Adding files to deploy MultimodalQnA application on ROCm vLLM (#1737)
Signed-off-by: Artem Astafev <a.astafev@datamonsters.com>
This commit is contained in:
Artem Astafev
@@ -72,12 +72,21 @@ function setup_env() {
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export DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/generate_captions"
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export DATAPREP_GET_FILE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/get"
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export DATAPREP_DELETE_FILE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/delete"
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export MODEL_CACHE=${model_cache:-"/var/opea/multimodalqna-service/data"}
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}
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function start_services() {
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cd $WORKPATH/docker_compose/amd/gpu/rocm
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docker compose -f compose.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
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sleep 1m
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n=0
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until [[ "$n" -ge 100 ]]; do
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docker logs tgi-llava-rocm-server >& $LOG_PATH/tgi-llava-rocm-server_start.log
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if grep -q "Connected" $LOG_PATH/tgi-llava-rocm-server_start.log; then
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break
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fi
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sleep 10s
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n=$((n+1))
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done
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}
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function prepare_data() {
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340
MultimodalQnA/tests/test_compose_vllm_on_rocm.sh
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340
MultimodalQnA/tests/test_compose_vllm_on_rocm.sh
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@@ -0,0 +1,340 @@
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#!/bin/bash
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# Copyright (C) 2024 Advanced Micro Devices, Inc.
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# SPDX-License-Identifier: Apache-2.0
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set -ex
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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 $1}')
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export image_fn="apple.png"
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export video_fn="WeAreGoingOnBullrun.mp4"
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export caption_fn="apple.txt"
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function build_docker_images() {
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opea_branch=${opea_branch:-"main"}
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# If the opea_branch isn't main, replace the git clone branch in Dockerfile.
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if [[ "${opea_branch}" != "main" ]]; then
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cd $WORKPATH
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OLD_STRING="RUN git clone --depth 1 https://github.com/opea-project/GenAIComps.git"
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NEW_STRING="RUN git clone --depth 1 --branch ${opea_branch} https://github.com/opea-project/GenAIComps.git"
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find . -type f -name "Dockerfile*" | while read -r file; do
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echo "Processing file: $file"
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sed -i "s|$OLD_STRING|$NEW_STRING|g" "$file"
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done
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fi
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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="multimodalqna multimodalqna-ui embedding-multimodal-bridgetower embedding retriever lvm dataprep whisper vllm-rocm"
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docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
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docker images && sleep 1m
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}
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function setup_env() {
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export HOST_IP=${ip_address}
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export host_ip=${ip_address}
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export MULTIMODAL_HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
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export MULTIMODAL_VLLM_SERVICE_PORT="8399"
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export no_proxy=${your_no_proxy}
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export http_proxy=${your_http_proxy}
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export https_proxy=${your_http_proxy}
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export BRIDGE_TOWER_EMBEDDING=true
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export EMBEDDER_PORT=6006
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export MMEI_EMBEDDING_ENDPOINT="http://${HOST_IP}:$EMBEDDER_PORT"
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export MM_EMBEDDING_PORT_MICROSERVICE=6000
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export WHISPER_SERVER_PORT=7066
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export WHISPER_SERVER_ENDPOINT="http://${HOST_IP}:${WHISPER_SERVER_PORT}/v1/asr"
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export REDIS_URL="redis://${HOST_IP}:6379"
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export REDIS_HOST=${HOST_IP}
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export INDEX_NAME="mm-rag-redis"
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export LVM_ENDPOINT="http://${HOST_IP}:8399"
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export EMBEDDING_MODEL_ID="BridgeTower/bridgetower-large-itm-mlm-itc"
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export MULTIMODAL_LLM_MODEL_ID="Xkev/Llama-3.2V-11B-cot"
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export WHISPER_MODEL="base"
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export MM_EMBEDDING_SERVICE_HOST_IP=${HOST_IP}
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export MM_RETRIEVER_SERVICE_HOST_IP=${HOST_IP}
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export LVM_SERVICE_HOST_IP=${HOST_IP}
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export MEGA_SERVICE_HOST_IP=${HOST_IP}
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export BACKEND_SERVICE_ENDPOINT="http://${HOST_IP}:8888/v1/multimodalqna"
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export DATAPREP_INGEST_SERVICE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/ingest"
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export DATAPREP_GEN_TRANSCRIPT_SERVICE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/generate_transcripts"
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export DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/generate_captions"
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export DATAPREP_GET_FILE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/get"
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export DATAPREP_DELETE_FILE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/delete"
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export MODEL_CACHE=${model_cache:-"/var/opea/multimodalqna-service/data"}
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}
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function start_services() {
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cd $WORKPATH/docker_compose/amd/gpu/rocm
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docker compose -f compose_vllm.yaml 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 multimodalqna-vllm-service >& $LOG_PATH/search-vllm-service_start.log
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if grep -q "Application startup complete" $LOG_PATH/search-vllm-service_start.log; then
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break
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fi
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sleep 10s
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n=$((n+1))
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done
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}
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function prepare_data() {
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cd $LOG_PATH
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echo "Downloading image and video"
