Add compose deploy example for DocSum on AMD ROCm

Signed-off-by: astafevav <[email protected]>

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

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+## 🚀 Start Microservices and MegaService
+
+### Required Models
+
+We set default model as "Intel/neural-chat-7b-v3-3", change "LLM_MODEL_ID" in following setting if you want to use other models.
+If use gated models, you also need to provide [huggingface token](https://huggingface.co/docs/hub/security-tokens) to "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 DOCSUM_TGI_IMAGE="ghcr.io/huggingface/text-generation-inference:2.3.1-rocm"
+export DOCSUM_LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
+export HOST_IP=${host_ip}
+export DOCSUM_TGI_SERVICE_PORT="18882"
+export DOCSUM_TGI_LLM_ENDPOINT="http://${HOST_IP}:${DOCSUM_TGI_SERVICE_PORT}"
+export DOCSUM_HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
+export DOCSUM_LLM_SERVER_PORT="8008"
+export DOCSUM_BACKEND_SERVER_PORT="8888"
+export DOCSUM_FRONTEND_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/card1:/dev/dri/card1
+      - /dev/dri/renderD129:/dev/dri/renderD129
+```
+
+Pelase 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/DocSum/docker_compose/amd/gpu/rocm
+docker compose up -d
+```
+
+### Validate Microservices
+
+1. TGI Service
+
+   ```bash
+   curl http://${host_ip}:8008/generate \
+     -X POST \
+     -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
+     -H 'Content-Type: application/json'
+   ```
+
+2. LLM Microservice
+
+   ```bash
+   curl http://${host_ip}:9000/v1/chat/docsum \
+     -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/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.","max_tokens":32, "language":"en", "stream":false
+        }'
+   ```
+
+## 🚀 Launch the Svelte UI
+
+Open this URL `http://{host_ip}:5173` in your browser to access the frontend.
+
+![project-screenshot](https://github.com/intel-ai-tce/GenAIExamples/assets/21761437/93b1ed4b-4b76-4875-927e-cc7818b4825b)
+
+Here is an example for summarizing a article.
+
+![image](https://github.com/intel-ai-tce/GenAIExamples/assets/21761437/67ecb2ec-408d-4e81-b124-6ded6b833f55)
+
+## 🚀 Launch the React UI (Optional)
+
+To access the React-based frontend, modify the UI service in the `compose.yaml` file. Replace `docsum-rocm-ui-server` service with the `docsum-rocm-react-ui-server` service as per the config below:
+
+```yaml
+docsum-rocm-react-ui-server:
+  image: ${REGISTRY:-opea}/docsum-react-ui:${TAG:-latest}
+  container_name: docsum-rocm-react-ui-server
+  depends_on:
+    - docsum-rocm-backend-server
+  ports:
+    - "5174:80"
+  environment:
+    - no_proxy=${no_proxy}
+    - https_proxy=${https_proxy}
+    - http_proxy=${http_proxy}
+    - DOC_BASE_URL=${BACKEND_SERVICE_ENDPOINT}
+```
+
+Open this URL `http://{host_ip}:5175` in your browser to access the frontend.
+
+![project-screenshot](../../../../assets/img/docsum-ui-react.png)

