Add compose example for FaqGen AMD ROCm

FaqGen/docker_compose/amd/gpu/rocm/README.md

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+# Build and deploy FaqGen Application on AMD GPU (ROCm)
+
+## Build images
+
+### Build the LLM Docker Image
+
+```bash
+### 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](https://huggingface.co/docs/hub/security-tokens) 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.
+
+```bash
+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<node>, where <node> 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
+
+```bash
+cd GenAIExamples/FaqGen/docker_compose/amd/gpu/rocm/
+docker compose up -d
+```
+
+### Validate Microservices
+
+1. TGI Service
+
+   ```bash
+   curl http://${host_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
+
+2. LLM Microservice
+
+   ```bash
+   curl http://${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
+
+   ```bash
+   curl http://${host_ip}:8888/v1/faqgen -H "Content-Type: application/json" -d '{
+        "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
+        }'
+   ```
+
+   Following the validation of all aforementioned microservices, we are now prepared to construct a mega-service.
+
+## 🚀 Launch the UI
+
+Open this URL `http://{host_ip}:5173` in your browser to access the frontend.
+
+![project-screenshot](../../../../assets/img/faqgen_ui_text.png)
+
+## 🚀 Launch the React UI (Optional)
+
+To access the FAQGen (react based) frontend, modify the UI service in the `compose.yaml` file. Replace `faqgen-rocm-ui-server` service with the `faqgen-rocm-react-ui-server` service as per the config below:
+
+```bash
+  faqgen-rocm-react-ui-server:
+    image: opea/faqgen-react-ui:latest
+    container_name: faqgen-rocm-react-ui-server
+    environment:
+      - no_proxy=${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](../../../../assets/img/faqgen_react_ui_text.png)
+
+- Create FAQs from Text Files
+  ![project-screenshot](../../../../assets/img/faqgen_react_ui_text_file.png)

FaqGen/docker_compose/amd/gpu/rocm/compose.yaml

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+# Copyright (C) 2024 Advanced Micro Devices, Inc.
+# SPDX-License-Identifier: Apache-2.0
+
+services:
+  faqgen-tgi-service:
+    image: ghcr.io/huggingface/text-generation-inference:2.3.1-rocm
+    container_name: faggen-tgi-service
+    ports:
+      - "${FAQGEN_TGI_SERVICE_PORT:-8008}:80"
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      TGI_LLM_ENDPOINT: "http://${HOST_IP}:${FAQGEN_TGI_SERVICE_PORT}"
+      HUGGINGFACEHUB_API_TOKEN: ${FAQGEN_HUGGINGFACEHUB_API_TOKEN}
+      HUGGING_FACE_HUB_TOKEN: ${FAQGEN_HUGGINGFACEHUB_API_TOKEN}
+    volumes:
+      - "/var/opea/faqgen-service/data:/data"
+    shm_size: 1g
+    devices:
+      - /dev/kfd:/dev/kfd
+      - /dev/dri/${FAQGEN_CARD_ID}:/dev/dri/${FAQGEN_CARD_ID}
+      - /dev/dri/${FAQGEN_RENDER_ID}:/dev/dri/${FAQGEN_RENDER_ID}
+    cap_add:
+      - SYS_PTRACE
+    group_add:
+      - video
+    security_opt:
+      - seccomp:unconfined
+    ipc: host
+    command: --model-id ${FAQGEN_LLM_MODEL_ID}
+  faqgen-llm-server:
+    image: ${REGISTRY:-opea}/llm-faqgen-tgi:${TAG:-latest}
+    container_name: faqgen-llm-server
+    depends_on:
+      - faqgen-tgi-service
+    ports:
+      - "${FAQGEN_LLM_SERVER_PORT:-9000}:9000"
+    ipc: host
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      TGI_LLM_ENDPOINT: "http://${HOST_IP}:${FAQGEN_TGI_SERVICE_PORT}"
+      HUGGINGFACEHUB_API_TOKEN: ${FAQGEN_HUGGINGFACEHUB_API_TOKEN}
+      HUGGING_FACE_HUB_TOKEN: ${FAQGEN_HUGGINGFACEHUB_API_TOKEN}
+    restart: unless-stopped
+  faqgen-backend-server:
+    image: ${REGISTRY:-opea}/faqgen:${TAG:-latest}
+    container_name: faqgen-backend-server
+    depends_on:
+      - faqgen-tgi-service
+      - faqgen-llm-server
+    ports:
+      - "${FAQGEN_BACKEND_SERVER_PORT:-8888}:8888"
+    environment:
+      - no_proxy=${no_proxy}
+      - https_proxy=${https_proxy}
+      - http_proxy=${http_proxy}
+      - MEGA_SERVICE_HOST_IP=${HOST_IP}
+      - LLM_SERVICE_HOST_IP=${HOST_IP}
+    ipc: host
+    restart: always
+  faqgen-ui-server:
+    image: ${REGISTRY:-opea}/faqgen-ui:${TAG:-latest}
+    container_name: faqgen-ui-server
+    depends_on:
+      - faqgen-backend-server
+    ports:
+      - "${FAGGEN_UI_PORT:-5173}:5173"
+    environment:
+      - no_proxy=${no_proxy}
+      - https_proxy=${https_proxy}
+      - http_proxy=${http_proxy}
+      - DOC_BASE_URL="http://${HOST_IP}:${FAQGEN_BACKEND_SERVER_PORT}/v1/faqgen"
+    ipc: host
+    restart: always
+networks:
+  default:
+    driver: bridge

