Serve the Huggingface model using Triton Inference Runtime¶

Nvidia Triton Inference Server is a robust serving runtime thanks to its optmized performance, scalability, and flexibility. Combined with the expansive library of Hugging Face, which offers state-of-the-art natural language processing capabilities, it opens up immense possibilities for deploying production-ready Huggface Face transformer based models.

By harnessing the power of these tools, here we'll show you how KServe can help further simplify the Triton Inference containers deployment and make efficient use of GPUs by automatically wiring up the open inference protocol between pre/post processing(tokenization) and model inference on triton inference container.

Export the Model to Triton format¶

Export the Hugging Face models to supported model formats Torchscript or ONNX in triton model repository layout. For more details, please refer to triton model configuration.

Deploy InferenceService with Triton and Hugging Face Runtime¶

Create an InferenceService with triton predictor by specifying the storageUri with the Hugging Face model stored on cloud storage according to triton model repository layout. The KServe transformer container is created using the KServe Hugging Face runtime for the tokenization step to encode the text tokens and decode the token ids from the output the triton inference container. The Hugging Face tokenizing container and triton inference container can communicate with either REST or gRPC protocol by specifiying the --predictor_protocol=v2 or --predictor_protocol=grpc-v2.

kubectl apply -f - <<EOF
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
name: huggingface-triton
spec:
  predictor:
    model:
      args:
      - --log-verbose=1
      modelFormat:
        name: triton
      protocolVersion: v2
      resources:
        limits:
          cpu: "1"
          memory: 8Gi
          nvidia.com/gpu: "1"
        requests:
          cpu: "1"
          memory: 8Gi
          nvidia.com/gpu: "1"
      runtimeVersion: 23.10-py3
      storageUri: gs://kfserving-examples/models/triton/huggingface/model_repository
  transformer:
    containers:
    - args:
      - --model_name=bert
      - --model_id=bert-base-uncased
      - --predictor_protocol=v2
      - --tensor_input_names=input_ids
      image: kserve/huggingfaceserver:v0.13.0
      name: kserve-container
      resources:
        limits:
          cpu: "1"
          memory: 2Gi
        requests:
          cpu: 100m
          memory: 2Gi
EOF

Perform Model Inference¶

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT.

MODEL_NAME=bert
SERVICE_HOSTNAME=$(kubectl get inferenceservice huggingface-triton -o jsonpath='{.status.url}' | cut -d "/" -f 3)

Perform inference using v1 REST Protocol

curl -H "content-type:application/json" -H "Host: ${SERVICE_HOSTNAME}" -v http://${INGRESS_HOST}:${INGRESS_PORT}/v1/models/${MODEL_NAME}:predict -d '{"instances": ["The capital of france is [MASK]."] }'

   {"predictions":["paris"]}