> For the complete documentation index, see [llms.txt](https://docs.clore.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clore.ai/guides/guides_v2-de/erste-schritte/docker-images.md).

# Docker-Images

Bereit zum Bereitstellen: Docker-Images für KI-Workloads auf CLORE.AI.

{% hint style="success" %}
Bereitstellen Sie diese Images direkt unter [CLORE.AI Marketplace](https://clore.ai/marketplace).
{% endhint %}

## Schnellbereitstellungsreferenz

### Beliebteste

| Aufgabe                             | Image                                | Ports     |
| ----------------------------------- | ------------------------------------ | --------- |
| Mit KI chatten                      | `ollama/ollama`                      | 22, 11434 |
| ChatGPT-ähnliche Benutzeroberfläche | `ghcr.io/open-webui/open-webui`      | 22, 8080  |
| Bildgenerierung                     | `universonic/stable-diffusion-webui` | 22, 7860  |
| Knotenbasierte Bildgenerierung      | `yanwk/comfyui-boot`                 | 22, 8188  |
| LLM-API-Server                      | `vllm/vllm-openai`                   | 22, 8000  |

***

## Sprachmodelle

### Ollama

**Universeller LLM-Runner – der einfachste Weg, jedes Modell auszuführen.**

```
Image: ollama/ollama
Ports: 22/tcp, 11434/http
Befehl: ollama serve
```

**Nach der Bereitstellung:**

```bash
# Per SSH in den Server einloggen
ssh -p <port> root@<proxy>

# Modell herunterladen und ausführen
ollama pull llama3.2
ollama run llama3.2
```

**Umgebungsvariablen:**

```
OLLAMA_HOST=0.0.0.0
OLLAMA_MODELS=/root/.ollama/models
```

***

### WebUI öffnen

**ChatGPT-ähnliche Oberfläche für Ollama.**

```
Image: ghcr.io/open-webui/open-webui:ollama
Ports: 22/tcp, 8080/http
```

Enthält Ollama integriert. Zugriff über HTTP-Port.

**Standalone (mit vorhandenem Ollama verbinden):**

```
Image: ghcr.io/open-webui/open-webui:main
Ports: 22/tcp, 8080/http
Umgebung: OLLAMA_BASE_URL=http://localhost:11434
```

***

### vLLM

**Leistungsstarkes LLM-Serving mit OpenAI-kompatibler API.**

```
Image: vllm/vllm-openai:latest
Ports: 22/tcp, 8000/http
Befehl: python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3.1-8B-Instruct --host 0.0.0.0
```

**Für größere Modelle (Multi-GPU):**

```bash
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Meta-Llama-3.1-70B-Instruct \
    --tensor-parallel-size 2 \
    --host 0.0.0.0
```

**Umgebungsvariablen:**

```
HUGGING_FACE_HUB_TOKEN=<dein-token>  # Für gesperrte Modelle
```

***

### Text Generation Inference (TGI)

**HuggingFace's Produktions-LLM-Server.**

```
Image: ghcr.io/huggingface/text-generation-inference:latest
Ports: 22/tcp, 8080/http
Befehl: --model-id meta-llama/Meta-Llama-3.1-8B-Instruct
```

**Umgebungsvariablen:**

```
HUGGING_FACE_HUB_TOKEN=<dein-token>
MAX_INPUT_LENGTH=4096
MAX_TOTAL_TOKENS=8192
```

***

## Bildgenerierung

### Stable Diffusion WebUI (AUTOMATIC1111)

**Beliebteste SD-Oberfläche mit Erweiterungen.**

```
Image: universonic/stable-diffusion-webui:latest
Ports: 22/tcp, 7860/http
```

**Für wenig VRAM (8GB oder weniger):**

```bash
./webui.sh --listen --medvram --xformers
```

**Für API-Zugriff:**

```bash
./webui.sh --listen --xformers --api
```

***

### ComfyUI

**Knotenbasierter Workflow für fortgeschrittene Benutzer.**

```
Image: yanwk/comfyui-boot:cu126-slim
Ports: 22/tcp, 8188/http
Umgebung: CLI_ARGS=--listen 0.0.0.0
```