FaqGen/docker_compose/amd/gpu/rocm/set_env.sh

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+#!/usr/bin/env bash
+
+# Copyright (C) 2024 Advanced Micro Devices, Inc.
+# SPDX-License-Identifier: Apache-2.0
+
+export FAQGEN_LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
+export FAQGEN_TGI_SERVICE_IMAGE="ghcr.io/huggingface/text-generation-inference:2.3.1-rocm"
+export HOST_IP=${host_ip}
+export FAQGEN_CARD_ID="card0"
+export FAQGEN_RENDER_ID="renderD128"
+export FAQGEN_TGI_SERVICE_PORT=8883
+export FAQGEN_LLM_SERVER_PORT=9001
+export FAQGEN_HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
+export FAQGEN_BACKEND_SERVER_PORT=8881
+export FAGGEN_UI_PORT=5174

FaqGen/tests/test_compose_on_rocm.sh

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+#!/bin/bash
+# Copyright (C) 2024 Advanced Micro Devices, Inc.
+# SPDX-License-Identifier: Apache-2.0
+
+set -xe
+IMAGE_REPO=${IMAGE_REPO:-"opea"}
+IMAGE_TAG=${IMAGE_TAG:-"latest"}
+echo "REGISTRY=IMAGE_REPO=${IMAGE_REPO}"
+echo "TAG=IMAGE_TAG=${IMAGE_TAG}"
+export REGISTRY=${IMAGE_REPO}
+export TAG=${IMAGE_TAG}
+
+WORKPATH=$(dirname "$PWD")
+LOG_PATH="$WORKPATH/tests"
+ip_address=$(hostname -I | awk '{print $1}')
+
+export HOST_IP=${ip_address}
+export FAQGEN_LLM_MODEL_ID="meta-llama/Meta-Llama-3-8B-Instruct"
+export FAQGEN_TGI_SERVICE_PORT=8008
+export FAQGEN_LLM_SERVER_PORT=9000
+export FAQGEN_HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
+export FAQGEN_CARD_ID="card0"
+export FAQGEN_RENDER_ID="renderD128"
+export FAQGEN_BACKEND_SERVER_PORT=8888
+export FAGGEN_UI_PORT=5173
+export TGI_LLM_ENDPOINT="http://${ip_address}:8008"
+export MEGA_SERVICE_HOST_IP=${ip_address}
+export LLM_SERVICE_HOST_IP=${ip_address}
+export BACKEND_SERVICE_ENDPOINT="http://${ip_address}:8888/v1/faqgen"
+
+function build_docker_images() {
+    cd "$WORKPATH"/docker_image_build
+    git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
+
+    echo "Build all the images with --no-cache, check docker_image_build.log for details..."
+    service_list="faqgen faqgen-ui llm-faqgen-tgi"
+    docker compose -f build.yaml build ${service_list} --no-cache > "${LOG_PATH}"/docker_image_build.log
+    docker pull ghcr.io/huggingface/text-generation-inference:2.3.1-rocm
+    docker images && sleep 1s
+}
+
+function start_services() {
+    cd "$WORKPATH"/docker_compose/amd/gpu/rocm
+
+    sed -i "s/backend_address/$ip_address/g" "$WORKPATH"/ui/svelte/.env
+
+    # Start Docker Containers
+    docker compose up -d > "${LOG_PATH}"/start_services_with_compose.log
+
+    n=0
+    until [[ "$n" -ge 100 ]]; do
+        docker logs faggen-tgi-service > "${LOG_PATH}"/tgi_service_start.log
+        if grep -q Connected "${LOG_PATH}"/tgi_service_start.log; then
+            break
+        fi
+        sleep 5s
+        n=$((n+1))
+    done
+}
+
+function validate_services() {
+    local URL="$1"
+    local EXPECTED_RESULT="$2"
+    local SERVICE_NAME="$3"
+    local DOCKER_NAME="$4"
