tei¶
Helm chart for deploying Hugging Face Text Generation Inference service.
Installing the Chart¶
To install the chart, run the following:
cd ${GenAIInfro_repo}/helm-charts/common
export MODELDIR=/mnt/opea-models
export MODELNAME="BAAI/bge-base-en-v1.5"
helm install tei tei --set global.modelUseHostPath=${MODELDIR} --set EMBEDDING_MODEL_ID=${MODELNAME}
By default, the tei service will downloading the “BAAI/bge-base-en-v1.5” 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/BAAI/bge-base-en-v1.5”
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/tei 2081:80
to expose the tei service for access.
Open another terminal and run the following command to verify the service if working:
curl http://localhost:2081/embed -X POST -d '{"inputs":"What is Deep Learning?"}' -H 'Content-Type: application/json'
Values¶
Key |
Type |
Default |
Description |
---|---|---|---|
EMBEDDING_MODEL_ID |
string |
|
Models id from https://huggingface.co/, or predownloaded model directory |
global.modelUseHostPath |
string |
|
Cached models directory, tei 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 |
|
|
image.tag |
string |
|
|
horizontalPodAutoscaler.enabled |
bool |
false |
Enable HPA autoscaling for the service deployment based on metrics it provides. See HPA section before enabling! |