**Alternative Images:**

```
# Mit häufigen Erweiterungen
Image: ai-dock/comfyui:latest

# Minimal
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
```

**Manueller Setup-Befehl:**

```bash
git clone https://github.com/comfyanonymous/ComfyUI && cd ComfyUI && pip install -r requirements.txt && python main.py --listen 0.0.0.0
```

***

### Fooocus

**Vereinfachte SD-Oberfläche, Midjourney-ähnlich.**

```
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
Ports: 22/tcp, 7865/http
Befehl: git clone https://github.com/lllyasviel/Fooocus && cd Fooocus && pip install -r requirements.txt && python launch.py --listen
```

***

### FLUX

**Neueste hochwertige Bildgenerierung.**

ComfyUI mit FLUX-Nodes verwenden:

```
Image: yanwk/comfyui-boot:cu126-slim
Ports: 22/tcp, 8188/http
```

Oder über Diffusers:

```
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
Ports: 22/tcp
```

```python
# Nach SSH
pip install diffusers transformers accelerate
python << 'EOF'
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell")
pipe.enable_model_cpu_offload()
image = pipe("A cat", num_inference_steps=4).images[0]
image.save("output.png")
EOF
```

***

## Videogenerierung

### Stable Video Diffusion

```
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
Ports: 22/tcp
```

```bash
pip install diffusers transformers accelerate
python << 'EOF'
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video
pipe = StableVideoDiffusionPipeline.from_pretrained(
    "stabilityai/stable-video-diffusion-img2vid-xt",
    variant="fp16"
)
pipe.to("cuda")
image = load_image("input.png")
frames = pipe(image, num_frames=25).frames[0]
export_to_video(frames, "output.mp4", fps=7)
EOF
```

***

### AnimateDiff

Verwendung mit ComfyUI:

```
Image: yanwk/comfyui-boot:cu126-slim
Ports: 22/tcp, 8188/http
```

Installiere AnimateDiff-Nodes über den ComfyUI Manager.

***

## Audio & Stimme

### Whisper (Transkription)

```
Image: onerahmet/openai-whisper-asr-webservice:latest
Ports: 22/tcp, 9000/http
Umgebung: ASR_MODEL=large-v3
```

**API-Nutzung:**

```bash
curl -X POST "http://localhost:9000/asr" \
    -F "audio_file=@audio.mp3" \
    -F "task=transcribe"
```

***

### Bark (Text-to-Speech)

```
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
Ports: 22/tcp
```

```bash
pip install bark
python << 'EOF'
from bark import SAMPLE_RATE, generate_audio, preload_models
from scipy.io.wavfile import write as write_wav
preload_models()
audio = generate_audio("Hello, this is a test.")
write_wav("output.wav", SAMPLE_RATE, audio)
EOF
```

***

### Stable Audio

```
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
Ports: 22/tcp
```

```bash
pip install stable-audio-tools
# Erfordert HF-Token für den Modellzugriff
```

***

## Visionsmodelle

### LLaVA

```
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
Ports: 22/tcp
```

```bash
pip install llava
python -m llava.serve.cli --model-path liuhaotian/llava-v1.6-34b
```

***

### Llama 3.2 Vision

Ollama verwenden:

```
Image: ollama/ollama
Ports: 22/tcp, 11434/http
```

```bash
ollama pull llama3.2-vision
ollama run llama3.2-vision "describe this image" --images photo.jpg
```

***

## Entwicklung & Training

### PyTorch-Basis

**Für benutzerdefinierte Setups und Training.**

```
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
Ports: 22/tcp
```

Enthält: CUDA 12.1, cuDNN 8, PyTorch 2.1

***

### Jupyter Lab

**Interaktive Notebooks für ML.**

```
Image: jupyter/pytorch-notebook:cuda12-pytorch-2.1
Ports: 22/tcp, 8888/http
```

Oder verwende die PyTorch-Basis mit Jupyter:

```bash
pip install jupyterlab
jupyter lab --ip=0.0.0.0 --allow-root --no-browser
```

***

### Kohya Training

**Für LoRA und Feinabstimmung von Modellen.**

```
Image: pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
Ports: 22/tcp
```

```bash
git clone https://github.com/kohya-ss/sd-scripts
cd sd-scripts
pip install -r requirements.txt
# Trainingsskripte verwenden
```

