+class FineTuningJobWandbIntegrationObject(BaseModel):
+    type: Literal["wandb"]
+    """The type of the integration being enabled for the fine-tuning job."""
+
+    wandb: FineTuningJobWandbIntegration
+    """The settings for your integration with Weights and Biases.
+
+    This payload specifies the project that metrics will be sent to. Optionally, you
+    can set an explicit display name for your run, add tags to your run, and set a
+    default entity (team, username, etc) to be associated with your run.
+    """
+
+
+class FineTuningJobsRequest(BaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/fine-tuning/create
+    model: str
+    """The name of the model to fine-tune."""
+
+    training_file: str
+    """The ID of an uploaded file that contains training data."""
+
+    hyperparameters: Optional[Hyperparameters] = None
+    """The hyperparameters used for the fine-tuning job."""
+
+    suffix: Optional[str] = None
+    """A string of up to 64 characters that will be added to your fine-tuned model name."""
+
+    validation_file: Optional[str] = None
+    """The ID of an uploaded file that contains validation data."""
+
+    integrations: Optional[List[FineTuningJobWandbIntegrationObject]] = None
+    """A list of integrations to enable for your fine-tuning job."""
+
+    seed: Optional[str] = None
+
+
+class Error(BaseModel):
+    code: str
+    """A machine-readable error code."""
+
+    message: str
+    """A human-readable error message."""
+
+    param: Optional[str] = None
+    """The parameter that was invalid, usually `training_file` or `validation_file`.
+
+    This field will be null if the failure was not parameter-specific.
+    """
+
+
+class FineTuningJob(BaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/fine-tuning/object
+    id: str
+    """The object identifier, which can be referenced in the API endpoints."""
+
+    created_at: int
+    """The Unix timestamp (in seconds) for when the fine-tuning job was created."""
+
+    error: Optional[Error] = None
+    """For fine-tuning jobs that have `failed`, this will contain more information on
+    the cause of the failure."""
+
+    fine_tuned_model: Optional[str] = None
+    """The name of the fine-tuned model that is being created.
+
+    The value will be null if the fine-tuning job is still running.
+    """
+
+    finished_at: Optional[int] = None
+    """The Unix timestamp (in seconds) for when the fine-tuning job was finished.
+
+    The value will be null if the fine-tuning job is still running.
+    """
+
+    hyperparameters: Hyperparameters
+    """The hyperparameters used for the fine-tuning job.
+
+    See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
+    for more details.
+    """
+
+    model: str
+    """The base model that is being fine-tuned."""
+
+    object: Literal["fine_tuning.job"] = "fine_tuning.job"
+    """The object type, which is always "fine_tuning.job"."""
+
+    organization_id: Optional[str] = None
+    """The organization that owns the fine-tuning job."""
+
+    result_files: List[str] = None
+    """The compiled results file ID(s) for the fine-tuning job.
+
+    You can retrieve the results with the
+    [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
+    """
+
+    status: Literal["validating_files", "queued", "running", "succeeded", "failed", "cancelled"]
+    """The current status of the fine-tuning job, which can be either
+    `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`."""
+
+    trained_tokens: Optional[int] = None
+    """The total number of billable tokens processed by this fine-tuning job.
+
+    The value will be null if the fine-tuning job is still running.
+    """
+
+    training_file: str
+    """The file ID used for training.
+
+    You can retrieve the training data with the
+    [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
+    """
+
+    validation_file: Optional[str] = None
+    """The file ID used for validation.
+
+    You can retrieve the validation results with the
+    [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
+    """
+
+    integrations: Optional[List[FineTuningJobWandbIntegrationObject]] = None
+    """A list of integrations to enable for this fine-tuning job."""
+
+    seed: Optional[int] = None
+    """The seed used for the fine-tuning job."""
+
+    estimated_finish: Optional[int] = None
+    """The Unix timestamp (in seconds) for when the fine-tuning job is estimated to
+    finish.
+
+    The value will be null if the fine-tuning job is not running.
+    """
+
+
+class FineTuningJobIDRequest(BaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/fine-tuning/retrieve
+    # https://platform.openai.com/docs/api-reference/fine-tuning/cancel
+    fine_tuning_job_id: str
+    """The ID of the fine-tuning job."""
+
+
+class FineTuningJobListRequest(BaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/fine-tuning/list
+    after: Optional[str] = None
+    """Identifier for the last job from the previous pagination request."""
+
+    limit: Optional[int] = 20
+    """Number of fine-tuning jobs to retrieve."""
+
+
+class FineTuningJobList(BaseModel):
+    # Ordered by official OpenAI API documentation
+    # https://platform.openai.com/docs/api-reference/fine-tuning/list
+    object: str = "list"
+    """The object type, which is always "list".
+
+    This indicates that the returned data is a list of fine-tuning jobs.
+    """
+
+    data: List[FineTuningJob]
+    """A list containing FineTuningJob objects."""
+
+    has_more: bool
+    """Indicates whether there are more fine-tuning jobs beyond the current list.
+
+    If true, additional requests can be made to retrieve more jobs.
+    """

comps/finetuning/README.md

@@ -60,7 +60,7 @@ docker build -t opea/finetuning:latest --build-arg https_proxy=$https_proxy --bu
 Start docker container with below command:
 
 ```bash
-docker run -d --name="finetuning-server" -p 8000:8000 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/finetuning:latest
+docker run -d --name="finetuning-server" -p 8001:8001 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/finetuning:latest
 ```
 
