aissemble-open-inference-protocol-use-case-example

This use case repository is to show that when wrapping the YOLO model with aissemble-open-inference-protocol (OIP), we can easily deploy to different endpoints (KServe, Fastapi, and gRPC) with minimal changes.

Each service requires a handler and a service. When using the aissemble open inference protocol, we can define one handler and pass to different endpoint service class.

Following deploy steps will show how easy that we can switch to use different service with minimal code changes.

• Clone and build the project
• Deploy KServe
• Deploy FastAPI
• Deploy gRPC

Clone and build the project

git clone [email protected]:boozallen/aissemble-open-inference-protocol-use-case-example.git

Deploy KServe

Preparation

pyproject.toml

Due to dependency conflicts, comment out the aissemble-open-inference-protocol-fastapi and aissemble-open-inference-protocol-grpc dependencies and uncomment aissemble-open-inference-protocol-kserve as following:

# comment out both FastAPI and gRPC endpoint packages if using kserve because of dependencies conflict
#aissemble-open-inference-protocol-fastapi = "1.1.0.*"
#aissemble-open-inference-protocol-grpc = "1.1.0.*"

# uncomment to switch to kserve endpoints
aissemble-open-inference-protocol-kserve = "1.1.0.*"

main.py

1. Comment out the code for FastAPI and gRPC from line #16 to line #37 as following:

   ## comment out to start() function when starting kserve
   ## start of FastAPI and gRPC functions definitions
   
   # import asyncio
   # from aissemble_open_inference_protocol_fastapi.aissemble_oip_fastapi import (
   #     AissembleOIPFastAPI,
   # )
   #
   # from aissemble_open_inference_protocol_grpc.aissemble_oip_grpc import AissembleOIPgRPC
   #
   #
   # def get_oip_fastapi(oip_handler) -> AissembleOIPFastAPI:
   #     return AissembleOIPFastAPI(oip_handler)
   #
   #
   # def get_oip_grpc(oip_handler) -> AissembleOIPgRPC:
   #     return AissembleOIPgRPC(oip_handler)
   #
   #
   # async def start(oip_handler, model_name):
   #     app = get_oip_fastapi(oip_handler)
   #     # app = get_oip_grpc(oip_handler)
   #
   #     app.model_load(model_name)
   #     await app.start_server()
   
   ## end of FastAPI and gRPC functions definitions

2. Uncomment KServe functions definitions from line #44 to line #56

   ## uncomment when starting kserve
   # start of kserve function definition
   from aissemble_open_inference_protocol_kserve.aissemble_oip_kserve import (
       AissembleOIPKServe,
   )
   
   
   def start_oip_kserve(oip_handler, model_name):
       kserve = AissembleOIPKServe(
           name=model_name,
           model_handler=oip_handler,
       )
       # model needs to be loaded before server start
       kserve.load()
       kserve.start_server()
   ## end of kserve function definition

3. Comment out the FastAPI and gRPC start function (line #66), uncomment KServe start (line #69) function, as following:

       # comment out this line when starting kserve
       # asyncio.run(start(oip_handler, model_name))
   
       # uncomment this line when starting kserve
       start_oip_kserve(oip_handler, model_name)

Build

mvn clean install

Deploy

Navigate to the deploy folder and follow the deploy/README documentation.

Deploy FastAPI

Preparation

pyproject.toml

Due to dependency conflicts, comment out the aissemble-open-inference-protocol-kserve and uncomment aissemble-open-inference-protocol-grpc and aissemble-open-inference-protocol-fastapi dependencies as following:

# comment out both FastAPI and gRPC endpoint packages if using kserve because of dependencies conflict
aissemble-open-inference-protocol-fastapi = "1.1.0.*"
aissemble-open-inference-protocol-grpc = "1.1.0.*"

# uncomment to switch to kserve endpoints
# aissemble-open-inference-protocol-kserve = "1.1.0.*"

main.py

1. Comment out KServe functions definitions from line #44 to line #56, as following:

   ## uncomment when starting kserve
   # start of kserve function definition
   # from aissemble_open_inference_protocol_kserve.aissemble_oip_kserve import (
   #     AissembleOIPKServe,
   # )
   # 
   # 
   # def start_oip_kserve(oip_handler, model_name):
   #     kserve = AissembleOIPKServe(
   #         name=model_name,
   #         model_handler=oip_handler,
   #     )
   #     # model needs to be loaded before server start
   #     kserve.load()
   #     kserve.start_server()
   ## end of kserve function definition

