Refine DocSum Test Script (#247)
Signed-off-by: zehao-intel <zehao.huang@intel.com>
This commit is contained in:
zehao-intel
@@ -43,57 +43,62 @@ function start_services() {
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sleep 2m # Waits 2 minutes
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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 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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# TODO: Any results check required??
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curl http://${ip_address}:8008/generate \
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-X POST \
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-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
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-H 'Content-Type: application/json' > ${LOG_PATH}/generate.log
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exit_code=$?
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if [ $exit_code -ne 0 ]; then
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echo "Microservice failed, please check the logs in artifacts!"
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docker logs tgi-gaudi-server >> ${LOG_PATH}/generate.log
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exit 1
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fi
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sleep 5s
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curl http://${ip_address}:9000/v1/chat/docsum \
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-X POST \
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-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."}' \
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-H 'Content-Type: application/json' > ${LOG_PATH}/completions.log
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exit_code=$?
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if [ $exit_code -ne 0 ]; then
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echo "Microservice failed, please check the logs in artifacts!"
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docker logs docsum-gaudi-backend-server >> ${LOG_PATH}/completions.log
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exit 1
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fi
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sleep 5s
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# tgi for llm service
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validate_services \
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"${ip_address}:8008/generate" \
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"generated_text" \
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"tgi-gaudi" \
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"tgi-gaudi-server" \
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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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"${ip_address}:9000/v1/chat/docsum" \
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"data: " \
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"llm" \
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"llm-docsum-gaudi-server" \
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'{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
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}
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function validate_megaservice() {
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# Curl the Mega Service
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curl http://${ip_address}:8888/v1/docsum -H "Content-Type: application/json" -d '{
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"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."}' > ${LOG_PATH}/curl_megaservice.log
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echo "Checking response results, make sure the output is reasonable. "
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local status=false
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if [[ -f $LOG_PATH/curl_megaservice.log ]] && \
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[[ $(grep -c "versatile toolkit" $LOG_PATH/curl_megaservice.log) != 0 ]] && \
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[[ $(grep -c "offering" $LOG_PATH/curl_megaservice.log) != 0 ]] && \
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[[ $(grep -c "extraction" $LOG_PATH/curl_megaservice.log) != 0 ]]; then
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status=true
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fi
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if [ $status == false ]; then
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echo "Response check failed, please check the logs in artifacts!"
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exit 1
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else
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echo "Response check succeed!"
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fi
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echo "Checking response format, make sure the output format is acceptable for UI."
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# TODO
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validate_services \
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"${ip_address}:8888/v1/docsum" \
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"versatile toolkit" \
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"mega-docsum" \
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"docsum-gaudi-backend-server" \
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'{"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."}'
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}
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function stop_docker() {
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@@ -41,58 +41,62 @@ function start_services() {
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sleep 2m # Waits 2 minutes
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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 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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# TODO: Any results check required??
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curl http://${ip_address}:8008/generate \
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-X POST \
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-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
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-H 'Content-Type: application/json' > ${LOG_PATH}/generate.log
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exit_code=$?
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if [ $exit_code -ne 0 ]; then
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echo "Microservice failed, please check the logs in artifacts!"
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docker logs tgi_service >> ${LOG_PATH}/generate.log
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exit 1
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fi
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sleep 5s
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curl http://${ip_address}:9000/v1/chat/docsum \
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-X POST \
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-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."}' \
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-H 'Content-Type: application/json' > ${LOG_PATH}/completions.log
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exit_code=$?
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if [ $exit_code -ne 0 ]; then
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echo "Microservice failed, please check the logs in artifacts!"
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docker logs llm-docsum-server >> ${LOG_PATH}/completions.log
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exit 1
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fi
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sleep 5s
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# tgi for llm service
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validate_services \
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"${ip_address}:8008/generate" \
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"generated_text" \
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"tgi-llm" \
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"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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"${ip_address}:9000/v1/chat/docsum" \
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"data: " \
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"llm" \
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"llm-docsum-server" \
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'{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
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}
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function validate_megaservice() {
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# Curl the Mega Service
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curl http://${ip_address}:8888/v1/docsum -H "Content-Type: application/json" -d '{
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"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."}' > ${LOG_PATH}/curl_megaservice.log
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echo "Checking response results, make sure the output is reasonable. "
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local status=false
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if [[ -f $LOG_PATH/curl_megaservice.log ]] && \
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[[ $(grep -c " flexible" $LOG_PATH/curl_megaservice.log) != 0 ]] && \
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[[ $(grep -c " tool" $LOG_PATH/curl_megaservice.log) != 0 ]] && \
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[[ $(grep -c "kit" $LOG_PATH/curl_megaservice.log) != 0 ]]; then
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status=true
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fi
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if [ $status == false ]; then
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echo "Response check failed, please check the logs in artifacts!"
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exit 1
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else
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echo "Response check succeed!"
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fi
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echo "Checking response format, make sure the output format is acceptable for UI."
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# TODO
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validate_services \
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"${ip_address}:8888/v1/docsum" \
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"versatile toolkit" \
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"mega-docsum" \
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"docsum-xeon-backend-server" \
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'{"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."}'
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}
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function stop_docker() {
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