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

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+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+services:
+  docsum-tgi-service:
+    image: ghcr.io/huggingface/text-generation-inference:2.3.1-rocm
+    container_name: docsum-tgi-service
+    ports:
+      - "${DOCSUM_TGI_SERVICE_PORT}:80"
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      TGI_LLM_ENDPOINT: "http://${HOST_IP}:${DOCSUM_TGI_SERVICE_PORT}"
+      HUGGINGFACEHUB_API_TOKEN: ${DOCSUM_HUGGINGFACEHUB_API_TOKEN}
+    volumes:
+      - "/var/opea/docsum-service/data:/data"
+    shm_size: 1g
+    devices:
+      - /dev/kfd:/dev/kfd
+    cap_add:
+      - SYS_PTRACE
+    group_add:
+      - video
+    security_opt:
+      - seccomp:unconfined
+    ipc: host
+    command: --model-id ${DOCSUM_LLM_MODEL_ID}
+  docsum-llm-server:
+    image: ${REGISTRY:-opea}/llm-docsum-tgi:${TAG:-latest}
+    container_name: docsum-llm-server
+    depends_on:
+      - docsum-tgi-service
+    ports:
+      - "${DOCSUM_LLM_SERVER_PORT}:9000"
+    ipc: host
+    group_add:
+      - video
+    security_opt:
+      - seccomp:unconfined
+    cap_add:
+      - SYS_PTRACE
+    devices:
+      - /dev/kfd:/dev/kfd
+      - /dev/dri/${DOCSUM_CARD_ID}:/dev/dri/${DOCSUM_CARD_ID}
+      - /dev/dri/${DOCSUM_RENDER_ID}:/dev/dri/${DOCSUM_RENDER_ID}
+    environment:
+      no_proxy: ${no_proxy}
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      TGI_LLM_ENDPOINT: "http://${HOST_IP}:${DOCSUM_TGI_SERVICE_PORT}"
+      HUGGINGFACEHUB_API_TOKEN: ${DOCSUM_HUGGINGFACEHUB_API_TOKEN}
+    restart: unless-stopped
+  docsum-backend-server:
+    image: ${REGISTRY:-opea}/docsum:${TAG:-latest}
+    container_name: docsum-backend-server
+    depends_on:
+      - docsum-tgi-service
+      - docsum-llm-server
+    ports:
+      - "${DOCSUM_BACKEND_SERVER_PORT}:8888"
+    environment:
+      - no_proxy=${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
+  docsum-ui-server:
+    image: ${REGISTRY:-opea}/docsum-ui:${TAG:-latest}
+    container_name: docsum-ui-server
+    depends_on:
+      - docsum-backend-server
+    ports:
+      - "${DOCSUM_FRONTEND_PORT}:5173"
+    environment:
+      - no_proxy=${no_proxy}
+      - https_proxy=${https_proxy}
+      - http_proxy=${http_proxy}
+      - DOC_BASE_URL="http://${HOST_IP}:${DOCSUM_BACKEND_PORT}/v1/docsum"
+    ipc: host
+    restart: always
+
+networks:
+  default:
+    driver: bridge

DocSum/tests/test_compose_on_rocm.sh

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+#!/bin/bash
+# Copyright (C) 2024 Intel Corporation
+# 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}')
+
+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="docsum docsum-ui llm-docsum-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 $WORKPATH/docker_compose/amd/gpu/rocm
+
+    export DOCSUM_TGI_IMAGE="ghcr.io/huggingface/text-generation-inference:2.3.1-rocm"
+    export DOCSUM_LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
+    export HOST_IP=${ip_address}
+    export DOCSUM_TGI_SERVICE_PORT="8008"
+    export DOCSUM_TGI_LLM_ENDPOINT="http://${HOST_IP}:8008"
+    export DOCSUM_HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
+    export DOCSUM_LLM_SERVER_PORT="9000"
+    export DOCSUM_BACKEND_SERVER_PORT="8888"
+    export DOCSUM_FRONTEND_PORT="5552"
+    export MEGA_SERVICE_HOST_IP=${ip_address}
+    export LLM_SERVICE_HOST_IP=${ip_address}
+    export BACKEND_SERVICE_ENDPOINT="http://${ip_address}:8888/v1/docsum"
+
+    sed -i "s/backend_address/$ip_address/g" $WORKPATH/ui/svelte/.env
+
+    # Start Docker Containers
+    docker compose up -d > ${LOG_PATH}/start_services_with_compose.log
+
+    until [[ "$n" -ge 100 ]]; do
+        docker logs docsum-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 \
+        "${ip_address}:8008/generate" \
+        "generated_text" \
+        "tgi-llm" \
+        "tgi-service" \
+        '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
+
+    # llm microservice
+    validate_services \
+        "${ip_address}:9000/v1/chat/docsum" \
+        "data: " \
+        "llm" \
+        "llm-docsum-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() {
+    local SERVICE_NAME="mega-docsum"
+    local DOCKER_NAME="docsum-backend-server"
+    local EXPECTED_RESULT="embedding"
+    local INPUT_DATA="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."
+    local URL="${ip_address}:8888/v1/docsum"
+    local HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -F "$INPUT_DATA" -H 'Content-Type: multipart/form-data' "$URL")
+    if [ "$HTTP_STATUS" -eq 200 ]; then
+        echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
+
+        local CONTENT=$(curl -s -X POST -F "$INPUT_DATA" -H 'Content-Type: multipart/form-data' "$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_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