Build the conda env

 $ cd model_repository/sentiment
 $ sh build-env.sh

Start docker container:

   $ docker run -d --shm-size=10G -p8000:8000 -p8001:8001 -p8002:8002 --gpus device=0  -v $PWD/model_repository:/mnt/model_repository nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/mnt/model_repository --log-verbose=1

Hello example from Triton:

   $ curl --location --request POST 'http://localhost:8000/v2/models/hello/infer' \
     --header 'Content-Type: application/json' \
     --data-raw '{
        "inputs":[
        {    
         "name": "text",
         "shape": [1],
         "datatype": "BYTES",
         "data":  ["Hello world"]
        }
       ]
     }'  
     Response:{"model_name":"hello","model_version":"1","outputs":[{"name":"text","datatype":"BYTES","shape":[1],"data":["Hello from Triton Hello world"]}]}

HuggingFace model Inference using CURL:

     $ curl --location --request POST 'http://localhost:8000/v2/models/sentiment/infer' \
         --header 'Content-Type: application/json' \
         --data-raw '{
            "inputs":[
            {    
             "name": "text",
             "shape": [1],
             "datatype": "BYTES",
             "data":  ["I really enjoyed this"]
            }
           ]
         }'  

 Response:
     {"model_name":"sentiment","model_version":"1","outputs":[{"name":"sentiment","datatype":"BYTES","shape":[1],"data":["positive"]}]}