tgi¶

Helm chart for deploying Hugging Face Text Generation Inference service.

Installing the Chart¶

To install the chart, run the following:

cd GenAIInfra/helm-charts/common
export MODELDIR=/mnt/opea-models
export MODELNAME="bigscience/bloom-560m"
export HFTOKEN="insert-your-huggingface-token-here"
helm install tgi tgi --set global.modelUseHostPath=${MODELDIR} --set LLM_MODEL_ID=${MODELNAME} --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN}
# To deploy on Gaudi enabled kubernetes cluster
# helm install tgi tgi --set global.modelUseHostPath=${MODELDIR} --set LLM_MODEL_ID=${MODELNAME} --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --values gaudi-values.yaml

By default, the tgi service will downloading the “bigscience/bloom-560m” which is about 1.1GB.

If you already cached the model locally, you can pass it to container like this example:

MODELDIR=/mnt/opea-models

MODELNAME=”/data/models–bigscience–bloom-560m”

Verify¶

To verify the installation, run the command kubectl get pod to make sure all pods are runinng.

Then run the command kubectl port-forward svc/tgi 2080:80 to expose the tgi service for access.

Open another terminal and run the following command to verify the service if working:

curl http://localhost:2080/generate \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
    -H 'Content-Type: application/json'

Values¶

Key	Type	Default	Description
LLM_MODEL_ID	string	`"bigscience/bloom-560m"`	Models id from https://huggingface.co/, or predownloaded model directory
global.HUGGINGFACEHUB_API_TOKEN	string	`insert-your-huggingface-token-here`	Hugging Face API token
global.modelUseHostPath	string	`"/mnt/opea-models"`	Cached models directory, tgi will not download if the model is cached here. The host path “modelUseHostPath” will be mounted to container as /data directory. Set this to null/empty will force it to download model.
image.repository	string	`"ghcr.io/huggingface/text-generation-inference"`
image.tag	string	`"1.4"`
horizontalPodAutoscaler.enabled	bool	false	Enable HPA autoscaling for the service deployment based on metrics it provides. See HPA section in ../../README.md before enabling!