2. Uncomment the code for FastAPI and gRPC from line #16 to line #37 as following:

   ## comment out to start() function when starting kserve
   ## start of FastAPI and gRPC functions definitions
   
   import asyncio
   from aissemble_open_inference_protocol_fastapi.aissemble_oip_fastapi import (
       AissembleOIPFastAPI,
   )
   
   from aissemble_open_inference_protocol_grpc.aissemble_oip_grpc import AissembleOIPgRPC
   
   
   def get_oip_fastapi(oip_handler) -> AissembleOIPFastAPI:
       return AissembleOIPFastAPI(oip_handler)
   
   
   def get_oip_grpc(oip_handler) -> AissembleOIPgRPC:
       return AissembleOIPgRPC(oip_handler)
   
   
   async def start(oip_handler, model_name):
       app = get_oip_fastapi(oip_handler)
       # app = get_oip_grpc(oip_handler)
   
       app.model_load(model_name)
       await app.start_server()
   
   ## end of FastAPI and gRPC functions definitions

3. Use AissembleOIPFastAPI - Ensure the app (line #33) was using the AissembleOIPFastAPI service

   async def start(oip_handler, model_name):
       app = get_oip_fastapi(oip_handler)
       # app = get_oip_grpc(oip_handler)
   
       app.model_load(model_name)
       await app.start_server()

4. Uncomment out the FastAPI and gRPC start function (line #66), comment out KServe start (line #69) function, as following:

       # comment out this line when starting kserve
       asyncio.run(start(oip_handler, model_name))
   
       # uncomment this line when starting kserve
       #start_oip_kserve(oip_handler, model_name)

Build

When deploy to use FastAPI, the image isn't required.

mvn clean install -Pskip-docker

Deploy

Deploy the Application

1. Run start server command at the project root directory.

   poetry run run_server

2. Upload data and monitor results

   curl -w "\nHTTP Code: %{http_code}\n" \
   -H 'Content-Type: application/json' \
   -d '{"id" : "2214",
        "inputs" : [{
        "name" : "sample",
        "shape" : [3],
        "datatype"  : "BYTES",
        "data" : ["src/resources/images/cat.png","src/resources/images/bus.png","https://ultralytics.com/images/bus.jpg"]
       }]}' \
   -X POST \
   http://127.0.0.1:8082/v2/models/yolo11n/infer   

Deploy gRPC

Preparation

pyproject.toml

Due to dependency conflicts, comment out the aissemble-open-inference-protocol-kserve and uncomment aissemble-open-inference-protocol-grpc and aissemble-open-inference-protocol-fastapi dependencies as following:

# comment out both FastAPI and gRPC endpoint packages if using kserve because of dependencies conflict
aissemble-open-inference-protocol-fastapi = "1.1.0.*"
aissemble-open-inference-protocol-grpc = "1.1.0.*"

# uncomment to switch to kserve endpoints
# aissemble-open-inference-protocol-kserve = "1.1.0.*"

main.py

2. Comment out KServe functions definitions from line #44 to line #56, as following:

   ## uncomment when starting kserve
   # start of kserve function definition
   # from aissemble_open_inference_protocol_kserve.aissemble_oip_kserve import (
   #     AissembleOIPKServe,
   # )
   # 
   # 
   # def start_oip_kserve(oip_handler, model_name):
   #     kserve = AissembleOIPKServe(
   #         name=model_name,
   #         model_handler=oip_handler,
   #     )
   #     # model needs to be loaded before server start
   #     kserve.load()
   #     kserve.start_server()
   ## end of kserve function definition

