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Add VisualQnA docker for both Gaudi and Xeon using TGI serving (#547)

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* Add VisualQnA docker for both Gaudi and Xeon

Signed-off-by: lvliang-intel <liang1.lv@intel.com>

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Signed-off-by: lvliang-intel <liang1.lv@intel.com>

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Signed-off-by: lvliang-intel <liang1.lv@intel.com>
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VisualQnA/README.md
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@@ -18,61 +18,63 @@ This example guides you through how to deploy a [LLaVA](https://llava-vl.github.
![llava screenshot](./assets/img/llava_screenshot1.png)
![llava-screenshot](./assets/img/llava_screenshot2.png)
## Start the LLaVA service
# Deploy VisualQnA Service
1. Build the Docker image needed for starting the service
The VisualQnA service can be effortlessly deployed on either Intel Gaudi2 or Intel XEON Scalable Processors.
```
cd serving/
docker build . --build-arg http_proxy=${http_proxy} --build-arg https_proxy=${http_proxy} -t intel/gen-ai-examples:llava-gaudi
Currently we support deploying VisualQnA services with docker compose.
## Setup Environment Variable
To set up environment variables for deploying VisualQnA services, follow these steps:
1. Set the required environment variables:
```bash
# Example: host_ip="192.168.1.1"
export host_ip="External_Public_IP"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy"
```
2. If you are in a proxy environment, also set the proxy-related environment variables:
```bash
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
```
3. Set up other environment variables:
> Notice that you can only choose **one** command below to set up envs according to your hardware. Other that the port numbers may be set incorrectly.
```bash
# on Gaudi
source ./docker/gaudi/set_env.sh
# on Xeon
source ./docker/xeon/set_env.sh
```
## Deploy VisualQnA on Gaudi
Refer to the [Gaudi Guide](./docker/gaudi/README.md) to build docker images from source.
Find the corresponding [compose.yaml](./docker/gaudi/compose.yaml).
```bash
cd GenAIExamples/VisualQnA/docker/gaudi/
docker compose up -d
```
2. Start the LLaVA service on Intel Gaudi2
> Notice: Currently only the **Habana Driver 1.16.x** is supported for Gaudi.
## Deploy VisualQnA on Xeon
Refer to the [Xeon Guide](./docker/xeon/README.md) for more instructions on building docker images from source.
Find the corresponding [compose.yaml](./docker/xeon/compose.yaml).
```bash
cd GenAIExamples/VisualQnA/docker/xeon/
docker compose up -d
```
docker run -d -p 8085:8000 -v ./data:/root/.cache/huggingface/hub/ -e http_proxy=$http_proxy -e https_proxy=$http_proxy --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host intel/gen-ai-examples:llava-gaudi
```
Here are some explanation about the above parameters:
- `-p 8085:8000`: This will map the 8000 port of the LLaVA service inside the container to the 8085 port on the host
- `-v ./data:/root/.cache/huggingface/hub/`: This is to prevent from re-downloading model files
- `http_proxy` and `https_proxy` are used if you have some proxy setting
- `--runtime=habana ...` is required for running this service on Intel Gaudi2
Now you have a LLaVa service with the exposed port `8085` and you can check whether this service is up by:
```
curl localhost:8085/health -v
```
If the reply has a `200 OK`, then the service is up.
## Start the Gradio app
Now you have two options to start the frontend UI by following commands:
### English Interface (Default)
```
cd ui/
pip install -r requirements.txt
http_proxy= python app.py --host 0.0.0.0 --port 7860 --worker-addr http://localhost:8085 --share
```
### Chinese Interface
```
cd ui/
pip install -r requirements.txt
http_proxy= python app.py --host 0.0.0.0 --port 7860 --worker-addr http://localhost:8085 --lang CN --share
```
Here are some explanation about the above parameters:
- `--host`: the host of the gradio app
- `--port`: the port of the gradio app, by default 7860
- `--worker-addr`: the LLaVA service IP address. If you setup the service on a different machine, please replace `localhost` to the IP address of your Gaudi2 host machine
- `--lang`: Specify this parameter to use the Chinese interface. The default UI language is English and can be used without any additional parameter.
SCRIPT USAGE NOTICE:  By downloading and using any script file included with the associated software package (such as files with .bat, .cmd, or .JS extensions, Docker files, or any other type of file that, when executed, automatically downloads and/or installs files onto your system) (the “Script File”), it is your obligation to review the Script File to understand what files (e.g.,  other software, AI models, AI Datasets) the Script File will download to your system (“Downloaded Files”). Furthermore, by downloading and using the Downloaded Files, even if they are installed through a silent install, you agree to any and all terms and conditions associated with such files, including but not limited to, license terms, notices, or disclaimers.