+    local INPUT_DATA="$5"
+
+    local HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
+    if [ "$HTTP_STATUS" -eq 200 ]; then
+        echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
+
+        local CONTENT=$(curl -s -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL" | tee "${LOG_PATH}"/"${SERVICE_NAME}".log)
+
+        if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
+            echo "[ $SERVICE_NAME ] Content is as expected."
+        else
+            echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
+            docker logs "${DOCKER_NAME}" >> "${LOG_PATH}"/"${SERVICE_NAME}".log
+            exit 1
+        fi
+    else
+        echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
+        docker logs "${DOCKER_NAME}" >> "${LOG_PATH}"/"${SERVICE_NAME}".log
+        exit 1
+    fi
+    sleep 1s
+}
+
+function validate_microservices() {
+    # Check if the microservices are running correctly.
+
+    # tgi for llm service
+    validate_services \
+        "${ip_address}:8008/generate" \
+        "generated_text" \
+        "tgi-service" \
+        "faqgen-tgi-service" \
+        '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
+
+    # llm microservice
+    validate_services \
+        "${ip_address}:9000/v1/faqgen" \
+        "data: " \
+        "llm" \
+        "faqgen-llm-server" \
+        '{"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."}'
+}
+
+function validate_megaservice() {
+    # Curl the Mega Service
+    validate_services \
+    "${ip_address}:8888/v1/faqgen" \
+    "Text Embeddings Inference" \
+    "mega-faqgen" \
+    "faqgen-backend-server" \
+    '{"messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
+}
+
+function validate_frontend() {
+    cd "$WORKPATH"/ui/svelte
+    local conda_env_name="OPEA_e2e"
+    export PATH=${HOME}/miniforge3/bin/:$PATH
+    if conda info --envs | grep -q "$conda_env_name"; then
+        echo "$conda_env_name exist!"
+    else
+        conda create -n ${conda_env_name} python=3.12 -y
+    fi
+    source activate ${conda_env_name}
+
+    sed -i "s/localhost/$ip_address/g" playwright.config.ts
+
+    conda install -c conda-forge nodejs -y
+    npm install && npm ci && npx playwright install --with-deps
+    node -v && npm -v && pip list
+
+    exit_status=0
+    npx playwright test || exit_status=$?
+
+    if [ $exit_status -ne 0 ]; then
+        echo "[TEST INFO]: ---------frontend test failed---------"
+        exit $exit_status
+    else
+        echo "[TEST INFO]: ---------frontend test passed---------"
+    fi
+}
+
+function stop_docker() {
+    cd "$WORKPATH"/docker_compose/amd/gpu/rocm
+    docker compose stop && docker compose rm -f
+}
+
+function main() {
+
+    stop_docker
+
+    if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
+    start_services
+
+    validate_microservices
+    validate_megaservice
+#    validate_frontend
+
+    stop_docker
+    echo y | docker system prune
+
+}
+
+main