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# Run Ollama, Stable Diffusion and Automatic Speech Recognition with your Intel Arc GPU
[[Blog](https://blog.eleiton.dev/posts/llm-and-genai-in-docker/)]
Effortlessly deploy a Docker-based solution that uses [Open WebUI](https://github.com/open-webui/open-webui) as your user-friendly
AI Interface and [Ollama](https://github.com/ollama/ollama) for integrating Large Language Models (LLM).
Additionally, you can run [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [SD.Next](https://github.com/vladmandic/sdnext) docker containers to
streamline Stable Diffusion capabilities.
You can also run an optional docker container with [OpenAI Whisper](https://github.com/openai/whisper) to perform Automatic Speech Recognition (ASR) tasks.
All these containers have been optimized for Intel Arc Series GPUs on Linux systems by using [Intel® Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).
![screenshot](resources/open-webui.png)
## Services
1. Ollama
* Runs llama.cpp and Ollama with IPEX-LLM on your Linux computer with Intel Arc GPU.
* Built following the guidelines from [Intel](https://github.com/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/README.md).
* Uses the official [Intel ipex-llm docker image](https://hub.docker.com/r/intelanalytics/ipex-llm-inference-cpp-xpu) as the base container.
* Uses the latest versions of required packages, prioritizing cutting-edge features over stability.
* Exposes port `11434` for connecting other tools to your Ollama service.
2. Open WebUI
* Uses the official distribution of Open WebUI.
* `WEBUI_AUTH` is turned off for authentication-free usage.
* `ENABLE_OPENAI_API` and `ENABLE_OLLAMA_API` flags are set to off and on, respectively, allowing interactions via Ollama only.
* `ENABLE_IMAGE_GENERATION` is set to true, allowing you to generate images from the UI.
* `IMAGE_GENERATION_ENGINE` is set to automatic1111 (SD.Next is compatible).
3. ComfyUI
* The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
* Uses as the base container the official [Intel® Extension for PyTorch](https://pytorch-extension.intel.com/installation?platform=gpu)
4. SD.Next
* All-in-one for AI generative image based on Automatic1111
* Uses as the base container the official [Intel® Extension for PyTorch](https://pytorch-extension.intel.com/installation?platform=gpu)
* Uses a customized version of the SD.Next [docker file](https://github.com/vladmandic/sdnext/blob/dev/configs/Dockerfile.ipex), making it compatible with the Intel Extension for Pytorch image.
5. OpenAI Whisper
* Robust Speech Recognition via Large-Scale Weak Supervision
* Uses as the base container the official [Intel® Extension for PyTorch](* Uses as the base container the official [Intel® Extension for PyTorch](https://pytorch-extension.intel.com/installation?platform=gpu)
## Setup
Run the following commands to start your Ollama instance with Open WebUI
```bash
$ git clone https://github.com/eleiton/ollama-intel-arc.git
$ cd ollama-intel-arc
$ podman compose up
```
Additionally, if you want to run one or more of the image generation tools, run these command in a different terminal:
For ComfyUI
```bash
$ podman compose -f docker-compose.comfyui.yml up
```
For SD.Next
```bash
$ podman compose -f docker-compose.sdnext.yml up
```
If you want to run Whisper for automatic speech recognition, run this command in a different terminal:
```bash
$ podman compose -f docker-compose.whisper.yml up
```
## Validate
Run the following command to verify your Ollama instance is up and running
```bash
$ curl http://localhost:11434/
Ollama is running
```
When using Open WebUI, you should see this partial output in your console, indicating your arc gpu was detected
```bash
[ollama-intel-arc] | Found 1 SYCL devices:
[ollama-intel-arc] | | | | | |Max | |Max |Global | |
[ollama-intel-arc] | | | | | |compute|Max work|sub |mem | |
[ollama-intel-arc] | |ID| Device Type| Name|Version|units |group |group|size | Driver version|
[ollama-intel-arc] | |--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|
[ollama-intel-arc] | | 0| [level_zero:gpu:0]| Intel Arc Graphics| 12.71| 128| 1024| 32| 62400M| 1.6.32224+14|
```
## Using Image Generation
* Open your web browser to http://localhost:7860 to access the SD.Next web page.
* For the purposes of this demonstration, we'll use the [DreamShaper](https://civitai.com/models/4384/dreamshaper) model.
* Follow these steps:
* Download the `dreamshaper_8` model by clicking on its image (1).
