README.md

Deploy ChatQnA in Kubernetes Cluster

[NOTE] The following values must be set before you can deploy: HUGGINGFACEHUB_API_TOKEN

You can also customize the "MODEL_ID" if needed.

You need to make sure you have created the directory /mnt/opea-models to save the cached model on the node where the ChatQnA workload is running. Otherwise, you need to modify the chatqna.yaml file to change the model-volume to a directory that exists on the node.

Deploy On Xeon

cd GenAIExamples/ChatQnA/kubernetes/intel/cpu/xeon/manifests
export HUGGINGFACEHUB_API_TOKEN="YourOwnToken"
sed -i "s/insert-your-huggingface-token-here/${HUGGINGFACEHUB_API_TOKEN}/g" chatqna.yaml
kubectl apply -f chatqna.yaml

Newer CPUs such as Intel Cooper Lake, Sapphire Rapids, support bfloat16 data type. If you have such CPUs, and given model supports bfloat16, adding --dtype bfloat16 argument for huggingface/text-generation-inference server halves its memory usage and speeds it a bit. To use it, run the following commands:

# label your node for scheduling the service on it automatically
kubectl label node 'your-node-name' node-type=node-bfloat16

# add `nodeSelector` for the `huggingface/text-generation-inference` server at `chatqna_bf16.yaml`
# create
kubectl apply -f chatqna_bf16.yaml

Deploy On Gaudi

cd GenAIExamples/ChatQnA/kubernetes/intel/hpu/gaudi/manifests
export HUGGINGFACEHUB_API_TOKEN="YourOwnToken"
sed -i "s/insert-your-huggingface-token-here/${HUGGINGFACEHUB_API_TOKEN}/g" chatqna.yaml
kubectl apply -f chatqna.yaml

Verify Services

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

Then run the command kubectl port-forward svc/chatqna 8888:8888 to expose the ChatQnA service for access.

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

curl http://localhost:8888/v1/chatqna \
    -H 'Content-Type: application/json' \
    -d '{"messages": "What is the revenue of Nike in 2023?"}'