Build and deploy Translation Application on AMD GPU (ROCm)¶

Build images¶

Build the LLM Docker Image¶

### Cloning repo
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps

### Build Docker image
docker build -t opea/llm-textgen:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/src/text-generation/Dockerfile .

Build the MegaService Docker Image¶

### Cloning repo
git clone https://github.com/opea-project/GenAIExamples
cd GenAIExamples/Translation/

### Build Docker image
docker build -t opea/translation:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .

Build the UI Docker Image¶

cd GenAIExamples/Translation/ui
### Build UI Docker image
docker build -t opea/translation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile .

Deploy Translation Application¶

Features of Docker compose for AMD GPUs¶

Added forwarding of GPU devices to the container TGI service with instructions:

shm_size: 1g
devices:
  - /dev/kfd:/dev/kfd
  - /dev/dri/:/dev/dri/
cap_add:
  - SYS_PTRACE
group_add:
  - video
security_opt:
  - seccomp:unconfined

In this case, all GPUs are thrown. To reset a specific GPU, you need to use specific device names cardN and renderN.

For example:

shm_size: 1g
devices:
  - /dev/kfd:/dev/kfd
  - /dev/dri/card0:/dev/dri/card0
  - /dev/dri/render128:/dev/dri/render128
cap_add:
  - SYS_PTRACE
group_add:
  - video
security_opt:
  - seccomp:unconfined

To find out which GPU device IDs cardN and renderN correspond to the same GPU, use the GPU driver utility

Go to the directory with the Docker compose file¶

cd GenAIExamples/Translation/docker_compose/amd/gpu/rocm

Set environments¶

In the file “GenAIExamples/Translation/docker_compose/amd/gpu/rocm/set_env.sh “ it is necessary to set the required values. Parameter assignments are specified in the comments for each variable setting command

chmod +x set_env.sh
. set_env.sh

Run services¶

docker compose up -d

Validate the MicroServices and MegaService¶

Validate TGI service¶

curl http://${TRANSLATION_HOST_IP}:${TRANSLATIONS_TGI_SERVICE_PORT}/generate \
  -X POST \
  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
  -H 'Content-Type: application/json'

Validate LLM service¶

curl http://${TRANSLATION_HOST_IP}:9000/v1/chat/completions \
  -X POST \
  -d '{"query":"Translate this from Chinese to English:\nChinese: 我爱机器翻译。\nEnglish:"}' \
  -H 'Content-Type: application/json'

Validate MegaService¶

curl http://${TRANSLATION_HOST_IP}:${TRANSLATION_BACKEND_SERVICE_PORT}/v1/translation -H "Content-Type: application/json" -d '{
     "language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'

Validate Nginx service¶

curl http://${TRANSLATION_HOST_IP}:${TRANSLATION_NGINX_PORT}/v1/translation \
    -H "Content-Type: application/json" \
    -d '{"language_from": "Chinese","language_to": "English","source_language": "我爱机器翻译。"}'