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LVM Microservice

Visual Question and Answering is one of the multimodal tasks empowered by LVMs (Large Visual Models). This microservice supports visual Q&A by using LLaVA as the base large visual model. It accepts two inputs: a prompt and an image. It outputs the answer to the prompt about the image.

🚀1. Start Microservice with Python (Option 1)

1.1 Install Requirements

pip install -r requirements.txt

1.2 Start LLaVA Service/Test

  • Xeon CPU
# Start LLaVA service
cd dependency/
nohup python llava_server.py --device=cpu &
# Wait until the server is up
# Test
python check_llava_server.py
  • Gaudi2 HPU
pip install optimum[habana]
cd dependency/
# Start LLaVA service
nohup python llava_server.py &
# Test
python check_llava_server.py

1.3 Start Image To Text Service/Test

cd ..
# Start the OPEA Microservice
python lvm.py
# Test
python check_lvm.py

🚀2. Start Microservice with Docker (Option 2)

2.1 Build Images

2.1.1 LLaVA Server Image

  • Xeon CPU
cd ../../../
docker build -t opea/lvm-llava:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/llava/dependency/Dockerfile .
  • Gaudi2 HPU
cd ../../../
docker build -t opea/lvm-llava:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/llava/dependency/Dockerfile.intel_hpu .

2.1.2 LVM Service Image

cd ../../../
docker build -t opea/lvm-llava-svc:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/llava/Dockerfile .

2.2 Start LLaVA and LVM Service

2.2.1 Start LLaVA server

  • Xeon
docker run -p 8399:8399 -e http_proxy=$http_proxy --ipc=host -e https_proxy=$https_proxy opea/lvm-llava:latest
  • Gaudi2 HPU
docker run -p 8399:8399 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy opea/lvm-llava:latest

2.2.2 Start LVM service

ip_address=$(hostname -I | awk '{print $1}')

docker run -p 9399:9399 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e LVM_ENDPOINT=http://$ip_address:8399 opea/lvm-llava-svc:latest

2.2.3 Test

# Use curl/python

# curl with an image and a prompt
http_proxy="" curl http://localhost:9399/v1/lvm -XPOST -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}' -H 'Content-Type: application/json'

# curl with a prompt only (no image)
http_proxy="" curl http://localhost:9399/v1/lvm -XPOST -d '{"image": "", "prompt":"What is deep learning?"}' -H 'Content-Type: application/json'

# python
python check_lvm.py