 # 🚀3. Consume Finetuning Service
@@ -70,11 +70,25 @@ docker run -d --name="finetuning-server" -p 8000:8000 --runtime=runc --ipc=host
 Assuming a training file `alpaca_data.json` is uploaded, it can be downloaded in [here](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json), the following script launches a finetuning job using `meta-llama/Llama-2-7b-chat-hf` as base model:
 
 ```bash
-curl http://${your_ip}:8000/v1/fine_tuning/jobs \
+# create a finetuning job
+curl http://${your_ip}:8001/v1/fine_tuning/jobs \
   -X POST \
   -H "Content-Type: application/json" \
   -d '{
     "training_file": "alpaca_data.json",
     "model": "meta-llama/Llama-2-7b-chat-hf"
   }'
+
+# list finetuning jobs
+curl http://${your_ip}:8001/v1/fine_tuning/jobs   -X GET
+
+# retrieve one finetuning job
+curl http://localhost:8001/v1/fine_tuning/jobs/retrieve   -X POST   -H "Content-Type: application/json"   -d '{
+    "fine_tuning_job_id": ${fine_tuning_job_id}}'
+
+# cancel one finetuning job
+
+curl http://localhost:8001/v1/fine_tuning/jobs/cancel   -X POST   -H "Content-Type: application/json"   -d '{
+    "fine_tuning_job_id": ${fine_tuning_job_id}}'
+
 ```

comps/finetuning/finetuning_service.py

@@ -1,41 +1,45 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import uvicorn
-from fastapi import BackgroundTasks, FastAPI
+from fastapi import BackgroundTasks
 
+from comps import opea_microservices, register_microservice
+from comps.cores.proto.api_protocol import FineTuningJobIDRequest, FineTuningJobsRequest
 from comps.finetuning.handlers import (
     handle_cancel_finetuning_job,
     handle_create_finetuning_jobs,
     handle_list_finetuning_jobs,
     handle_retrieve_finetuning_job,
 )
-from comps.finetuning.models import FineTuningJob, FineTuningJobList, FineTuningJobsRequest
 
-app = FastAPI()
 
-
-@app.post("/v1/fine_tuning/jobs", response_model=FineTuningJob)
+@register_microservice(name="opea_service@finetuning", endpoint="/v1/fine_tuning/jobs", host="0.0.0.0", port=8001)
 def create_finetuning_jobs(request: FineTuningJobsRequest, background_tasks: BackgroundTasks):
     return handle_create_finetuning_jobs(request, background_tasks)
 
 
-@app.get("/v1/fine_tuning/jobs", response_model=FineTuningJobList)
+@register_microservice(
+    name="opea_service@finetuning", endpoint="/v1/fine_tuning/jobs", host="0.0.0.0", port=8001, methods=["GET"]
+)
 def list_finetuning_jobs():
     return handle_list_finetuning_jobs()
 
 
-@app.get("/v1/fine_tuning/jobs/{fine_tuning_job_id}", response_model=FineTuningJob)
-def retrieve_finetuning_job(fine_tuning_job_id):
-    job = handle_retrieve_finetuning_job(fine_tuning_job_id)
+@register_microservice(
+    name="opea_service@finetuning", endpoint="/v1/fine_tuning/jobs/retrieve", host="0.0.0.0", port=8001
+)
+def retrieve_finetuning_job(request: FineTuningJobIDRequest):
+    job = handle_retrieve_finetuning_job(request)
     return job
 
 
-@app.post("/v1/fine_tuning/jobs/{fine_tuning_job_id}/cancel", response_model=FineTuningJob)
-def cancel_finetuning_job(fine_tuning_job_id):
-    job = handle_cancel_finetuning_job(fine_tuning_job_id)
+@register_microservice(
+    name="opea_service@finetuning", endpoint="/v1/fine_tuning/jobs/cancel", host="0.0.0.0", port=8001
+)
+def cancel_finetuning_job(request: FineTuningJobIDRequest):
+    job = handle_cancel_finetuning_job(request)
     return job
 
 
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    opea_microservices["opea_service@finetuning"].start()