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wget https://github.com/docarray/docarray/blob/main/tests/toydata/image-data/apple.png?raw=true -O ${image_fn}
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wget http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/WeAreGoingOnBullrun.mp4 -O ${video_fn}
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echo "Writing caption file"
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echo "This is an apple." > ${caption_fn}
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sleep 1m
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}
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function validate_service() {
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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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if [[ $SERVICE_NAME == *"dataprep-multimodal-redis-transcript"* ]]; then
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cd $LOG_PATH
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F "files=@./${video_fn}" -H 'Content-Type: multipart/form-data' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep-multimodal-redis-caption"* ]]; then
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cd $LOG_PATH
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F "files=@./${image_fn}" -H 'Content-Type: multipart/form-data' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep-multimodal-redis-ingest"* ]]; then
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cd $LOG_PATH
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F "files=@./${image_fn}" -F "files=@./apple.txt" -H 'Content-Type: multipart/form-data' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep_get"* ]]; then
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -H 'Content-Type: application/json' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep_del"* ]]; then
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d '{"file_path": "apple.txt"}' -H 'Content-Type: application/json' "$URL")
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else
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
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fi
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HTTP_STATUS=$(echo $HTTP_RESPONSE | tr -d '\n' | sed -e 's/.*HTTPSTATUS://')
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RESPONSE_BODY=$(echo $HTTP_RESPONSE | sed -e 's/HTTPSTATUS\:.*//g')
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docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
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# check response status
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if [ "$HTTP_STATUS" -ne "200" ]; then
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echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
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exit 1
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else
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echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
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fi
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# check response body
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if [[ "$RESPONSE_BODY" != *"$EXPECTED_RESULT"* ]]; then
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echo "[ $SERVICE_NAME ] Content does not match the expected result: $RESPONSE_BODY"
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exit 1
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else
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echo "[ $SERVICE_NAME ] Content is as expected."
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fi
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sleep 1s
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}
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function validate_microservices() {
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# Check if the microservices are running correctly.
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# Bridgetower Embedding Server
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echo "Validating embedding-multimodal-bridgetower"
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validate_service \
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"http://${host_ip}:${EMBEDDER_PORT}/v1/encode" \
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'"embedding":[' \
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"embedding-multimodal-bridgetower" \
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"embedding-multimodal-bridgetower" \
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'{"text":"This is example"}'
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validate_service \
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"http://${host_ip}:${EMBEDDER_PORT}/v1/encode" \
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'"embedding":[' \
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"embedding-multimodal-bridgetower" \
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"embedding-multimodal-bridgetower" \
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'{"text":"This is example", "img_b64_str": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC"}'
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# embedding microservice
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echo "Validating embedding"
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validate_service \
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"http://${host_ip}:$MM_EMBEDDING_PORT_MICROSERVICE/v1/embeddings" \
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'"embedding":[' \
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"embedding" \
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"embedding" \
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'{"text" : "This is some sample text."}'
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validate_service \
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"http://${host_ip}:$MM_EMBEDDING_PORT_MICROSERVICE/v1/embeddings" \
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'"embedding":[' \
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"embedding" \
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"embedding" \
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'{"text": {"text" : "This is some sample text."}, "image" : {"url": "https://github.com/docarray/docarray/blob/main/tests/toydata/image-data/apple.png?raw=true"}}'
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sleep 1m # retrieval can't curl as expected, try to wait for more time
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# test data prep
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echo "Data Prep with Generating Transcript for Video"
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validate_service \
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"${DATAPREP_GEN_TRANSCRIPT_SERVICE_ENDPOINT}" \
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"Data preparation succeeded" \
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"dataprep-multimodal-redis-transcript" \
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"dataprep-multimodal-redis"
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echo "Data Prep with Image & Caption Ingestion"
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validate_service \
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"${DATAPREP_INGEST_SERVICE_ENDPOINT}" \
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"Data preparation succeeded" \
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"dataprep-multimodal-redis-ingest" \
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"dataprep-multimodal-redis"
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echo "Validating get file returns mp4"
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validate_service \
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"${DATAPREP_GET_FILE_ENDPOINT}" \
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'.mp4' \
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"dataprep_get" \
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"dataprep-multimodal-redis"
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echo "Validating get file returns png"
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validate_service \
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"${DATAPREP_GET_FILE_ENDPOINT}" \
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'.png' \
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"dataprep_get" \
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"dataprep-multimodal-redis"
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sleep 2m
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# multimodal retrieval microservice
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echo "Validating retriever-redis"
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your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(512)]; print(embedding)")
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validate_service \
