Refactor folder to support different vendors (#743)
Signed-off-by: Xinyao Wang <xinyao.wang@intel.com> Signed-off-by: chensuyue <suyue.chen@intel.com>
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XinyaoWa
177
CodeTrans/tests/test_compose_on_xeon.sh
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177
CodeTrans/tests/test_compose_on_xeon.sh
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#!/bin/bash
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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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set -xe
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IMAGE_REPO=${IMAGE_REPO:-"opea"}
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IMAGE_TAG=${IMAGE_TAG:-"latest"}
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echo "REGISTRY=IMAGE_REPO=${IMAGE_REPO}"
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echo "TAG=IMAGE_TAG=${IMAGE_TAG}"
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export REGISTRY=${IMAGE_REPO}
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export TAG=${IMAGE_TAG}
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WORKPATH=$(dirname "$PWD")
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LOG_PATH="$WORKPATH/tests"
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ip_address=$(hostname -I | awk '{print $1}')
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function build_docker_images() {
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cd $WORKPATH/docker_image_build
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git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
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echo "Build all the images with --no-cache, check docker_image_build.log for details..."
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service_list="codetrans codetrans-ui llm-tgi nginx"
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docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
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docker pull ghcr.io/huggingface/text-generation-inference:sha-e4201f4-intel-cpu
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docker images && sleep 1s
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}
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function start_services() {
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cd $WORKPATH/docker_compose/intel/cpu/xeon/
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export http_proxy=${http_proxy}
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export https_proxy=${http_proxy}
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export LLM_MODEL_ID="HuggingFaceH4/mistral-7b-grok"
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export TGI_LLM_ENDPOINT="http://${ip_address}:8008"
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export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
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export MEGA_SERVICE_HOST_IP=${ip_address}
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export LLM_SERVICE_HOST_IP=${ip_address}
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export BACKEND_SERVICE_ENDPOINT="http://${ip_address}:7777/v1/codetrans"
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export FRONTEND_SERVICE_IP=${ip_address}
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export FRONTEND_SERVICE_PORT=5173
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export BACKEND_SERVICE_NAME=codetrans
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export BACKEND_SERVICE_IP=${ip_address}
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export BACKEND_SERVICE_PORT=7777
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export NGINX_PORT=80
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sed -i "s/backend_address/$ip_address/g" $WORKPATH/ui/svelte/.env
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# Start Docker Containers
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docker compose up -d > ${LOG_PATH}/start_services_with_compose.log
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n=0
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until [[ "$n" -ge 100 ]]; do
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docker logs codetrans-tgi-service > ${LOG_PATH}/tgi_service_start.log
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if grep -q Connected ${LOG_PATH}/tgi_service_start.log; then
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break
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fi
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sleep 5s
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n=$((n+1))
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done
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}
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function validate_services() {
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local URL="$1"
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local EXPECTED_RESULT="$2"
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local SERVICE_NAME="$3"
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local DOCKER_NAME="$4"
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local INPUT_DATA="$5"
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local HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
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if [ "$HTTP_STATUS" -eq 200 ]; then
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echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
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local CONTENT=$(curl -s -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL" | tee ${LOG_PATH}/${SERVICE_NAME}.log)
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if echo "$CONTENT" | grep -q "$EXPECTED_RESULT"; then
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echo "[ $SERVICE_NAME ] Content is as expected."
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else
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echo "[ $SERVICE_NAME ] Content does not match the expected result: $CONTENT"
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docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
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exit 1
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fi
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else
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echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
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docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
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exit 1
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fi
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sleep 5s
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}
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function validate_microservices() {
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# tgi for embedding service
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validate_services \
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"${ip_address}:8008/generate" \
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"generated_text" \
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"tgi" \
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"codetrans-tgi-service" \
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'{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
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# llm microservice
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validate_services \
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"${ip_address}:9000/v1/chat/completions" \
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"data: " \
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"llm" \
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"llm-tgi-server" \
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'{"query":" ### System: Please translate the following Golang codes into Python codes. ### Original codes: '\'''\'''\''Golang \npackage main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n '\'''\'''\'' ### Translated codes:"}'
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}
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function validate_megaservice() {
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# Curl the Mega Service
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validate_services \
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"${ip_address}:7777/v1/codetrans" \
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"print" \
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"mega-codetrans" \
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"codetrans-xeon-backend-server" \
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'{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n}"}'
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# test the megeservice via nginx
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validate_services \
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"${ip_address}:80/v1/codetrans" \
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"print" \
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"mega-codetrans-nginx" \
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"codetrans-xeon-nginx-server" \
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'{"language_from": "Golang","language_to": "Python","source_code": "package main\n\nimport \"fmt\"\nfunc main() {\n fmt.Println(\"Hello, World!\");\n}"}'
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}
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function validate_frontend() {
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cd $WORKPATH/ui/svelte
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local conda_env_name="OPEA_e2e"
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export PATH=${HOME}/miniforge3/bin/:$PATH
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if conda info --envs | grep -q "$conda_env_name"; then
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echo "$conda_env_name exist!"
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else
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conda create -n ${conda_env_name} python=3.12 -y
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fi
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source activate ${conda_env_name}
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sed -i "s/localhost/$ip_address/g" playwright.config.ts
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conda install -c conda-forge nodejs -y
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npm install && npm ci && npx playwright install --with-deps
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node -v && npm -v && pip list
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exit_status=0
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npx playwright test || exit_status=$?
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if [ $exit_status -ne 0 ]; then
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echo "[TEST INFO]: ---------frontend test failed---------"
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exit $exit_status
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else
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echo "[TEST INFO]: ---------frontend test passed---------"
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fi
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}
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function stop_docker() {
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cd $WORKPATH/docker_compose/intel/cpu/xeon/
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docker compose stop && docker compose rm -f
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}
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function main() {
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stop_docker
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if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
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start_services
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validate_microservices
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validate_megaservice
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validate_frontend
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stop_docker
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echo y | docker system prune
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}
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main
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