* Wait for it to download (~2GB in size) and then select it in the dropbox (2).
* (Optional) If you want to stay in the SD.Next UI, feel free to explore (3).
![screenshot](resources/sd.next.png)
* For more information on using SD.Next, refer to the official [documentation](https://vladmandic.github.io/sdnext-docs/).
* Open your web browser to http://localhost:4040 to access the Open WebUI web page.
* Go to the administrator [settings](http://localhost:4040/admin/settings) page.
* Go to the Image section (1)
* Make sure all settings look good, and validate them pressing the refresh button (2)
* (Optional) Save any changes if you made them. (3)
![screenshot](resources/open-webui-settings.png)
* For more information on using Open WebUI, refer to the official [documentation](https://docs.openwebui.com/)
* That's it, go back to Open WebUI main page and start chatting. Make sure to select the `Image` button to indicate you want to generate Images.
![screenshot](resources/open-webui-chat.png)
## Using Automatic Speech Recognition
* This is an example of a command to transcribe audio files:
```bash
podman exec -it whisper-ipex whisper https://www.lightbulblanguages.co.uk/resources/ge-audio/hobbies-ge.mp3 --device xpu --model small --language German --task transcribe
```
* Response:
```bash
[00:00.000 --> 00:08.000] Ich habe viele Hobbys. In meiner Freizeit mache ich sehr gerne Sport, wie zum Beispiel Wasserball oder Radfahren.
[00:08.000 --> 00:13.000] Außerdem lese ich gerne und lerne auch gerne Fremdsprachen.
[00:13.000 --> 00:19.000] Ich gehe gerne ins Kino, höre gerne Musik und treffe mich mit meinen Freunden.
[00:19.000 --> 00:22.000] Früher habe ich auch viel Basketball gespielt.
[00:22.000 --> 00:26.000] Im Frühling und im Sommer werde ich viele Radtouren machen.
[00:26.000 --> 00:29.000] Außerdem werde ich viel schwimmen gehen.
[00:29.000 --> 00:33.000] Am liebsten würde ich das natürlich im Meer machen.
```
* This is an example of a command to translate audio files:
```bash
podman exec -it whisper-ipex whisper https://www.lightbulblanguages.co.uk/resources/ge-audio/hobbies-ge.mp3 --device xpu --model small --language German --task translate
```
* Response:
```bash
[00:00.000 --> 00:02.000] I have a lot of hobbies.
[00:02.000 --> 00:05.000] In my free time I like to do sports,
[00:05.000 --> 00:08.000] such as water ball or cycling.
[00:08.000 --> 00:10.000] Besides, I like to read
[00:10.000 --> 00:13.000] and also like to learn foreign languages.
[00:13.000 --> 00:15.000] I like to go to the cinema,
[00:15.000 --> 00:16.000] like to listen to music
[00:16.000 --> 00:19.000] and meet my friends.
[00:19.000 --> 00:22.000] I used to play a lot of basketball.
[00:22.000 --> 00:26.000] In spring and summer I will do a lot of cycling tours.
[00:26.000 --> 00:29.000] Besides, I will go swimming a lot.
[00:29.000 --> 00:33.000] Of course, I would prefer to do this in the sea.
```
* To use your own audio files instead of web files, place them in the `~/whisper-files` folder and access them like this:
```bash
podman exec -it whisper-ipex whisper YOUR_FILE_NAME.mp3 --device xpu --model small --task translate
```
## Updating the containers
If there are new updates in the [ipex-llm-inference-cpp-xpu](https://hub.docker.com/r/intelanalytics/ipex-llm-inference-cpp-xpu) docker Image or in the Open WebUI docker Image, you may want to update your containers, to stay up to date.
Before any updates, be sure to stop your containers
```bash
$ podman compose down
```
Then just run a pull command to retrieve the `latest` images.
```bash
$ podman compose pull
```
After that, you can run compose up to start your services again.
```bash
$ podman compose up
```
## Manually connecting to your Ollama container
You can connect directly to your Ollama container by running these commands:
```bash
$ podman exec -it ollama-intel-arc /bin/bash
$ /llm/ollama/ollama -v
```
## My development environment:
* Core Ultra 7 155H
* Intel® Arc™ Graphics (Meteor Lake-P)
* Fedora 41
## References
* [Open WebUI documentation](https://docs.openwebui.com/)
* [Docker - Intel ipex-llm tags](https://hub.docker.com/r/intelanalytics/ipex-llm-serving-xpu/tags)
* [Docker - Intel extension for pytorch](https://hub.docker.com/r/intel/intel-extension-for-pytorch/tags)
* [GitHub - Intel ipex-llm tags](https://github.com/intel/ipex-llm/tags)
* [GitHub - Intel extension for pytorch](https://github.com/intel/intel-extension-for-pytorch/tags)