***

## Referenz für Basis-Images

### NVIDIA Offiziell

| Image                                    | CUDA | Einsatzgebiet         |
| ---------------------------------------- | ---- | --------------------- |
| `nvidia/cuda:12.1.0-devel-ubuntu22.04`   | 12.1 | CUDA-Entwicklung      |
| `nvidia/cuda:12.1.0-runtime-ubuntu22.04` | 12.1 | Nur CUDA-Laufzeit     |
| `nvidia/cuda:11.8.0-devel-ubuntu22.04`   | 11.8 | Legacy-Kompatibilität |

### PyTorch Offiziell

| Image                                          | PyTorch | CUDA |
| ---------------------------------------------- | ------- | ---- |
| `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel`  | 2.5     | 12.4 |
| `pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel`  | 2.0     | 11.7 |
| `pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel` | 1.13    | 11.6 |

### HuggingFace

| Image                                           | Zweck                  |
| ----------------------------------------------- | ---------------------- |
| `huggingface/transformers-pytorch-gpu`          | Transformers + PyTorch |
| `ghcr.io/huggingface/text-generation-inference` | TGI-Server             |

***

## Umgebungsvariablen

### Häufige Variablen

| Variable                 | Beschreibung                       | Beispiel       |
| ------------------------ | ---------------------------------- | -------------- |
| `HUGGING_FACE_HUB_TOKEN` | HF-API-Token für gesperrte Modelle | `hf_xxx`       |
| `CUDA_VISIBLE_DEVICES`   | GPU-Auswahl                        | `0,1`          |
| `TRANSFORMERS_CACHE`     | Modell-Cache-Verzeichnis           | `/root/.cache` |

### Ollama-Variablen

| Variable              | Beschreibung        | Standard           |
| --------------------- | ------------------- | ------------------ |
| `OLLAMA_HOST`         | Bind-Adresse        | `127.0.0.1`        |
| `OLLAMA_MODELS`       | Modelle-Verzeichnis | `~/.ollama/models` |
| `OLLAMA_NUM_PARALLEL` | Parallele Anfragen  | `1`                |

### vLLM-Variablen

| Variable                 | Beschreibung                  |
| ------------------------ | ----------------------------- |
| `VLLM_ATTENTION_BACKEND` | Attention-Implementierung     |
| `VLLM_USE_MODELSCOPE`    | ModelScope statt HF verwenden |

***

## Portreferenz

| Port  | Protokoll | Dienst                     |
| ----- | --------- | -------------------------- |
| 22    | TCP       | SSH                        |
| 7860  | HTTP      | Gradio (SD WebUI, Fooocus) |
| 7865  | HTTP      | Fooocus-Alternative        |
| 8000  | HTTP      | vLLM-API                   |
| 8080  | HTTP      | Open WebUI, TGI            |
| 8188  | HTTP      | ComfyUI                    |
| 8888  | HTTP      | Jupyter                    |
| 9000  | HTTP      | Whisper-API                |
| 11434 | TCP       | Ollama-API                 |

***

## Tipps

### Persistenter Speicher

Volumes einbinden, um Daten zwischen Neustarts zu behalten:

```bash
docker run -v /data/models:/root/.cache/huggingface ...
```

### GPU-Auswahl

Für Multi-GPU-Systeme:

```bash
docker run --gpus '"device=0,1"' ...
# oder
CUDA_VISIBLE_DEVICES=0,1
```

### Speicherverwaltung

Wenn der VRAM knapp wird:

1. Verwende kleinere Modelle
2. CPU-Offload aktivieren
3. Batch-Größe reduzieren
4. Verwende quantisierte Modelle (GGUF Q4)

## Nächste Schritte

* [GPU-Vergleich](/guides/guides_v2-de/erste-schritte/gpu-comparison.md) - Wähle die richtige GPU
* [Modellkompatibilität](/guides/guides_v2-de/erste-schritte/model-compatibility.md) - Was wo läuft
* [Schnellstart-Anleitung](/guides/guides_v2-de/quickstart.md) - Starte in 5 Minuten


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