Docker Images

Ready-to-deploy Docker images for AI workloads on Clore.ai

Ready-to-deploy Docker images for AI workloads on CLORE.AI.

Deploy these images directly at CLORE.AI Marketplace.

Quick Deploy Reference

Most Popular

Task	Image	Ports
Chat with AI	ollama/ollama	22, 11434
ChatGPT-like UI	ghcr.io/open-webui/open-webui	22, 8080
Image Generation	universonic/stable-diffusion-webui	22, 7860
Node-based Image Gen	yanwk/comfyui-boot	22, 8188
LLM API Server	vllm/vllm-openai	22, 8000

---

Language Models

Ollama

Universal LLM runner - easiest way to run any model.

Image: ollama/ollama
Ports: 22/tcp, 11434/http
Command: ollama serve

After deploy:

# SSH into server
ssh -p <port> root@<proxy>

# Pull and run a model
ollama pull llama3.2
ollama run llama3.2

Environment variables:

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

---

Open WebUI

ChatGPT-like interface for Ollama.

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

Includes Ollama built-in. Access via HTTP port.

Standalone (connect to existing Ollama):

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

---

vLLM

High-performance LLM serving with OpenAI-compatible API.

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

For larger models (multi-GPU):

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

Environment variables:

HUGGING_FACE_HUB_TOKEN=<your-token>  # For gated models

---

Text Generation Inference (TGI)

HuggingFace's production LLM server.

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

Environment variables:

HUGGING_FACE_HUB_TOKEN=<your-token>
MAX_INPUT_LENGTH=4096
MAX_TOTAL_TOKENS=8192

---

Image Generation

Stable Diffusion WebUI (AUTOMATIC1111)

Most popular SD interface with extensions.

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

For low VRAM (8GB or less):

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

For API access:

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

---

ComfyUI

Node-based workflow for advanced users.

Image: yanwk/comfyui-boot:cu130-slim
Ports: 22/tcp, 8188/http
Environment: CLI_ARGS=--listen 0.0.0.0

Alternative images:

# With common extensions
Image: yanwk/comfyui-boot:cu130-megapak-pt211

# Minimal
Image: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel

Manual setup command:

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

---

Fooocus

Simplified SD interface, Midjourney-like.

Image: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel
Ports: 22/tcp, 7865/http
Command: git clone https://github.com/lllyasviel/Fooocus && cd Fooocus && pip install -r requirements.txt && python launch.py --listen

---

FLUX

Latest high-quality image generation.

Use ComfyUI with FLUX nodes:

Image: yanwk/comfyui-boot:cu130-slim
Ports: 22/tcp, 8188/http

Or via Diffusers:

Image: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel
Ports: 22/tcp

# After 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

---

Video Generation

Stable Video Diffusion

Image: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel
Ports: 22/tcp

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

Use with ComfyUI:

Image: yanwk/comfyui-boot:cu130-slim
Ports: 22/tcp, 8188/http

Install AnimateDiff nodes via ComfyUI Manager.

---

Audio & Voice

Whisper (Transcription)

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

API usage:

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

---

Bark (Text-to-Speech)

Image: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel
Ports: 22/tcp

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:2.11.0-cuda12.8-cudnn9-devel
Ports: 22/tcp

pip install stable-audio-tools
# Requires HF token for model access

---

Vision Models

LLaVA

Image: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel
Ports: 22/tcp

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

---

Llama 3.2 Vision

Use Ollama:

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

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

---

Development & Training

PyTorch Base

For custom setups and training.

Image: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel
Ports: 22/tcp

Includes: CUDA 12.8, cuDNN 9, PyTorch 2.11

---

Jupyter Lab

Interactive notebooks for ML.

Image: quay.io/jupyter/pytorch-notebook:cuda12-pytorch-2.11.0
Ports: 22/tcp, 8888/http

Or use PyTorch base with Jupyter:

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

---

Kohya Training

For LoRA and model fine-tuning.

Image: pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel
Ports: 22/tcp

git clone https://github.com/kohya-ss/sd-scripts
cd sd-scripts
pip install -r requirements.txt
# Use training scripts

---

Base Images Reference

Match the CUDA build to the card. RTX 50-series (Blackwell) needs CUDA 12.8+ and PyTorch 2.7+; a CUDA 12.4 image fails on it with no kernel image is available for execution on the device. Full matrix: CUDA & PyTorch Compatibility.

NVIDIA Official

Image	CUDA	Use Case
nvidia/cuda:12.8.1-devel-ubuntu22.04	12.8	CUDA development, works Turing → Blackwell
nvidia/cuda:12.8.1-runtime-ubuntu22.04	12.8	CUDA runtime only
nvidia/cuda:13.0.3-devel-ubuntu24.04	13.0	Newest; Turing and up only
nvidia/cuda:11.8.0-devel-ubuntu22.04	11.8	Legacy cards (Pascal, old Volta stacks)

PyTorch Official

Image	PyTorch	CUDA	Cards
pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel	2.11	12.8	Default. Ampere, Ada, Blackwell
pytorch/pytorch:2.13.0-cuda13.2-cudnn9-devel	2.13	13.2	Turing and newer
pytorch/pytorch:2.10.0-cuda12.8-cudnn9-devel	2.10	12.8	Last release with Tesla V100 support
pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel	2.5	12.4	Pascal mining cards

HuggingFace

Image	Purpose
huggingface/transformers-pytorch-gpu	Transformers + PyTorch
ghcr.io/huggingface/text-generation-inference	TGI server

---

Environment Variables

Common Variables

Variable	Description	Example
HUGGING_FACE_HUB_TOKEN	HF API token for gated models	hf_xxx
CUDA_VISIBLE_DEVICES	GPU selection	0,1
TRANSFORMERS_CACHE	Model cache directory	/root/.cache

Ollama Variables

Variable	Description	Default
OLLAMA_HOST	Bind address	127.0.0.1
OLLAMA_MODELS	Models directory	~/.ollama/models
OLLAMA_NUM_PARALLEL	Parallel requests	1

vLLM Variables

Variable	Description
VLLM_ATTENTION_BACKEND	Attention implementation
VLLM_USE_MODELSCOPE	Use ModelScope instead of HF

---

Port Reference

Port	Protocol	Service
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

---

Tips

Persistent Storage

Mount volumes to keep data between restarts:

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

GPU Selection

For multi-GPU systems:

docker run --gpus '"device=0,1"' ...
# or
CUDA_VISIBLE_DEVICES=0,1

Memory Management

If running out of VRAM:

1. Use smaller models

2. Enable CPU offload

3. Reduce batch size

4. Use quantized models (GGUF Q4)

Next Steps

• GPU Comparison - Choose the right GPU

• Model Compatibility - What runs where

• Quickstart Guide - Get started in 5 minutes