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@@ -0,0 +1,33 @@
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
FROM python:3.11-slim
RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \
libgl1-mesa-glx \
libjemalloc-dev \
vim \
git
RUN useradd -m -s /bin/bash user && \
mkdir -p /home/user && \
chown -R user /home/user/
WORKDIR /home/user/
RUN git clone https://github.com/opea-project/GenAIComps.git
WORKDIR /home/user/GenAIComps
RUN pip install --no-cache-dir --upgrade pip && \
pip install --no-cache-dir -r /home/user/GenAIComps/requirements.txt
COPY ./visualqna.py /home/user/visualqna.py
ENV PYTHONPATH=$PYTHONPATH:/home/user/GenAIComps
USER user
WORKDIR /home/user
ENTRYPOINT ["python", "visualqna.py"]

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# Build MegaService of VisualQnA on Gaudi
This document outlines the deployment process for a VisualQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Gaudi server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as llm. We will publish the Docker images to Docker Hub, it will simplify the deployment process for this service.
## 🚀 Build Docker Images
First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.
### 1. Source Code install GenAIComps
```bash
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
```
### 2. Build LLM Image
```bash
docker build --no-cache -t opea/lvm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/Dockerfile_tgi .
```
### 3. Build TGI Gaudi Image
Since TGI Gaudi has not supported llava-next in main branch, we'll need to build it from a PR branch for now.
```bash
git clone https://github.com/yuanwu2017/tgi-gaudi.git
cd tgi-gaudi/
git checkout v2.0.4
docker build -t opea/llava-tgi:latest .
cd ../
```
### 4. Build MegaService Docker Image
To construct the Mega Service, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `visuralqna.py` Python script. Build the MegaService Docker image using the command below:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/VisualQnA/docker
docker build --no-cache -t opea/visualqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
cd ../../..
```
### 5. Build UI Docker Image
Build frontend Docker image via below command:
```bash
cd GenAIExamples/VisualQnA/docker/ui/
docker build --no-cache -t opea/visualqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
cd ../../../..
```
Then run the command `docker images`, you will have the following 4 Docker Images:
1. `opea/llava-tgi:latest`
2. `opea/lvm-tgi:latest`
3. `opea/visualqna:latest`
4. `opea/visualqna-ui:latest`
## 🚀 Start MicroServices and MegaService
### Setup Environment Variables
Since the `compose.yaml` will consume some environment variables, you need to setup them in advance as below.
```bash
export no_proxy=${your_no_proxy}
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}
export LVM_MODEL_ID="llava-hf/llava-v1.6-mistral-7b-hf"
export LVM_ENDPOINT="http://${host_ip}:8399"
export LVM_SERVICE_PORT=9399
export MEGA_SERVICE_HOST_IP=${host_ip}
export LVM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/visualqna"
```
Note: Please replace with `host_ip` with you external IP address, do **NOT** use localhost.
### Start all the services Docker Containers
```bash
cd GenAIExamples/VisualQnA/docker/gaudi/
```
```bash
docker compose -f compose.yaml up -d
```
> **_NOTE:_** Users need at least one Gaudi cards to run the VisualQnA successfully.
### Validate MicroServices and MegaService
Follow the instructions to validate MicroServices.
1. LLM Microservice
```bash
http_proxy="" curl http://${host_ip}:9399/v1/lvm -XPOST -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}' -H 'Content-Type: application/json'
```
2. MegaService
```bash
curl http://${host_ip}:8888/v1/visualqna -H "Content-Type: application/json" -d '{
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What'\''s in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
}
}
]
}
],
"max_tokens": 300
}'
```
## 🚀 Launch the UI
To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose.yaml` file as shown below:
```yaml
visualqna-gaudi-ui-server:
image: opea/visualqna-ui:latest
...
ports:
- "80:5173"
```

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# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
version: "3.8"
services:
llava-tgi-service:
image: opea/llava-tgi:latest
container_name: tgi-llava-gaudi-server
ports:
- "8399:80"
volumes:
- "./data:/data"
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
HF_HUB_DISABLE_PROGRESS_BARS: 1
HF_HUB_ENABLE_HF_TRANSFER: 0
HABANA_VISIBLE_DEVICES: all
OMPI_MCA_btl_vader_single_copy_mechanism: none
runtime: habana
cap_add:
- SYS_NICE
ipc: host
command: --model-id ${LVM_MODEL_ID} --max-input-length 4096 --max-total-tokens 8192
lvm-tgi:
image: opea/lvm-tgi:latest
container_name: lvm-tgi-gaudi-server
depends_on:
- llava-tgi-service
ports:
- "9399:9399"
ipc: host
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
LVM_ENDPOINT: ${LVM_ENDPOINT}
HF_HUB_DISABLE_PROGRESS_BARS: 1
HF_HUB_ENABLE_HF_TRANSFER: 0
restart: unless-stopped
visualqna-gaudi-backend-server:
image: opea/visualqna:latest
container_name: visualqna-gaudi-backend-server
depends_on:
- llava-tgi-service
- lvm-tgi
ports:
- "8888:8888"
environment:
- no_proxy=${no_proxy}
- https_proxy=${https_proxy}
- http_proxy=${http_proxy}
- MEGA_SERVICE_HOST_IP=${MEGA_SERVICE_HOST_IP}
- LVM_SERVICE_HOST_IP=${LVM_SERVICE_HOST_IP}
ipc: host
restart: always
visualqna-gaudi-ui-server:
image: opea/visualqna-ui:latest
container_name: visualqna-gaudi-ui-server
depends_on:
- visualqna-gaudi-backend-server
ports:
- "5173:5173"
environment:
- no_proxy=${no_proxy}
- https_proxy=${https_proxy}
- http_proxy=${http_proxy}
- CHAT_BASE_URL=${BACKEND_SERVICE_ENDPOINT}
ipc: host
restart: always
networks:
default:
driver: bridge

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#!/usr/bin/env bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
export LVM_MODEL_ID="llava-hf/llava-v1.6-mistral-7b-hf"
export LVM_ENDPOINT="http://${host_ip}:8399"
export LVM_SERVICE_PORT=9399
export MEGA_SERVICE_HOST_IP=${host_ip}
export LVM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/visualqna"

