71e3c57366
Update all the names for classes and files in llm comps to follow the standard format, related GenAIComps PR opea-project/GenAIComps#1162 Signed-off-by: Xinyao Wang <xinyao.wang@intel.com>
176 lines
6.4 KiB
Markdown
176 lines
6.4 KiB
Markdown
# Build and deploy DocSum Application on AMD GPU (ROCm)
|
|
|
|
## Build images
|
|
|
|
## 🚀 Build Docker Images
|
|
|
|
First of all, you need to build Docker Images locally and install the python package of it.
|
|
|
|
### 1. Build LLM Image
|
|
|
|
```bash
|
|
git clone https://github.com/opea-project/GenAIComps.git
|
|
cd GenAIComps
|
|
docker build -t opea/llm-docsum-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/src/doc-summarization/Dockerfile .
|
|
```
|
|
|
|
Then run the command `docker images`, you will have the following four Docker Images:
|
|
|
|
### 2. Build MegaService Docker Image
|
|
|
|
To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `docsum.py` Python script. Build the MegaService Docker image via below command:
|
|
|
|
```bash
|
|
git clone https://github.com/opea-project/GenAIExamples
|
|
cd GenAIExamples/DocSum/
|
|
docker build -t opea/docsum:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
|
|
```
|
|
|
|
### 3. Build UI Docker Image
|
|
|
|
Build the frontend Docker image via below command:
|
|
|
|
```bash
|
|
cd GenAIExamples/DocSum/ui
|
|
docker build -t opea/docsum-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f docker/Dockerfile .
|
|
```
|
|
|
|
Then run the command `docker images`, you will have the following Docker Images:
|
|
|
|
1. `opea/llm-docsum-tgi:latest`
|
|
2. `opea/docsum:latest`
|
|
3. `opea/docsum-ui:latest`
|
|
|
|
### 4. Build React UI Docker Image
|
|
|
|
Build the frontend Docker image via below command:
|
|
|
|
```bash
|
|
cd GenAIExamples/DocSum/ui
|
|
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/docsum"
|
|
docker build -t opea/docsum-react-ui:latest --build-arg BACKEND_SERVICE_ENDPOINT=$BACKEND_SERVICE_ENDPOINT -f ./docker/Dockerfile.react .
|
|
|
|
docker build -t opea/docsum-react-ui:latest --build-arg BACKEND_SERVICE_ENDPOINT=$BACKEND_SERVICE_ENDPOINT --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
|
|
```
|
|
|
|
Then run the command `docker images`, you will have the following Docker Images:
|
|
|
|
1. `opea/llm-docsum-tgi:latest`
|
|
2. `opea/docsum:latest`
|
|
3. `opea/docsum-ui:latest`
|
|
4. `opea/docsum-react-ui:latest`
|
|
|
|
## 🚀 Start Microservices and MegaService
|
|
|
|
### Required Models
|
|
|
|
Default model is "Intel/neural-chat-7b-v3-3". Change "LLM_MODEL_ID" in environment variables below if you want to use another model.
|
|
For gated models, you also need to provide [HuggingFace token](https://huggingface.co/docs/hub/security-tokens) in "HUGGINGFACEHUB_API_TOKEN" environment variable.
|
|
|
|
### Setup Environment Variables
|
|
|
|
Since the `compose.yaml` will consume some environment variables, you need to setup them in advance as below.
|
|
|
|
```bash
|
|
export DOCSUM_TGI_IMAGE="ghcr.io/huggingface/text-generation-inference:2.3.1-rocm"
|
|
export DOCSUM_LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
|
|
export HOST_IP=${host_ip}
|
|
export DOCSUM_TGI_SERVICE_PORT="18882"
|
|
export DOCSUM_TGI_LLM_ENDPOINT="http://${HOST_IP}:${DOCSUM_TGI_SERVICE_PORT}"
|
|
export DOCSUM_HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
|
|
export DOCSUM_LLM_SERVER_PORT="8008"
|
|
export DOCSUM_BACKEND_SERVER_PORT="8888"
|
|
export DOCSUM_FRONTEND_PORT="5173"
|
|
export DocSum_COMPONENT_NAME="OpeaDocSumTgi"
|
|
```
|
|
|
|
Note: Please replace with `host_ip` with your external IP address, do not use localhost.
|
|
|
|
Note: In order to limit access to a subset of GPUs, please pass each device individually using one or more -device /dev/dri/rendered<node>, where <node> is the card index, starting from 128. (https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html#docker-restrict-gpus)
|
|
|
|
Example for set isolation for 1 GPU
|
|
|
|
```
|
|
- /dev/dri/card0:/dev/dri/card0
|
|
- /dev/dri/renderD128:/dev/dri/renderD128
|
|
```
|
|
|
|
Example for set isolation for 2 GPUs
|
|
|
|
```
|
|
- /dev/dri/card0:/dev/dri/card0
|
|
- /dev/dri/renderD128:/dev/dri/renderD128
|
|
- /dev/dri/card1:/dev/dri/card1
|
|
- /dev/dri/renderD129:/dev/dri/renderD129
|
|
```
|
|
|
|
Please find more information about accessing and restricting AMD GPUs in the link (https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/docker.html#docker-restrict-gpus)
|
|
|
|
### Start Microservice Docker Containers
|
|
|
|
```bash
|
|
cd GenAIExamples/DocSum/docker_compose/amd/gpu/rocm
|
|
docker compose up -d
|
|
```
|
|
|
|
### Validate Microservices
|
|
|
|
1. TGI Service
|
|
|
|
```bash
|
|
curl http://${host_ip}:8008/generate \
|
|
-X POST \
|
|
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":64, "do_sample": true}}' \
|
|
-H 'Content-Type: application/json'
|
|
```
|
|
|
|
2. LLM Microservice
|
|
|
|
```bash
|
|
curl http://${host_ip}:9000/v1/docsum \
|
|
-X POST \
|
|
-d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \
|
|
-H 'Content-Type: application/json'
|
|
```
|
|
|
|
3. MegaService
|
|
|
|
```bash
|
|
curl http://${host_ip}:8888/v1/docsum -H "Content-Type: application/json" -d '{
|
|
"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.","max_tokens":32, "language":"en", "stream":false
|
|
}'
|
|
```
|
|
|
|
## 🚀 Launch the Svelte UI
|
|
|
|
Open this URL `http://{host_ip}:5173` in your browser to access the frontend.
|
|
|
|
|
|
|
|
Here is an example for summarizing a article.
|
|
|
|
|
|
|
|
## 🚀 Launch the React UI (Optional)
|
|
|
|
To access the React-based frontend, modify the UI service in the `compose.yaml` file. Replace `docsum-rocm-ui-server` service with the `docsum-rocm-react-ui-server` service as per the config below:
|
|
|
|
```yaml
|
|
docsum-rocm-react-ui-server:
|
|
image: ${REGISTRY:-opea}/docsum-react-ui:${TAG:-latest}
|
|
container_name: docsum-rocm-react-ui-server
|
|
depends_on:
|
|
- docsum-rocm-backend-server
|
|
ports:
|
|
- "5174:80"
|
|
environment:
|
|
- no_proxy=${no_proxy}
|
|
- https_proxy=${https_proxy}
|
|
- http_proxy=${http_proxy}
|
|
- DOC_BASE_URL=${BACKEND_SERVICE_ENDPOINT}
|
|
```
|
|
|
|
Open this URL `http://{host_ip}:5175` in your browser to access the frontend.
|
|
|
|
|