Build and deploy FaqGen Application on AMD GPU (ROCm)

Build images

Build the LLM Docker Image

### Cloning repo
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps

### Build Docker image
docker build -t opea/llm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/text-generation/tgi/Dockerfile .

🚀 Start Microservices and MegaService

Required Models

Default model is “meta-llama/Meta-Llama-3-8B-Instruct”. Change “LLM_MODEL_ID” in environment variables below if you want to use another model.

For gated models, you also need to provide HuggingFace token in “HUGGINGFACEHUB_API_TOKEN” environment variable.

Setup Environment Variables

Since the compose.yaml will consume some environment variables, you need to setup them in advance as below.

export FAQGEN_LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
export HOST_IP=${your_no_proxy}
export FAQGEN_TGI_SERVICE_PORT=8008
export FAQGEN_LLM_SERVER_PORT=9000
export FAQGEN_HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
export FAQGEN_BACKEND_SERVER_PORT=8888
export FAGGEN_UI_PORT=5173

Note: Please replace with host_ip with your external IP address, do not use localhost.

Note: In order to limit access to a subset of GPUs, please pass each device individually using one or more -device /dev/dri/rendered, where is the card index, starting from 128. (https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html#docker-restrict-gpus)

Example for set isolation for 1 GPU

      - /dev/dri/card0:/dev/dri/card0
      - /dev/dri/renderD128:/dev/dri/renderD128

Example for set isolation for 2 GPUs

      - /dev/dri/card0:/dev/dri/card0
      - /dev/dri/renderD128:/dev/dri/renderD128
      - /dev/dri/card0:/dev/dri/card0
      - /dev/dri/renderD129:/dev/dri/renderD129

Please find more information about accessing and restricting AMD GPUs in the link (https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html#docker-restrict-gpus)

Start Microservice Docker Containers

cd GenAIExamples/FaqGen/docker_compose/amd/gpu/rocm/
docker compose up -d

Validate Microservices

  1. TGI Service

    curl http://${host_ip}:8008/generate \
  -X POST \
  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
  -H 'Content-Type: application/json'

  2. LLM Microservice

    curl http://${host_ip}:9000/v1/faqgen \
  -X POST \
  -d '{"query":"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."}' \
  -H 'Content-Type: application/json'

  3. MegaService

    curl http://${host_ip}:8888/v1/faqgen \
-H "Content-Type: multipart/form-data" \
-F "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." \
-F "max_tokens=32" \
-F "stream=false"

    Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.

🚀 Launch the UI

Open this URL http://{host_ip}:5173 in your browser to access the frontend.

project-screenshot

🚀 Launch the React UI (Optional)

To access the FAQGen (react based) frontend, modify the UI service in the compose.yaml file. Replace faqgen-rocm-ui-server service with the faqgen-rocm-react-ui-server service as per the config below:

  faqgen-rocm-react-ui-server:
    image: opea/faqgen-react-ui:latest
    container_name: faqgen-rocm-react-ui-server
    environment:
      - no_proxy=${no_proxy}
      - https_proxy=${https_proxy}
      - http_proxy=${http_proxy}
    ports:
      - 5174:80
    depends_on:
      - faqgen-rocm-backend-server
    ipc: host
    restart: always

Open this URL http://{host_ip}:5174 in your browser to access the react based frontend.

  • Create FAQs from Text input project-screenshot

  • Create FAQs from Text Files project-screenshot