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"http://${host_ip}:7000/v1/retrieval" \
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"retrieved_docs" \
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"retriever-redis" \
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"retriever-redis" \
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"{\"text\":\"test\",\"embedding\":${your_embedding}}"
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sleep 5m
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#vLLM Service
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echo "Evaluating vllm"
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validate_service \
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"${host_ip}:${MULTIMODAL_VLLM_SERVICE_PORT}/v1/chat/completions" \
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"content" \
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"multimodalqna-vllm-service" \
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"multimodalqna-vllm-service" \
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'{"model": "Xkev/Llama-3.2V-11B-cot", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 17}'
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# lvm
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echo "Evaluating lvm"
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validate_service \
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"http://${host_ip}:9399/v1/lvm" \
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'"text":"' \
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"lvm" \
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"lvm" \
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'{"retrieved_docs": [], "initial_query": "What is this?", "top_n": 1, "metadata": [{"b64_img_str": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "transcript_for_inference": "yellow image", "video_id": "8c7461df-b373-4a00-8696-9a2234359fe0", "time_of_frame_ms":"37000000", "source_video":"WeAreGoingOnBullrun_8c7461df-b373-4a00-8696-9a2234359fe0.mp4"}], "chat_template":"The caption of the image is: '\''{context}'\''. {question}"}'
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# data prep requiring lvm
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echo "Data Prep with Generating Caption for Image"
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validate_service \
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"${DATAPREP_GEN_CAPTION_SERVICE_ENDPOINT}" \
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"Data preparation succeeded" \
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"dataprep-multimodal-redis-caption" \
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"dataprep-multimodal-redis"
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sleep 3m
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}
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function validate_megaservice() {
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# Curl the Mega Service with retrieval
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echo "Validate megaservice with first query"
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validate_service \
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"http://${host_ip}:8888/v1/multimodalqna" \
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'"time_of_frame_ms":' \
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"multimodalqna" \
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"multimodalqna-backend-server" \
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'{"messages": "What is the revenue of Nike in 2023?"}'
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echo "Validate megaservice with first audio query"
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validate_service \
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"http://${host_ip}:8888/v1/multimodalqna" \
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'"time_of_frame_ms":' \
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"multimodalqna" \
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"multimodalqna-backend-server" \
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'{"messages": [{"role": "user", "content": [{"type": "audio", "audio": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}]}]}'
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echo "Validate megaservice with follow-up query"
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validate_service \
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"http://${host_ip}:8888/v1/multimodalqna" \
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'"content":"' \
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"multimodalqna" \
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"multimodalqna-backend-server" \
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'{"messages": [{"role": "user", "content": [{"type": "audio", "audio": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}, {"type": "image_url", "image_url": {"url": "https://www.ilankelman.org/stopsigns/australia.jpg"}}]}, {"role": "assistant", "content": "opea project! "}, {"role": "user", "content": [{"type": "text", "text": "goodbye"}]}]}'
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echo "Validate megaservice with multiple text queries"
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validate_service \
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"http://${host_ip}:8888/v1/multimodalqna" \
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'"content":"' \
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"multimodalqna" \
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"multimodalqna-backend-server" \
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'{"messages": [{"role": "user", "content": [{"type": "text", "text": "hello, "}]}, {"role": "assistant", "content": "opea project! "}, {"role": "user", "content": [{"type": "text", "text": "goodbye"}]}]}'
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}
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function validate_delete {
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echo "Validate data prep delete files"
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export DATAPREP_DELETE_FILE_ENDPOINT="http://${HOST_IP}:6007/v1/dataprep/delete"
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validate_service \
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"${DATAPREP_DELETE_FILE_ENDPOINT}" \
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'{"status":true}' \
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"dataprep_del" \
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"dataprep-multimodal-redis"
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}
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function delete_data() {
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cd $LOG_PATH
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echo "Deleting image, video, and caption"
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rm -rf ${image_fn}
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rm -rf ${video_fn}
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rm -rf ${caption_fn}
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}
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function stop_docker() {
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cd $WORKPATH/docker_compose/amd/gpu/rocm
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docker compose -f compose.yaml stop && docker compose -f compose.yaml rm -f
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}
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function main() {
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setup_env
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stop_docker
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if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
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start_time=$(date +%s)
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start_services
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end_time=$(date +%s)
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duration=$((end_time-start_time))
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echo "Mega service start duration is $duration s" && sleep 1s
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prepare_data
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validate_microservices
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echo "==== microservices validated ===="
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validate_megaservice
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echo "==== megaservice validated ===="
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validate_delete
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echo "==== delete validated ===="
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delete_data
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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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