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# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import os
from comps import MicroService, ServiceOrchestrator, ServiceType, VisualQnAGateway
MEGA_SERVICE_HOST_IP = os.getenv("MEGA_SERVICE_HOST_IP", "0.0.0.0")
MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
LVM_SERVICE_HOST_IP = os.getenv("LVM_SERVICE_HOST_IP", "0.0.0.0")
LVM_SERVICE_PORT = int(os.getenv("LLM_SERVICE_PORT", 9399))
class VisualQnAService:
def __init__(self, host="0.0.0.0", port=8000):
self.host = host
self.port = port
self.megaservice = ServiceOrchestrator()
def add_remote_service(self):
llm = MicroService(
name="lvm",
host=LVM_SERVICE_HOST_IP,
port=LVM_SERVICE_PORT,
endpoint="/v1/lvm",
use_remote_service=True,
service_type=ServiceType.LVM,
)
self.megaservice.add(llm)
self.gateway = VisualQnAGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)
if __name__ == "__main__":
visualqna = VisualQnAService(host=MEGA_SERVICE_HOST_IP, port=MEGA_SERVICE_PORT)
visualqna.add_remote_service()

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# Build Mega Service of VisualQnA on Xeon
This document outlines the deployment process for a VisualQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `llm`. We will publish the Docker images to Docker Hub soon, it will simplify the deployment process for this service.
## 🚀 Apply Xeon Server on AWS
To apply a Xeon server on AWS, start by creating an AWS account if you don't have one already. Then, head to the [EC2 Console](https://console.aws.amazon.com/ec2/v2/home) to begin the process. Within the EC2 service, select the Amazon EC2 M7i or M7i-flex instance type to leverage the power of 4th Generation Intel Xeon Scalable processors. These instances are optimized for high-performance computing and demanding workloads.
For detailed information about these instance types, you can refer to this [link](https://aws.amazon.com/ec2/instance-types/m7i/). Once you've chosen the appropriate instance type, proceed with configuring your instance settings, including network configurations, security groups, and storage options.
After launching your instance, you can connect to it using SSH (for Linux instances) or Remote Desktop Protocol (RDP) (for Windows instances). From there, you'll have full access to your Xeon server, allowing you to install, configure, and manage your applications as needed.
**Certain ports in the EC2 instance need to opened up in the security group, for the microservices to work with the curl commands**
> See one example below. Please open up these ports in the EC2 instance based on the IP addresses you want to allow
```
llava-tgi-service
===========
Port 8399 - Open to 0.0.0.0/0
llm
===
Port 9399 - Open to 0.0.0.0/0
visualqna-xeon-backend-server
==========================
Port 8888 - Open to 0.0.0.0/0
visualqna-xeon-ui-server
=====================
Port 5173 - Open to 0.0.0.0/0
```
## 🚀 Build Docker Images
First of all, you need to build Docker Images locally and install the python package of it.
```bash
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
```
### 1. Build LVM Image
```bash
docker build --no-cache -t opea/lvm-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/lvms/Dockerfile_tgi .
```
### 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 `visualqna.py` Python script. Build MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/VisualQnA/docker
docker build --no-cache -t opea/visualqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
cd ../../..
```
### 3. Build UI Docker Image
Build frontend Docker image via below command:
```bash
cd GenAIExamples/VisualQnA/docker/ui/
docker build --no-cache -t opea/visualqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
cd ../../../..
```
### 4. Pull TGI image
```bash
docker pull ghcr.io/huggingface/text-generation-inference:2.2.0
```
Then run the command `docker images`, you will have the following 4 Docker Images:
1. `ghcr.io/huggingface/text-generation-inference:2.2.0`
2. `opea/lvm-tgi:latest`
3. `opea/visualqna:latest`
4. `opea/visualqna-ui:latest`
## 🚀 Start Microservices
### Setup Environment Variables
Since the `compose.yaml` will consume some environment variables, you need to setup them in advance as below.
**Export the value of the public IP address of your Xeon server to the `host_ip` environment variable**
> Change the External_Public_IP below with the actual IPV4 value
```
export host_ip="External_Public_IP"
```
**Append the value of the public IP address to the no_proxy list**
```
export your_no_proxy=${your_no_proxy},"External_Public_IP"
```
```bash
export no_proxy=${your_no_proxy}
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy}
export LVM_MODEL_ID="llava-hf/llava-v1.6-mistral-7b-hf"
export LVM_ENDPOINT="http://${host_ip}:8399"
export LVM_SERVICE_PORT=9399
export MEGA_SERVICE_HOST_IP=${host_ip}
export LVM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/visualqna"
```
Note: Please replace with `host_ip` with you external IP address, do not use localhost.
### Start all the services Docker Containers
> Before running the docker compose command, you need to be in the folder that has the docker compose yaml file
```bash
cd GenAIExamples/VisualQnA/docker/xeon/
```
```bash
docker compose -f compose.yaml up -d
```
### Validate Microservices
Follow the instructions to validate MicroServices.
1. LLM Microservice
```bash
http_proxy="" curl http://${host_ip}:9399/v1/lvm -XPOST -d '{"image": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAKCAYAAACNMs+9AAAAFUlEQVR42mP8/5+hnoEIwDiqkL4KAcT9GO0U4BxoAAAAAElFTkSuQmCC", "prompt":"What is this?"}' -H 'Content-Type: application/json'
```
2. MegaService
```bash
curl http://${host_ip}:8888/v1/visualqna -H "Content-Type: application/json" -d '{
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What'\''s in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
}
}
]
}
],
"max_tokens": 300
}'
```
## 🚀 Launch the UI
To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the `compose.yaml` file as shown below:
```yaml
visualqna-gaudi-ui-server:
image: opea/visualqna-ui:latest
...
ports:
- "80:5173"
```

