Skip to content

Latest commit

 

History

History
41 lines (31 loc) · 1.62 KB

README.md

File metadata and controls

41 lines (31 loc) · 1.62 KB
Raw

Deploy DocSum in Kubernetes Cluster

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

You can also customize the "MODEL_ID" and "model-volume"

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

Deploy On Xeon

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

Deploy On Gaudi

cd GenAIExamples/DocSum/kubernetes/intel/hpu/gaudi/manifests
export HUGGINGFACEHUB_API_TOKEN="YourOwnToken"
sed -i "s/insert-your-huggingface-token-here/${HUGGINGFACEHUB_API_TOKEN}/g" docsum.yaml
kubectl apply -f docsum.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/docsum 8888:8888 to expose the DocSum service for access.

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

curl http://localhost:8888/v1/docsum \
    -H 'Content-Type: application/json' \
    -d '{"messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'