cc1d97f816
Signed-off-by: chensuyue <suyue.chen@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: chensuyue <suyue.chen@intel.com>
138 lines
4.8 KiB
Markdown
138 lines
4.8 KiB
Markdown
# Build Mega Service of AudioQnA on AMD ROCm GPU
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This document outlines the deployment process for a AudioQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice
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pipeline on server on AMD ROCm GPU platform.
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## 🚀 Build Docker images
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### 1. Source Code install GenAIComps
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```bash
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git clone https://github.com/opea-project/GenAIComps.git
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cd GenAIComps
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```
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### 2. Build ASR Image
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```bash
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docker build -t opea/whisper:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/asr/src/integrations/dependency/whisper/Dockerfile .
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```
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### 3. Build LLM Image
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For compose for ROCm example AMD optimized image hosted in huggingface repo will be used for TGI service: ghcr.io/huggingface/text-generation-inference:2.3.1-rocm (https://github.com/huggingface/text-generation-inference)
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### 4. Build TTS Image
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```bash
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docker build -t opea/speecht5:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/tts/src/integrations/dependency/speecht5/Dockerfile .
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```
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### 5. Build MegaService Docker Image
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To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `audioqna.py` Python script. Build the MegaService Docker image using the command below:
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```bash
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git clone https://github.com/opea-project/GenAIExamples.git
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cd GenAIExamples/AudioQnA/
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docker build --no-cache -t opea/audioqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
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```
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Then run the command `docker images`, you will have following images ready:
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1. `opea/whisper:latest`
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2. `opea/speecht5:latest`
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3. `opea/audioqna:latest`
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## 🚀 Set the environment variables
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Before starting the services with `docker compose`, you have to recheck the following environment variables.
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```bash
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export host_ip=<your External Public IP> # export host_ip=$(hostname -I | awk '{print $1}')
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export HUGGINGFACEHUB_API_TOKEN=<your HF token>
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export LLM_MODEL_ID=Intel/neural-chat-7b-v3-3
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export MEGA_SERVICE_HOST_IP=${host_ip}
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export WHISPER_SERVER_HOST_IP=${host_ip}
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export SPEECHT5_SERVER_HOST_IP=${host_ip}
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export LLM_SERVER_HOST_IP=${host_ip}
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export WHISPER_SERVER_PORT=7066
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export SPEECHT5_SERVER_PORT=7055
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export LLM_SERVER_PORT=3006
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export BACKEND_SERVICE_ENDPOINT=http://${host_ip}:3008/v1/audioqna
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```
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or use set_env.sh file to setup environment variables.
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Note: Please replace with host_ip with your external IP address, do not use localhost.
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Note: In order to limit access to a subset of GPUs, please pass each device individually using one or more -device /dev/dri/rendered, where is the card index, starting from 128. (https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html#docker-restrict-gpus)
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Example for set isolation for 1 GPU
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- /dev/dri/card0:/dev/dri/card0
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- /dev/dri/renderD128:/dev/dri/renderD128
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Example for set isolation for 2 GPUs
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- /dev/dri/card0:/dev/dri/card0
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- /dev/dri/renderD128:/dev/dri/renderD128
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- /dev/dri/card0:/dev/dri/card0
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- /dev/dri/renderD129:/dev/dri/renderD129
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Please find more information about accessing and restricting AMD GPUs in the link (https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html#docker-restrict-gpus)
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## 🚀 Start the MegaService
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```bash
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cd GenAIExamples/AudioQnA/docker_compose/amd/gpu/rocm/
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docker compose up -d
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```
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In following cases, you could build docker image from source by yourself.
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- Failed to download the docker image.
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- If you want to use a specific version of Docker image.
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Please refer to 'Build Docker Images' in below.
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## 🚀 Consume the AudioQnA Service
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Test the AudioQnA megaservice by recording a .wav file, encoding the file into the base64 format, and then sending the
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base64 string to the megaservice endpoint. The megaservice will return a spoken response as a base64 string. To listen
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to the response, decode the base64 string and save it as a .wav file.
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```bash
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# voice can be "default" or "male"
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curl http://${host_ip}:3008/v1/audioqna \
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-X POST \
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-d '{"audio": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA", "max_tokens":64, "voice":"default"}' \
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-H 'Content-Type: application/json' | sed 's/^"//;s/"$//' | base64 -d > output.wav
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```
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## 🚀 Test MicroServices
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```bash
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# whisper service
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curl http://${host_ip}:7066/v1/asr \
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-X POST \
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-d '{"audio": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}' \
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-H 'Content-Type: application/json'
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# tgi service
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curl http://${host_ip}:3006/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'
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# speecht5 service
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curl http://${host_ip}:7055/v1/tts \
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-X POST \
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-d '{"text": "Who are you?"}' \
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-H 'Content-Type: application/json'
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```
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