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# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
version: "3.8"
services:
llava-tgi-service:
image: ghcr.io/huggingface/text-generation-inference:2.2.0
container_name: tgi-llava-xeon-server
ports:
- "9399:80"
volumes:
- "./data:/data"
shm_size: 1g
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
HF_HUB_DISABLE_PROGRESS_BARS: 1
HF_HUB_ENABLE_HF_TRANSFER: 0
command: --model-id ${LVM_MODEL_ID}
lvm-tgi:
image: opea/lvm-tgi:latest
container_name: lvm-tgi-server
depends_on:
- llava-tgi-service
ports:
- "9399:9399"
ipc: host
environment:
no_proxy: ${no_proxy}
http_proxy: ${http_proxy}
https_proxy: ${https_proxy}
LVM_ENDPOINT: ${LVM_ENDPOINT}
HF_HUB_DISABLE_PROGRESS_BARS: 1
HF_HUB_ENABLE_HF_TRANSFER: 0
restart: unless-stopped
visualqna-xeon-backend-server:
image: opea/visualqna:latest
container_name: visualqna-xeon-backend-server
depends_on:
- llava-tgi-service
- lvm-tgi
ports:
- "8888:8888"
environment:
- no_proxy=${no_proxy}
- https_proxy=${https_proxy}
- http_proxy=${http_proxy}
- MEGA_SERVICE_HOST_IP=${MEGA_SERVICE_HOST_IP}
- LVM_SERVICE_HOST_IP=${LVM_SERVICE_HOST_IP}
ipc: host
restart: always
visualqna-xeon-ui-server:
image: opea/visualqna-ui:latest
container_name: visualqna-xeon-ui-server
depends_on:
- visualqna-xeon-backend-server
ports:
- "5173:5173"
environment:
- no_proxy=${no_proxy}
- https_proxy=${https_proxy}
- http_proxy=${http_proxy}
- CHAT_BASE_URL=${BACKEND_SERVICE_ENDPOINT}
ipc: host
restart: always
networks:
default:
driver: bridge

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#!/usr/bin/env bash
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
export LVM_MODEL_ID="llava-hf/llava-v1.6-mistral-7b-hf"
export LVM_ENDPOINT="http://${host_ip}:8399"
export LVM_SERVICE_PORT=9399
export MEGA_SERVICE_HOST_IP=${host_ip}
export LVM_SERVICE_HOST_IP=${host_ip}
export BACKEND_SERVICE_ENDPOINT="http://${host_ip}:8888/v1/visualqna"