3. Uncomment the code for FastAPI and gRPC from line #16 to line #37 as following:

   ## comment out to start() function when starting kserve
   ## start of FastAPI and gRPC functions definitions
   
   import asyncio
   from aissemble_open_inference_protocol_fastapi.aissemble_oip_fastapi import (
       AissembleOIPFastAPI,
   )
   
   from aissemble_open_inference_protocol_grpc.aissemble_oip_grpc import AissembleOIPgRPC
   
   
   def get_oip_fastapi(oip_handler) -> AissembleOIPFastAPI:
       return AissembleOIPFastAPI(oip_handler)
   
   
   def get_oip_grpc(oip_handler) -> AissembleOIPgRPC:
       return AissembleOIPgRPC(oip_handler)
   
   
   async def start(oip_handler, model_name):
       app = get_oip_fastapi(oip_handler)
       # app = get_oip_grpc(oip_handler)
   
       app.model_load(model_name)
       await app.start_server()
   
   ## end of FastAPI and gRPC functions definitions

4. Use AissembleOIPgRPC - Ensure the app (line #34) was using the AissembleOIPgRPC service

   async def start(oip_handler, model_name):
       #app = get_oip_fastapi(oip_handler)
       app = get_oip_grpc(oip_handler)
   
       app.model_load(model_name)
       await app.start_server()

5. Uncomment out the FastAPI and gRPC start function (line #66), comment out KServe start (line #69) function, as following:

       # comment out this line when starting kserve
       asyncio.run(start(oip_handler, model_name))
   
       # uncomment this line when starting kserve
       #start_oip_kserve(oip_handler, model_name)

Build

When deploy to use gRPC, the image isn't required.

mvn clean install -Pskip-docker

Deploy

Deploy the Application

1. Run start server command at the project root directory.

   poetry run run_server

2. Upload data and monitor results

   grpcurl -plaintext \
   -import-path proto/ \
   -proto grpc_inference_service.proto \
   -d '{
       "model_name": "default",
       "model_version": "1.0",
       "id": "test_request",
       "inputs": [{
       "name": "input",
       "datatype": "BYTES",
       "shape": [3],
       "contents": {"bytes_contents": ["c3JjL3Jlc291cmNlcy9pbWFnZXMvY2F0LnBuZw==", "c3JjL3Jlc291cmNlcy9pbWFnZXMvYnVzLnBuZw==", "aHR0cHM6Ly91bHRyYWx5dGljcy5jb20vaW1hZ2VzL2J1cy5qcGc="]}
       }],
   "outputs": [{"name": "output"}]
   }' \
   localhost:8081 inference.GrpcInferenceService/ModelInfer   

   Note: gRPC requires the inputs data in encoded state so the image path has been encoded using base64 (i.e.: echo -n "src/resources/images/cat.png" | base64)

Run FastAPI with authentication enabled

In this example we will enable authentication and attempt to make an inference call using a generated JWT token. The token will include an authorized username, which will permit users to call inference.
Part of this example is running an Authzforce service, which will be handled by the launch example code (step 4).

1. Run the pyproject.toml step from the Deploy FastAPI section, but !!!IMPORTANT!!! Stop after the pyproject.toml section.
2. Edit src/resources/krausening/base/oip.properties and change auth_enabled=false to auth_enabled=true
3. Run the build mvn clean install -Pskip-docker Note: if you get a build error, try deleting the poetry.lock file.
4. Start the aiSSEMBLE OIP FastAPI endpoint and the Authzforce server

   python ./src/aissemble_open_inference_protocol_use_case/launch_fastapi_auth_example.py

5. In another terminal, run the following command to generate a JWT token and store it in the OIP_JWT environment variable (will be used in the inference request)

   export OIP_JWT=$(python src/aissemble_open_inference_protocol_use_case/generate_simple_jwt.py | jq -r .jwt)

6. Once the services are up and ready (step 4), run a curl command with the JWT token stored in OIP_JWT

   curl -X "POST" -w "\nHTTP Code: %{http_code}\n" \
   "http://127.0.0.1:8000/v2/models/yolo11n/infer" \
   -H 'Content-Type: application/json' \
   -H "Authorization: Bearer $OIP_JWT" \
   -d '{"id" : "2214",
   "inputs" : [{
   "name" : "sample",
   "shape" : [3],
   "datatype"  : "BYTES",
   "data" : ["src/resources/images/cat.png","src/resources/images/bus.png","https://ultralytics.com/images/bus.jpg"]
       }]}'

7. You should get a 200 response code and an inference for the images sent.