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FROM intel/intel-extension-for-pytorch:2.7.10-xpu
# Optional, might help with memory allocation performance and scalability
RUN apt-get update && \
apt-get install -y --no-install-recommends --fix-missing libjemalloc-dev
ENV LD_PRELOAD=libjemalloc.so.2
# Download the ComfyUI repository
RUN cat <<EOF > /bin/startup.sh
#!/bin/bash
git status || git clone https://github.com/comfyanonymous/ComfyUI.git /app
pip install -r /app/requirements.txt
python /app/main.py "\$@"
EOF
# Make the startup script executable
RUN chmod 755 /bin/startup.sh
# Set the working directory to /app
WORKDIR /app
# Run ComfyUI with custom parameters
CMD [ "startup.sh", "--highvram", "--use-pytorch-cross-attention", "--listen=0.0.0.0", "--port=8188" ]

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services:
comfyui-ipex:
build:
context: comfyui
dockerfile: Dockerfile
image: comfyui-ipex:local
container_name: comfyui-ipex
devices:
- /dev/dri:/dev/dri
ports:
- 8188:8188
volumes:
- comfyui-app-volume:/app
- comfyui-python-volume:/usr/local/lib/python3.10
environment:
- no_proxy=localhost,127.0.0.1
volumes:
comfyui-app-volume: {}
comfyui-python-volume: {}

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services:
sdnext-ipex:
build:
context: sdnext
dockerfile: Dockerfile
image: sdnext-ipex:local
container_name: sdnext-ipex
restart: unless-stopped
devices:
- /dev/dri:/dev/dri
ports:
- 7860:7860
volumes:
- sdnext-app-volume:/app
- sdnext-mnt-volume:/mnt
- sdnext-huggingface-volume:/root/.cache/huggingface
- sdnext-python-volume:/usr/local/lib/python3.10
volumes:
sdnext-app-volume: {}
sdnext-mnt-volume: {}
sdnext-python-volume: {}
sdnext-huggingface-volume: {}

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services:
whisper-ipex:
build:
context: whisper
dockerfile: Dockerfile
image: whisper-ipex:local
container_name: whisper-ipex
restart: unless-stopped
devices:
- /dev/dri:/dev/dri
volumes:
- whisper-models-volume:/root/.cache/whisper
- ~/whisper-files:/app
volumes:
whisper-models-volume: {}

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services:
ollama-intel-arc:
image: intelanalytics/ipex-llm-inference-cpp-xpu:latest
container_name: ollama-intel-arc
restart: unless-stopped
devices:
- /dev/dri:/dev/dri
volumes:
- ollama-volume:/root/.ollama
ports:
- 11434:11434
environment:
- no_proxy=localhost,127.0.0.1
- OLLAMA_HOST=0.0.0.0
- DEVICE=Arc
- OLLAMA_INTEL_GPU=true
- OLLAMA_NUM_GPU=999
- ZES_ENABLE_SYSMAN=1
command: sh -c 'mkdir -p /llm/ollama && cd /llm/ollama && init-ollama && exec ./ollama serve'
open-webui:
image: ghcr.io/open-webui/open-webui:latest
container_name: open-webui
volumes:
- open-webui-volume:/app/backend/data
depends_on:
- ollama-intel-arc
ports:
- 4040:8080
environment:
- WEBUI_AUTH=False
- ENABLE_OPENAI_API=False
- ENABLE_OLLAMA_API=True
- ENABLE_IMAGE_GENERATION=True
- IMAGE_GENERATION_ENGINE=automatic1111
- IMAGE_GENERATION_MODEL=dreamshaper_8
- IMAGE_SIZE=400x400
- IMAGE_STEPS=8
- AUTOMATIC1111_BASE_URL=http://sdnext-ipex:7860/
- AUTOMATIC1111_CFG_SCALE=2
- AUTOMATIC1111_SAMPLER=DPM++ SDE
- AUTOMATIC1111_SCHEDULER=Karras
extra_hosts:
- host.docker.internal:host-gateway
restart: unless-stopped
volumes:
ollama-volume: {}
open-webui-volume: {}

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FROM intel/intel-extension-for-pytorch:2.7.10-xpu
# Set paths to use with sdnext
ENV SD_DATADIR="/mnt/data"
ENV SD_MODELSDIR="/mnt/models"
# Download the SDNext repository
RUN cat <<EOF > /bin/startup.sh
#!/bin/bash
git status || git clone https://github.com/vladmandic/sdnext.git .
python /app/launch.py "\$@"
EOF
# Make the startup script executable
RUN chmod 755 /bin/startup.sh
# Set the working directory to /app
WORKDIR /app
# Run SDNext with custom parameters
CMD [ "startup.sh", "-f", "--use-ipex", "--uv", "--listen", "--debug", "--api-log", "--log", "sdnext.log" ]

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FROM intel/intel-extension-for-pytorch:2.7.10-xpu
ENV USE_XETLA=OFF
ENV SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
ENV SYCL_CACHE_PERSISTENT=1
# Install required packages
RUN apt-get update && apt-get install -y ffmpeg
# Download the Whisper repository
RUN pip install --upgrade pip && pip install -U openai-whisper
# Set the working directory to /app
WORKDIR /app
CMD ["tail", "-f", "/dev/null"]